**2. Epigenetic changes**

It has long been recognized that chromatin contains information beyond the primary DNA sequence. This information that is stored "on top of" the genetic information is highly dynamic and influences gene expression patterns and phenotypes without altering the nucleotide sequence while maintaining heritability to somatic daughter cells and in some cases even offspring *via* the germline. Multiple epigenetic mechanisms have been identified including ATP-dependent chromatin remodeling, the non-coding RNAs and different histone variants [6]. Here, we focus on the two most well studied aspects of epigenetic gene regulation, DNA methylation and histone modifications.

transcriptional repression [9] and established by DNA methyltransferases namely DNMT1, DNMT3A and DNMT3B. While DNMT1 is responsible for the maintenance of DNA methylation, DNMT3A and DNMT3B catalyze the *de novo* synthesis of 5-mC [10]. Conversely, 5-mC can be removed either by replication-dependent dilution or active DNA de-methylation by ten-eleven translocation (TET) proteins. TET proteins are Fe(II)/α-ketoglutarate-dependent dioxygenases that catalyze the oxidation of 5mC to 5-hydroxymethylcytosine (5hmC) and further to 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC), which eventually is removed via base excision repair (BER) to restore un-methylated cytosine (**Figure 1**) [11]. Aberrant DNA methylation is a wide spread phenomenon in all cancers [12] suggesting an important

**Figure 1. Schematic representation of DNA methylation and histone acetylation/methylation**. The basic structural unit of eukaryotic DNA is the nucleosome. A nucleosome includes 147 bp of DNA that is wrapped around a histone octamer consisting of two copies of each core histone protein H2A, H2B, H3 and H4. The N-terminal tails of the histones protrude from the core particle and are subject to posttranslational modifications. Lysine residues (K) of several histone tails can be either acetylated (Kac) by histone acetyltransferases (HAT) or mono-, di- or tri-methylated (Kme, Kme2 or Kme3) by histone methyltransferases (HMT). These modifications can be reversed by corresponding histone deacetylases (HDAc) or histone demethylases (HDM). Additionally, arginine residues (R) can be either mono- or di-methylated (Rme or Rme2), whereby di-methylation can be presented either symmetrically or asymmetrically. Cysteine residues (C) can be directly methylated by DNA methyltransferases (DNMT) resulting in 5-methylcytosine (5mC), which can be further processed as part of active DNA demethylation to 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC) by ten-eleven translocation (TET) proteins. DNA demethylation is then completed by thymine

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5

role in malignant transformation.

DNA glycosylase (TDG)-dependent base excision repair (BER).

#### **2.1. DNA methylation**

The most well studied form of epigenetic information is stored by direct covalent chemical modification of the DNA itself. Cytosine residues in CpG dinucleotides are methylated at the fifth position generating 5-methyl cytosine (5-mC) (**Figure 1**) without affecting Watson-Crick base pairing and sequence information [7]. This modification is consistently found in most eukaryotic model systems [8]. Generally speaking, DNA methylation is associated with

Epigenetics in Melanoma Development and Drug Resistance http://dx.doi.org/10.5772/intechopen.70983 5

After many years of research with unsatisfying treatment options and poor clinical outcomes, last decade has seen major advances in the therapy of metastatic melanoma driven by the revolutionizing discoveries of driver mutations and immune escape mechanisms that contribute to the aggressive nature of this disease. Drugs, developed to specifically exploit these mechanisms, administered either alone or in combination, have been shown to be clinically effective treatment strategies significantly increasing survival rates of patients [2–5]. Despite these recent ground-breaking advances in melanoma therapy, no currently available treatment options are curative in the majority of responding patients nor do all patients with *BRAFV600E* mutations respond to targeted therapies. Melanoma and targeted inhibition of oncogenic *BRAFV600E* became the poster child of an exciting initial therapy success followed by long-term resistance, which has also been experienced with other promising novel treatment strategies like immune checkpoint inhibitors targeting CTLA-4 and PD-1. The benefit of these new therapies is limited by the emergence of resistance, ultimately leading to tumor relapse. While the importance of genetic alterations for the development of disease and therapy resistance is unquestionable, it turns out that epigenetic remodeling is a fundamental feature of

This chapter will briefly introduce the concept of epigenetics focusing on epigenetic alterations, especially changes in histone and DNA modifications during melanoma development and the emergence of therapy resistance. Detailed investigations into these changes will greatly contribute to our understanding of the heterogeneous and adaptive nature of melanoma. A thorough perception of how epigenetic drivers are modulating the genetic landscape will be the foundation for the development of new treatment strategies beyond pathway and

It has long been recognized that chromatin contains information beyond the primary DNA sequence. This information that is stored "on top of" the genetic information is highly dynamic and influences gene expression patterns and phenotypes without altering the nucleotide sequence while maintaining heritability to somatic daughter cells and in some cases even offspring *via* the germline. Multiple epigenetic mechanisms have been identified including ATP-dependent chromatin remodeling, the non-coding RNAs and different histone variants [6]. Here, we focus on the two most well studied aspects of epigenetic gene regulation, DNA

The most well studied form of epigenetic information is stored by direct covalent chemical modification of the DNA itself. Cytosine residues in CpG dinucleotides are methylated at the fifth position generating 5-methyl cytosine (5-mC) (**Figure 1**) without affecting Watson-Crick base pairing and sequence information [7]. This modification is consistently found in most eukaryotic model systems [8]. Generally speaking, DNA methylation is associated with

tumor development and adaption to therapy.

4 Human Skin Cancers - Pathways, Mechanisms, Targets and Treatments

immune checkpoint inhibitors.

**2. Epigenetic changes**

**2.1. DNA methylation**

methylation and histone modifications.

**Figure 1. Schematic representation of DNA methylation and histone acetylation/methylation**. The basic structural unit of eukaryotic DNA is the nucleosome. A nucleosome includes 147 bp of DNA that is wrapped around a histone octamer consisting of two copies of each core histone protein H2A, H2B, H3 and H4. The N-terminal tails of the histones protrude from the core particle and are subject to posttranslational modifications. Lysine residues (K) of several histone tails can be either acetylated (Kac) by histone acetyltransferases (HAT) or mono-, di- or tri-methylated (Kme, Kme2 or Kme3) by histone methyltransferases (HMT). These modifications can be reversed by corresponding histone deacetylases (HDAc) or histone demethylases (HDM). Additionally, arginine residues (R) can be either mono- or di-methylated (Rme or Rme2), whereby di-methylation can be presented either symmetrically or asymmetrically. Cysteine residues (C) can be directly methylated by DNA methyltransferases (DNMT) resulting in 5-methylcytosine (5mC), which can be further processed as part of active DNA demethylation to 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC) by ten-eleven translocation (TET) proteins. DNA demethylation is then completed by thymine DNA glycosylase (TDG)-dependent base excision repair (BER).

transcriptional repression [9] and established by DNA methyltransferases namely DNMT1, DNMT3A and DNMT3B. While DNMT1 is responsible for the maintenance of DNA methylation, DNMT3A and DNMT3B catalyze the *de novo* synthesis of 5-mC [10]. Conversely, 5-mC can be removed either by replication-dependent dilution or active DNA de-methylation by ten-eleven translocation (TET) proteins. TET proteins are Fe(II)/α-ketoglutarate-dependent dioxygenases that catalyze the oxidation of 5mC to 5-hydroxymethylcytosine (5hmC) and further to 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC), which eventually is removed via base excision repair (BER) to restore un-methylated cytosine (**Figure 1**) [11]. Aberrant DNA methylation is a wide spread phenomenon in all cancers [12] suggesting an important role in malignant transformation.

#### **2.2. Histone modifications**

Regulatory epigenetic information is also embedded in the basic structure of chromatin and the nucleosome. The nucleosome core particle comprises 147 bp of DNA that is wrapped around an octamer of histone proteins consisting of two copies of H2A, H2B, H3 and H4 (**Figure 1**). Histones, especially the N-terminal tails, are subject to a multitude of posttranslational modifications including acetylation, methylation, phosphorylation, sumoylation, ubiquitylation or O-GlcNAcetylation with new modifications continuously identified [13]. The genome can be classified in transcriptionally active "open" euchromatin and transcriptionally inactive "closed" heterochromatin. Histone lysine acetylation affects this "open" and "closed" states by converting the charge of the affected residue at the histone tail, which decreases the histone/DNA interactions, increases DNA accessibility and therefore facilitates transcription and replication [14]. Alternatively, histone modifications can act as binding motives for transcription factors and other histone-modifying enzymes. For example, bromodomains specifically recognize acetylated lysine residues and are an important part of many chromatin-associated proteins [14]. The second very prominently studied histone modification is methylation of lysine or arginine residues. In contrast to acetylation, methylation can be present in different forms. Lysine residues can be mono-, di- or tri-methylated, while arginine residues can be mono-methylated or symmetrically or asymmetrically di-methylated, neither of which affects the charge of the amino acid side chain (**Figure 1**) [15]. Instead, methylated histone residues are recognized by a plethora of protein domains including plant homeodomain (PHD) zinc fingers, chromodomains, Tudor domains or WD40 repeats [16]. While histone acetylation is generally associated with active transcription, histone methylation has more diverse functions depending on the location of the modification. For example, H3K4me3 or H3K36me3 are usually found in active gene promoters whereas H3K9me3 or H3K27me3 are linked to transcriptional repression [13]. Histone modifications are generally reversible and dysregulation of either 'writers' (e.g. histone acetyltransferase or histone methyltransferases) or 'erasers' (e.g. histone deacetylase or histone demethylases) are attributed to the pathogenesis of human diseases [17].

**3.1. DNA methylation in melanoma development**

ylation with 7 hypermethylated [29].

pathway activity in melanoma [31].

One model used to investigate epigenetic alterations during melanoma development utilizes sequential cycles of anchorage blockade to transform mouse melanocytes resulting in cell lines that show different degrees of aggressiveness and *in vivo* tumor growth potential, to mimic different stages of melanomagenesis [21]. Investigating DNA and histone modifications in this model showed substantial epigenetic changes as global DNA methylation was decreased while multiple histone modifications including H4K16ac, H3K4me3, H3K27me3 and H3K9me3 were increased [21]. These findings are consistent with data from melanoma cell lines showing global hypo-methylation compared to melanocytes [22–24] with 11 out of 14 types of repetitive DNA elements being hypo-methylated [22]. Considering that repetitive DNA sequences constitute more than 45% of the human genome [25], changes of their methylation patterns affect the readout of global DNA methylation the most. Demethylation of these repetitive DNA elements has been reported to negatively influence chromatin organization, increase genetic instability or result in gene deregulation, all of which can promote tumorigenesis [26–28]. Microarray analysis of 27 common benign nevi and 22 primary invasive melanomas that covered 1505 CpG sites of regulatory regions of 807 cancer-related genes identified 26 CpG sites, associated with 22 genes that showed significant methylation differences. Of these 26 CpG sites, 19 showed significant hypometh-

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While it appears that global DNA methylation levels are decreased during melanocyte transformation, many gene-specific CpG islands are hypermethylated. Comparing 24 primary cutaneous melanomas and 5 benign nevi using the Infinium BeadChip technology covering 27,578 CpG loci in the promoter regions of 14,495 genes identified 106 hypermethylated and 44 hypomethylated CpG islands. Among the 106 hypermethylated genes, *MAPK13*, which encodes the p38 isoform, has been found to have tumor suppressive functions as retrovirusmediated overexpression of this gene displayed cytostatic effects and reduced melanoma growth *in vitro* [30]. Another interesting target gene that has been found to be regulated by DNA hypermethylation is the master regulator of pigmentation, MITF [31]. Lauss, et al. showed that hypermethylation of CpG islands belonging to *MITF* or MITF target genes correlated with decreased expression in metastatic melanoma tumors and melanoma cell lines. Melanoma cell lines that show intrinsically low *MITF* expression displayed CpG hypermethylation while cell lines that show high endogenous *MITF* levels were characterized by hypomethylation of these CpG islands. Accordingly, treatment with the DNA methyltransferase inhibitor 5′-Aza-2′-Deoxycytidine resulted in re-expression of MITF in MITF low cell lines. However, this re-expression was not sufficient to induce expression of the MITF target gene MLANA, suggesting that DNA methylation is involved but not sufficient to regulate MITF

Differences in DNA methylation between melanocytes and melanoma can also be attributed to mutant BRAF, the most frequently mutated gene in melanoma [32]. Knockdown of BRAF in BRAF-mutant melanoma cell lines resulted in profound alterations of the methylation landscape with changes in gene expression affecting proliferation and invasion. Furthermore knockdown of BRAF significantly decreased DNMT1 and EZH2 expression suggesting that

#### **3. Epigenetics in melanoma initiation and development**

High-throughput DNA sequencing enabled detailed investigations into the genetic makeup of cancer and revealed hundreds of genes that are frequently mutated in melanoma [18]. Among these, a set of driver mutations has been identified that allows melanocytes to proliferate excessively, to overcome senescence and to divide indefinitely, resulting in their transformation into melanoma [19]. Despite the undeniable importance of genetic events, detailed knowledge of the molecular mechanisms of tumor initiation is still absent. This is due to the fact that such events, like epigenetic changes, are challenging to observe because models that represent individual stages of melanomagenesis are required. Nevertheless, the importance of epigenetic dysregulation in melanoma development becomes increasingly apparent, which is emphasized by the high frequency of mutations found in epigenetic regulators [20].

#### **3.1. DNA methylation in melanoma development**

**2.2. Histone modifications**

6 Human Skin Cancers - Pathways, Mechanisms, Targets and Treatments

regulators [20].

Regulatory epigenetic information is also embedded in the basic structure of chromatin and the nucleosome. The nucleosome core particle comprises 147 bp of DNA that is wrapped around an octamer of histone proteins consisting of two copies of H2A, H2B, H3 and H4 (**Figure 1**). Histones, especially the N-terminal tails, are subject to a multitude of posttranslational modifications including acetylation, methylation, phosphorylation, sumoylation, ubiquitylation or O-GlcNAcetylation with new modifications continuously identified [13]. The genome can be classified in transcriptionally active "open" euchromatin and transcriptionally inactive "closed" heterochromatin. Histone lysine acetylation affects this "open" and "closed" states by converting the charge of the affected residue at the histone tail, which decreases the histone/DNA interactions, increases DNA accessibility and therefore facilitates transcription and replication [14]. Alternatively, histone modifications can act as binding motives for transcription factors and other histone-modifying enzymes. For example, bromodomains specifically recognize acetylated lysine residues and are an important part of many chromatin-associated proteins [14]. The second very prominently studied histone modification is methylation of lysine or arginine residues. In contrast to acetylation, methylation can be present in different forms. Lysine residues can be mono-, di- or tri-methylated, while arginine residues can be mono-methylated or symmetrically or asymmetrically di-methylated, neither of which affects the charge of the amino acid side chain (**Figure 1**) [15]. Instead, methylated histone residues are recognized by a plethora of protein domains including plant homeodomain (PHD) zinc fingers, chromodomains, Tudor domains or WD40 repeats [16]. While histone acetylation is generally associated with active transcription, histone methylation has more diverse functions depending on the location of the modification. For example, H3K4me3 or H3K36me3 are usually found in active gene promoters whereas H3K9me3 or H3K27me3 are linked to transcriptional repression [13]. Histone modifications are generally reversible and dysregulation of either 'writers' (e.g. histone acetyltransferase or histone methyltransferases) or 'erasers' (e.g. histone deacetylase or histone demethylases) are attributed to the pathogenesis of human diseases [17].

**3. Epigenetics in melanoma initiation and development**

High-throughput DNA sequencing enabled detailed investigations into the genetic makeup of cancer and revealed hundreds of genes that are frequently mutated in melanoma [18]. Among these, a set of driver mutations has been identified that allows melanocytes to proliferate excessively, to overcome senescence and to divide indefinitely, resulting in their transformation into melanoma [19]. Despite the undeniable importance of genetic events, detailed knowledge of the molecular mechanisms of tumor initiation is still absent. This is due to the fact that such events, like epigenetic changes, are challenging to observe because models that represent individual stages of melanomagenesis are required. Nevertheless, the importance of epigenetic dysregulation in melanoma development becomes increasingly apparent, which is emphasized by the high frequency of mutations found in epigenetic One model used to investigate epigenetic alterations during melanoma development utilizes sequential cycles of anchorage blockade to transform mouse melanocytes resulting in cell lines that show different degrees of aggressiveness and *in vivo* tumor growth potential, to mimic different stages of melanomagenesis [21]. Investigating DNA and histone modifications in this model showed substantial epigenetic changes as global DNA methylation was decreased while multiple histone modifications including H4K16ac, H3K4me3, H3K27me3 and H3K9me3 were increased [21]. These findings are consistent with data from melanoma cell lines showing global hypo-methylation compared to melanocytes [22–24] with 11 out of 14 types of repetitive DNA elements being hypo-methylated [22]. Considering that repetitive DNA sequences constitute more than 45% of the human genome [25], changes of their methylation patterns affect the readout of global DNA methylation the most. Demethylation of these repetitive DNA elements has been reported to negatively influence chromatin organization, increase genetic instability or result in gene deregulation, all of which can promote tumorigenesis [26–28]. Microarray analysis of 27 common benign nevi and 22 primary invasive melanomas that covered 1505 CpG sites of regulatory regions of 807 cancer-related genes identified 26 CpG sites, associated with 22 genes that showed significant methylation differences. Of these 26 CpG sites, 19 showed significant hypomethylation with 7 hypermethylated [29].

While it appears that global DNA methylation levels are decreased during melanocyte transformation, many gene-specific CpG islands are hypermethylated. Comparing 24 primary cutaneous melanomas and 5 benign nevi using the Infinium BeadChip technology covering 27,578 CpG loci in the promoter regions of 14,495 genes identified 106 hypermethylated and 44 hypomethylated CpG islands. Among the 106 hypermethylated genes, *MAPK13*, which encodes the p38 isoform, has been found to have tumor suppressive functions as retrovirusmediated overexpression of this gene displayed cytostatic effects and reduced melanoma growth *in vitro* [30]. Another interesting target gene that has been found to be regulated by DNA hypermethylation is the master regulator of pigmentation, MITF [31]. Lauss, et al. showed that hypermethylation of CpG islands belonging to *MITF* or MITF target genes correlated with decreased expression in metastatic melanoma tumors and melanoma cell lines. Melanoma cell lines that show intrinsically low *MITF* expression displayed CpG hypermethylation while cell lines that show high endogenous *MITF* levels were characterized by hypomethylation of these CpG islands. Accordingly, treatment with the DNA methyltransferase inhibitor 5′-Aza-2′-Deoxycytidine resulted in re-expression of MITF in MITF low cell lines. However, this re-expression was not sufficient to induce expression of the MITF target gene MLANA, suggesting that DNA methylation is involved but not sufficient to regulate MITF pathway activity in melanoma [31].

Differences in DNA methylation between melanocytes and melanoma can also be attributed to mutant BRAF, the most frequently mutated gene in melanoma [32]. Knockdown of BRAF in BRAF-mutant melanoma cell lines resulted in profound alterations of the methylation landscape with changes in gene expression affecting proliferation and invasion. Furthermore knockdown of BRAF significantly decreased DNMT1 and EZH2 expression suggesting that BRAFV600E-mediated pathway activation has a profound influence on the epigenetic landscape [33]. Analyzing BRAFV600E and BRAFWT samples from The Cancer Genome Atlas (TCGA) revealed that BRAFV600E correlates with global DNA hypomethylation. Primary melanoma samples showed a significantly decreased expression of *DNMT3A*, which is mainly responsible for *de novo* DNA methylation. Interestingly, *DNMT3A* expression was not found to be decreased in BRAF mutant BRAF wild type metastatic melanoma samples, suggesting that downregulation of this DNA methylatransferase is a transient event that might be important for melanoma initiation but not for metastatic spread and maintenance of global DNA hypomethylation [34]. Furthermore, Fang, et al. showed that BRAFV600E drives DNA hypermethylation and gene silencing of specific target genes in a v-maf avian musculoaponeurotic fibrosarcoma oncogene homolog G (MAFG) dependent manner in colorectal cancer and melanoma [35]. Specifically, MAFG is phosphorylated by the BRAF downstream kinase ERK1. This phosphorylation increases protein stability by reducing polyubiquitination and subsequent proteasomal degradation. MAFG then binds target gene promoters and recruits co-repressors including DNMT3B, ultimately resulting in DNA hypermethylation and gene silencing [35]. Unsupervised clustering of DNA methylation data from metastatic melanoma samples and TCGA melanoma samples identified three subgroups of melanoma tumors with differential methylation patterns [36]. These clusters did not correlate with either *BRAF-* or *NRAS*-mutation status nor primary tumor features like Breslow thickness or Clark's level, suggesting that factors beyond BRAF regulate DNA methylation in melanoma. The hypermethylated cluster (MS1) was associated with cell proliferation while the cluster with the lowest methylation levels (MS3) was associated with immunity, indicating a fundamental role of DNA methylation on melanoma and the microenvironment [36].

(SETDB1) is recurrently amplified within a region of chromosome 1 and shows a high expression in melanoma compared to nevi or normal skin [39]. Using the same zebrafish model as described above (*mitfa*-promoter crossed into a p53 loss-of-function background), Ceol, et al. identified that *SETDB1* amplification accelerates melanoma onset and increased invasivness. This was found to be independent of SETDB1 enzyme activity. Instead SETDB1 is part of a multimeric H3K9 methyltransferase complex including the H3K9me3 methyltransferase SUV39H1. Overexpression of SUV39H1 in the same zebrafish model also resulted in accelerated melanoma onset suggesting analogue functions of the entire H3K9 methyltransferase complex influencing melanoma devel-

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Another deregulated histone-modifying enzyme during melanoma development is the H3K27me3-specific histone methyltransferase enhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2). EZH2 and H3K27me3 have been found to be elevated in aggressive melanoma cell lines and metastatic tumor samples. The expression of tumor suppressors RUNX3 and E-cadherin was found to be suppressed by EZH2 dependent H3K27me3 [40]. Accordingly, EZH2 is a major factor for melanoma initiation and progression. Knockout of EZH2 in a genetically engineered *NRASQ61K* melanoma mouse model reduced the number of melanomas and prevented metastasis formation [41]. Mechanistically, this was mediated by EZH2 target genes including deoxycytidine kinase (*DCK*), adenosylmethionine decarboxylase 1 (*AMD1*) and WD repeat domain 19 (*WDR19*) which are suppressed in H3K27me3-dependent manner [41]. A possible explanation for EZH2 facilitating melanoma development could be by enabling senescence evasion. Knockdown of EZH2 in melanoma cells reestablished a senescence phenotype partially by reactivating p21/CDKN1A transcription, which was found to be independent of H3K27me3. Instead, at the transcriptional start site of p21/CDKN1A, H3K14ac was increased as a result of decreased recruitment of histone deacetylase 1 (HDAC1), which correlated with transcriptional activation [42]. This is in line with reports that EZH2, as part of the Polycomb repressive complex 2 (PRC2), is able to recruit histone deacetylases, which shows functional synergy with H3K27me3 in mediating target gene silencing [43]. Later, it was found that the non-canonical NF-kB pathway regulates EZH2 expression by direct binding of NF-kB2 to the EZH2 promoter. Inhibition of NF-kB2 induced a senescence-like phenotype, which was reversible upon EZH2 re-expression [44]. Not suprisingly, pharmacological inhibition of EZH2 has been shown to impair melanoma growth *in vitro* and *in vivo* [41, 44, 45]

opment, at least partially by abrogating oncogene-induced senescence [39].

and has emerged as a interesting target in multiple cancer types [46].

in active *TERT* transcription [51].

A more specific example how histone modifications promote melanoma progression is found in the case of the telomerase reverse transcriptase (TERT). Recently, activating *TERT* promoter mutations result in new transcription factor binding sites within the promoter, have been identified in up to 71% of all melanomas, which equals or even exceeds the frequency of *BRAF* and *NRAS* mutations, suggesting a key role for this genetic alteration in melanoma development [47]. Interestingly, mutations in the *TERT* promoter frequently co-occur with BRAF mutations [48–50]. In fact it was found that MAPK pathway inhibition decreased H3K4me3 and H3K9ac in the mutant *TERT* promoter region. This resulted in loss of RNA polymerase II (Pol II) recruitment and decreased *TERT* transcription. Mechanistically, ERK2 directly binds mutant *TERT* promoters and inhibits HDAC1 repressor complex recruitment, which results

#### **3.2. Histone modifications during melanoma development**

Remarkable insights into the importance of histone modifications for melanoma development have been revealed using a zebrafish model in which the human *BRAFV600E* gene is under the control of the *mitfa*-promoter crossed onto a p53 loss-of-function background. This model develops nevi that eventually progress into melanoma [37]. Like in most genetically engineered animal models, only a small fraction of genetically identical melanocytes transform into melanoma, highlighting the importance of molecular events beyond genetic alterations to drive melanoma development. To address this problem and investigate melanoma initiation in more detail, Kaufman, et al. developed a triple transgenic zebrafish model (p53/BRAF/ crestin:EGFP) in which a crestin/enhanced green fluorescent protein (crestin:EGFP) allows the visualization of neural crest stem/progenitor cells, the precursors of melanocytes [38]. Melanomas, which developed in these animals reestablish crestin:EGFP expression indicating that these cells reverse into a neural crest progenitor state. Knockout of sox10, a master regulator of neural crest identified and regulated by acetylation of histone 3 lysine 27 (H3K27Ac), significantly delayed melanoma onset. H3K27Ac in super enhancers at the *SOX10* locus was also found to be enriched in human melanoma cell lines indicating that epigenetic regulation of SOX10 expression is an important step of melanoma initiation [38].

Several histone-modifying enzymes have been shown to function aberrantly and contribute to melanoma progression. The H3K9me3-specific histone methyltransferase SET domain bifurcated 1 (SETDB1) is recurrently amplified within a region of chromosome 1 and shows a high expression in melanoma compared to nevi or normal skin [39]. Using the same zebrafish model as described above (*mitfa*-promoter crossed into a p53 loss-of-function background), Ceol, et al. identified that *SETDB1* amplification accelerates melanoma onset and increased invasivness. This was found to be independent of SETDB1 enzyme activity. Instead SETDB1 is part of a multimeric H3K9 methyltransferase complex including the H3K9me3 methyltransferase SUV39H1. Overexpression of SUV39H1 in the same zebrafish model also resulted in accelerated melanoma onset suggesting analogue functions of the entire H3K9 methyltransferase complex influencing melanoma development, at least partially by abrogating oncogene-induced senescence [39].

BRAFV600E-mediated pathway activation has a profound influence on the epigenetic landscape [33]. Analyzing BRAFV600E and BRAFWT samples from The Cancer Genome Atlas (TCGA) revealed that BRAFV600E correlates with global DNA hypomethylation. Primary melanoma samples showed a significantly decreased expression of *DNMT3A*, which is mainly responsible for *de novo* DNA methylation. Interestingly, *DNMT3A* expression was not found to be decreased in BRAF mutant BRAF wild type metastatic melanoma samples, suggesting that downregulation of this DNA methylatransferase is a transient event that might be important for melanoma initiation but not for metastatic spread and maintenance of global DNA hypomethylation [34]. Furthermore, Fang, et al. showed that BRAFV600E drives DNA hypermethylation and gene silencing of specific target genes in a v-maf avian musculoaponeurotic fibrosarcoma oncogene homolog G (MAFG) dependent manner in colorectal cancer and melanoma [35]. Specifically, MAFG is phosphorylated by the BRAF downstream kinase ERK1. This phosphorylation increases protein stability by reducing polyubiquitination and subsequent proteasomal degradation. MAFG then binds target gene promoters and recruits co-repressors including DNMT3B, ultimately resulting in DNA hypermethylation and gene silencing [35]. Unsupervised clustering of DNA methylation data from metastatic melanoma samples and TCGA melanoma samples identified three subgroups of melanoma tumors with differential methylation patterns [36]. These clusters did not correlate with either *BRAF-* or *NRAS*-mutation status nor primary tumor features like Breslow thickness or Clark's level, suggesting that factors beyond BRAF regulate DNA methylation in melanoma. The hypermethylated cluster (MS1) was associated with cell proliferation while the cluster with the lowest methylation levels (MS3) was associated with immunity, indicating a fundamental role

of DNA methylation on melanoma and the microenvironment [36].

of SOX10 expression is an important step of melanoma initiation [38].

Remarkable insights into the importance of histone modifications for melanoma development have been revealed using a zebrafish model in which the human *BRAFV600E* gene is under the control of the *mitfa*-promoter crossed onto a p53 loss-of-function background. This model develops nevi that eventually progress into melanoma [37]. Like in most genetically engineered animal models, only a small fraction of genetically identical melanocytes transform into melanoma, highlighting the importance of molecular events beyond genetic alterations to drive melanoma development. To address this problem and investigate melanoma initiation in more detail, Kaufman, et al. developed a triple transgenic zebrafish model (p53/BRAF/ crestin:EGFP) in which a crestin/enhanced green fluorescent protein (crestin:EGFP) allows the visualization of neural crest stem/progenitor cells, the precursors of melanocytes [38]. Melanomas, which developed in these animals reestablish crestin:EGFP expression indicating that these cells reverse into a neural crest progenitor state. Knockout of sox10, a master regulator of neural crest identified and regulated by acetylation of histone 3 lysine 27 (H3K27Ac), significantly delayed melanoma onset. H3K27Ac in super enhancers at the *SOX10* locus was also found to be enriched in human melanoma cell lines indicating that epigenetic regulation

Several histone-modifying enzymes have been shown to function aberrantly and contribute to melanoma progression. The H3K9me3-specific histone methyltransferase SET domain bifurcated 1

**3.2. Histone modifications during melanoma development**

8 Human Skin Cancers - Pathways, Mechanisms, Targets and Treatments

Another deregulated histone-modifying enzyme during melanoma development is the H3K27me3-specific histone methyltransferase enhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2). EZH2 and H3K27me3 have been found to be elevated in aggressive melanoma cell lines and metastatic tumor samples. The expression of tumor suppressors RUNX3 and E-cadherin was found to be suppressed by EZH2 dependent H3K27me3 [40]. Accordingly, EZH2 is a major factor for melanoma initiation and progression. Knockout of EZH2 in a genetically engineered *NRASQ61K* melanoma mouse model reduced the number of melanomas and prevented metastasis formation [41]. Mechanistically, this was mediated by EZH2 target genes including deoxycytidine kinase (*DCK*), adenosylmethionine decarboxylase 1 (*AMD1*) and WD repeat domain 19 (*WDR19*) which are suppressed in H3K27me3-dependent manner [41]. A possible explanation for EZH2 facilitating melanoma development could be by enabling senescence evasion. Knockdown of EZH2 in melanoma cells reestablished a senescence phenotype partially by reactivating p21/CDKN1A transcription, which was found to be independent of H3K27me3. Instead, at the transcriptional start site of p21/CDKN1A, H3K14ac was increased as a result of decreased recruitment of histone deacetylase 1 (HDAC1), which correlated with transcriptional activation [42]. This is in line with reports that EZH2, as part of the Polycomb repressive complex 2 (PRC2), is able to recruit histone deacetylases, which shows functional synergy with H3K27me3 in mediating target gene silencing [43]. Later, it was found that the non-canonical NF-kB pathway regulates EZH2 expression by direct binding of NF-kB2 to the EZH2 promoter. Inhibition of NF-kB2 induced a senescence-like phenotype, which was reversible upon EZH2 re-expression [44]. Not suprisingly, pharmacological inhibition of EZH2 has been shown to impair melanoma growth *in vitro* and *in vivo* [41, 44, 45] and has emerged as a interesting target in multiple cancer types [46].

A more specific example how histone modifications promote melanoma progression is found in the case of the telomerase reverse transcriptase (TERT). Recently, activating *TERT* promoter mutations result in new transcription factor binding sites within the promoter, have been identified in up to 71% of all melanomas, which equals or even exceeds the frequency of *BRAF* and *NRAS* mutations, suggesting a key role for this genetic alteration in melanoma development [47]. Interestingly, mutations in the *TERT* promoter frequently co-occur with BRAF mutations [48–50]. In fact it was found that MAPK pathway inhibition decreased H3K4me3 and H3K9ac in the mutant *TERT* promoter region. This resulted in loss of RNA polymerase II (Pol II) recruitment and decreased *TERT* transcription. Mechanistically, ERK2 directly binds mutant *TERT* promoters and inhibits HDAC1 repressor complex recruitment, which results in active *TERT* transcription [51].

A systematic overview of the epigenomic landscape of two phenotypically distinct melanocyte cell models that are characterized by low or high tumorigenicity showed distinct chromatin states associated with melanomagenesis. Specifically, chromatin state transitions characterized by loss of histone acetylation marks like H3K27Ac, H2BK5Ac and H4K5Ac and di-/trimethylation of H3K4 in regulatory domains associated with signaling pathways important for melanoma including phosphatidylinositol 3-kinase (PI3K), interferon (IFN) γ-, LKB1-, TRAILand platelet-derived growth factor (PDGF)-mediated signaling was observed, again emphasizing the link between epigenetic changes and melanoma development and progression [52].

TCGA melanoma cohort [57]. However, loss of PTEN expression is a more frequent event as reduced expression has been observed in approximately 50% of stage IIIB/C melanomas with a complete loss in 20–25% of all samples which correlated with decreased overall survival [58]. Accordingly, *PTEN* promoter methylation was found in 60.7% in the TCGA melanoma cohort and was an independent predictor for impaired patient survival [59]. Besides gene-specific DNA methylation, hypomethylation of repetitive DNA elements has also been associated with patient survival. Two studies report contradicting findings. Sigalotti, et al. analyzed cell lines isolated from 42 stage IIIC patients and reported that hypomethylation of 2 out of 3 CpG sites within Long Interspersed Nucleotide Element-1 (LINE-1) sequences correlated with improved prognosis and 5 year overall survival [59]. In contrast, Ecsedi, et al. extracted genomic DNA from primary melanoma and found that hypomethylation of 6 CpG sites associated within LINE-1 sequences in 46 primary melanomas correlated with decreased relapse-free survival of the corresponding patients and was also found to be associated with increased metastatic capacity [60]. A possible explanation for these contradictory results could be fundamental differences in the way how samples were analyzed. Sigalotti, et al. isolated and cultured melanoma cells from primary tumor tissue which might have

Epigenetics in Melanoma Development and Drug Resistance

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11

Besides changes in DNA methylation (5-mC), genome wide loss of the DNA demethylation intermediate 5-hydroxymethylcytosine (5-hmC) has recently been found to be a hallmark of melanoma [61]. Specifically, it has been shown that 5-hmC levels are progressively lost in melanoma compared to benign nevi, which was accompanied by decreased expression of TET family members and IDH2. Re-establishing 5-hmC by overexpression of TET2 reduced tumor growth and invasion suggesting an important function for 5-hmC in melanoma pathology. Accordingly, high levels of 5-hmC were found to negatively correlate with Breslow depth and predict better survival [61]. These findings were confirmed later on and suggest that 5-hmC analysis by immunohistochemistry could be a promising candidate as a

Presumed correlations between histone modifications and melanoma progression with prognosis have not been investigated compared to DNA methylation. This is in part because of technical challenges eminent by direct assessment of histone modifications [63]. Martinez, et al. performed immunohistochemical analyses of 10 benign nevi, 25 primary cutaneous melanomas without metastases, 19 primary cutaneous melanomas with metastases and 33 metastatic melanomas using an antibody specifically detecting H3K79 trimethylation and H3T80 phosphorylation (H3K79me3T80ph). They found a significant increase of H3K79me3T80ph in melanoma compared to nevi seemingly identifying a subset of primary melanomas with metastatic potential [64]. Another strategy to utilize histone modifications as biomarkers and prognostic factors that avoids the technical difficulties of direct assessment of histone modifications is to investigate the expression levels of histone-modifying enzymes. Along this line, it has been reported that the expression of the H3K27-specific histone methyltransferase EZH2 is increased during melanoma progression. However, only metastatic melanomas showed a significant increase compared to nevi [65]. Accordingly, analyses of EZH2 expression of TCGA melanoma samples showed a significantly shorter survival of patients with high EZH2 expression. Additionally, EZH2 high patients developed distant metastases faster, suggesting

affected the DNA methylome leading to the observed differences.

prognostic biomarker in melanoma [62].

Especially, loss of H3K4 methylation seems to be a key factor for melanoma growth and the highly problematic intratumor heterogeneity frequently observed in melanoma [53]. The histone 3 K4 demethylases jumonji/ARID1 (JARID1/KDM5B/PLU-1/RBP2-H1) defines a subpopulation of slow cycling melanoma cells, which is important for continuous growth of melanoma tumors. Interestingly, this subpopulation was found to be highly dynamic, as isolated KDM5B-positive and negative melanoma cells give rise to a heterogenous population consisting of both subpopulations [54] which highlights the variable nature of the epigenetic landscape in melanoma.

#### **3.3. Epigenetic modifications as biomarkers and prognostic factors in melanoma**

Because of the profound differences in DNA methylation patterns between melanocytic nevi and melanoma, several studies have investigated the suitability of DNA methylation as a predictive biomarker in melanoma. Unsupervised hierarchical clustering of 27 common benign nevi and 22 primary invasive melanomas resulted in separation of the two sample cohorts. Specifically, 22 genes were identified that significantly distinguished melanomas from nevi whereas 14 of these genes were validated in a separate set of 25 melanomas and 29 nevi [29] suggesting that analysis of differential DNA methylation patterns could be used as melanoma biomarkers. Later on Gao, et al. investigated the methylation differences of common nevi, dysplastic nevi, primary melanomas and metastatic melanomas and established a diagnostic algorithm based on promoter methylation patterns of *CLDN11, CDH11, PPP1R3C* which was able to distinguish dysplastic nevi from melanomas with a specificity of 89% and sensitivity of 67% [55]. DNA methylation changes, however, are not limited to melanoma development (nevi *versus* primary melanoma) but are also apparent in melanoma progression (primary melanoma *versus* metastatic melanoma). DNA methylation profiling using Illumina Infinium Human Methylation 450 K Beadchips of 14 normal nevi, 33 primary melanomas and 28 melanoma metastases identified gene promoters that were hypermethylated during melanoma development or melanoma progression [56]. Promoter methylation of several identified genes including *HOXA9, MEOX2, RBP1, TFAP2B, TWIST1* and *AKT3* were shown to be suitable biomarkers to distinguish between nevi, primary and metastatic melanoma. AKT3 and TFAP2B protein expression was also confirmed as biomarkers suitable for staining by immunohistochemistry. Furthermore, Wouters, et al. were able to correlate hypomethylation of *MEOX2, OLIG3* and *PON3* promoter hypomethylation with increased overall free survival [56]. Another major player in melanoma development that has been shown to be regulated by DNA methylation is Phosphatase and Tensin Homolog (*PTEN*). *PTEN* inactivating mutations or deletions have been found in 12% of the TCGA melanoma cohort [57]. However, loss of PTEN expression is a more frequent event as reduced expression has been observed in approximately 50% of stage IIIB/C melanomas with a complete loss in 20–25% of all samples which correlated with decreased overall survival [58]. Accordingly, *PTEN* promoter methylation was found in 60.7% in the TCGA melanoma cohort and was an independent predictor for impaired patient survival [59]. Besides gene-specific DNA methylation, hypomethylation of repetitive DNA elements has also been associated with patient survival. Two studies report contradicting findings. Sigalotti, et al. analyzed cell lines isolated from 42 stage IIIC patients and reported that hypomethylation of 2 out of 3 CpG sites within Long Interspersed Nucleotide Element-1 (LINE-1) sequences correlated with improved prognosis and 5 year overall survival [59]. In contrast, Ecsedi, et al. extracted genomic DNA from primary melanoma and found that hypomethylation of 6 CpG sites associated within LINE-1 sequences in 46 primary melanomas correlated with decreased relapse-free survival of the corresponding patients and was also found to be associated with increased metastatic capacity [60]. A possible explanation for these contradictory results could be fundamental differences in the way how samples were analyzed. Sigalotti, et al. isolated and cultured melanoma cells from primary tumor tissue which might have affected the DNA methylome leading to the observed differences.

A systematic overview of the epigenomic landscape of two phenotypically distinct melanocyte cell models that are characterized by low or high tumorigenicity showed distinct chromatin states associated with melanomagenesis. Specifically, chromatin state transitions characterized by loss of histone acetylation marks like H3K27Ac, H2BK5Ac and H4K5Ac and di-/trimethylation of H3K4 in regulatory domains associated with signaling pathways important for melanoma including phosphatidylinositol 3-kinase (PI3K), interferon (IFN) γ-, LKB1-, TRAILand platelet-derived growth factor (PDGF)-mediated signaling was observed, again emphasizing the link between epigenetic changes and melanoma development and progression [52]. Especially, loss of H3K4 methylation seems to be a key factor for melanoma growth and the highly problematic intratumor heterogeneity frequently observed in melanoma [53]. The histone 3 K4 demethylases jumonji/ARID1 (JARID1/KDM5B/PLU-1/RBP2-H1) defines a subpopulation of slow cycling melanoma cells, which is important for continuous growth of melanoma tumors. Interestingly, this subpopulation was found to be highly dynamic, as isolated KDM5B-positive and negative melanoma cells give rise to a heterogenous population consisting of both subpopulations [54] which highlights the variable nature of the epigenetic

10 Human Skin Cancers - Pathways, Mechanisms, Targets and Treatments

**3.3. Epigenetic modifications as biomarkers and prognostic factors in melanoma**

Because of the profound differences in DNA methylation patterns between melanocytic nevi and melanoma, several studies have investigated the suitability of DNA methylation as a predictive biomarker in melanoma. Unsupervised hierarchical clustering of 27 common benign nevi and 22 primary invasive melanomas resulted in separation of the two sample cohorts. Specifically, 22 genes were identified that significantly distinguished melanomas from nevi whereas 14 of these genes were validated in a separate set of 25 melanomas and 29 nevi [29] suggesting that analysis of differential DNA methylation patterns could be used as melanoma biomarkers. Later on Gao, et al. investigated the methylation differences of common nevi, dysplastic nevi, primary melanomas and metastatic melanomas and established a diagnostic algorithm based on promoter methylation patterns of *CLDN11, CDH11, PPP1R3C* which was able to distinguish dysplastic nevi from melanomas with a specificity of 89% and sensitivity of 67% [55]. DNA methylation changes, however, are not limited to melanoma development (nevi *versus* primary melanoma) but are also apparent in melanoma progression (primary melanoma *versus* metastatic melanoma). DNA methylation profiling using Illumina Infinium Human Methylation 450 K Beadchips of 14 normal nevi, 33 primary melanomas and 28 melanoma metastases identified gene promoters that were hypermethylated during melanoma development or melanoma progression [56]. Promoter methylation of several identified genes including *HOXA9, MEOX2, RBP1, TFAP2B, TWIST1* and *AKT3* were shown to be suitable biomarkers to distinguish between nevi, primary and metastatic melanoma. AKT3 and TFAP2B protein expression was also confirmed as biomarkers suitable for staining by immunohistochemistry. Furthermore, Wouters, et al. were able to correlate hypomethylation of *MEOX2, OLIG3* and *PON3* promoter hypomethylation with increased overall free survival [56]. Another major player in melanoma development that has been shown to be regulated by DNA methylation is Phosphatase and Tensin Homolog (*PTEN*). *PTEN* inactivating mutations or deletions have been found in 12% of the

landscape in melanoma.

Besides changes in DNA methylation (5-mC), genome wide loss of the DNA demethylation intermediate 5-hydroxymethylcytosine (5-hmC) has recently been found to be a hallmark of melanoma [61]. Specifically, it has been shown that 5-hmC levels are progressively lost in melanoma compared to benign nevi, which was accompanied by decreased expression of TET family members and IDH2. Re-establishing 5-hmC by overexpression of TET2 reduced tumor growth and invasion suggesting an important function for 5-hmC in melanoma pathology. Accordingly, high levels of 5-hmC were found to negatively correlate with Breslow depth and predict better survival [61]. These findings were confirmed later on and suggest that 5-hmC analysis by immunohistochemistry could be a promising candidate as a prognostic biomarker in melanoma [62].

Presumed correlations between histone modifications and melanoma progression with prognosis have not been investigated compared to DNA methylation. This is in part because of technical challenges eminent by direct assessment of histone modifications [63]. Martinez, et al. performed immunohistochemical analyses of 10 benign nevi, 25 primary cutaneous melanomas without metastases, 19 primary cutaneous melanomas with metastases and 33 metastatic melanomas using an antibody specifically detecting H3K79 trimethylation and H3T80 phosphorylation (H3K79me3T80ph). They found a significant increase of H3K79me3T80ph in melanoma compared to nevi seemingly identifying a subset of primary melanomas with metastatic potential [64]. Another strategy to utilize histone modifications as biomarkers and prognostic factors that avoids the technical difficulties of direct assessment of histone modifications is to investigate the expression levels of histone-modifying enzymes. Along this line, it has been reported that the expression of the H3K27-specific histone methyltransferase EZH2 is increased during melanoma progression. However, only metastatic melanomas showed a significant increase compared to nevi [65]. Accordingly, analyses of EZH2 expression of TCGA melanoma samples showed a significantly shorter survival of patients with high EZH2 expression. Additionally, EZH2 high patients developed distant metastases faster, suggesting a role for EZH2 in metastasis formation [41]. In contrast to EZH2, KDM5B has been found to be significantly downregulated during melanoma development. About 70% of the investigated nevi samples showed a KDM5B expression compared to 10 and 30% in primary and metastatic melanoma samples, respectively [66].

[83] or can be reversibly lost in response to inflammation [84]. Another study found a correlation between a mesenchymal transcription signature, including WNT5A and ROR2, with resistance to anti-PD-1 therapy in metastatic melanoma [85] suggesting the involvement of

Epigenetics in Melanoma Development and Drug Resistance

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13

In the following paragraphs, the current knowledge about epigenetic mechanisms contribut-

One of the most clinically relevant observations that point towards non-genetically regulated drug resistance is the concept of drug holidays, which describes the phenomenon of intermittent treatment schedules or treatment interruption. This delays the emergence of resistance. One of the first reports describing the benefit of treatment interruption was a case study of a patient diagnosed with an adenocarcinoma of the lungs. After initial chemotherapy, the patient enrolled in a phase I study of the orally active epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor gefitinib. After 18 month of drug response, the disease eventually progressed and was treated with a different combination of chemotherapy. One year after discontinuation of the initial treatment, gefitinib re-treatment resulted in a significant response [86]. Similar observations were further reported for patients treated with BRAF or BRAF/MEK inhibitors in which re-treatment with BRAF inhibitors resulted in a significant response after disease progression during an earlier treatment with BRAF or BRAF/MEK inhibitors [87]. A multi-institutional retrospective study later on found that 43% of patients that received re-treatment with BRAF inhibitors after disease progression and treatment interruption showed a clinically significant response [88]. Studies using vemurafenib-naive, primary human-patient-derived melanoma xenograft mouse models showed that vemurafenib resistance could be delayed by intermittent dosing schedules compared to

The reversibility of drug resistance observed in clinical settings matches well with findings of slow cycling subpopulations that have been found to allow for reversible drug tolerance *in vitro.* One of the first reports of such a drug-tolerant subpopulation showed that a very small fraction of cancer cells including melanoma survives treatment with drug concentrations 100 fold higher than the IC50 [74]. These surviving cells were found to be mainly quiescent and in G1 arrest, they eventually continued growth in the presence of the drug. Importantly, drug withdrawal re-sensitized these drug-tolerant cells and re-established the same cellular heterogeneity as found in the initial sensitive population. Mechanistically, the surviving drug-tolerant cells exerted an altered chromatin state with increased expression levels of the histone demethylase KDM5A (JARID1A/RBP2) and concomitantly reduced levels of H3K4me2/3. RNAi-mediated knockdown of KDM5A confirmed that this histone demethylase is important for the establishment of the reversible drug-tolerant state [74]. This observation of an epigenetically regulated mainly G1 arrested state surviving exposure to high drug concentrations is similar to the previously mentioned slow cycling KDM5Bhigh subpopulation that is important for continuous melanoma growth [54]. KDM5Bhigh cells have been found to be enriched upon drug treatment and resemble a slow cycling drug-tolerant state in melanoma

epithelial-mesenchymal transition in immunotherapy failure.

ing to drug resistance in melanoma is summarized.

**4.2. Epigenetic alterations and targeted therapy**

continuous treatment [89].

To our knowledge and despite the wealth of epigenetic changes that differentiate melanocytes and melanoma, no epigenetic biomarkers are used in the clinic to date.


*3.1. Finite Element Modeling of Nonlinear Behavior of Beam-Column Joint* 3.1.1. Finite Element Method

The finite element method (FEM) is the most widely used in numerical simulation of structures [35]. Finite element models have the potential to solve a wide range of complex problems from elastic linear models for linear elements to highly plastic models for nonlinear and solid elements. FEM is one of the leading methods to simulate all types of structures (timber, steel, concrete, masonry) [36].

### 3.1.2. Abaqus Software

To perform numerical simulation of beam-column joint using RPC a FEM-based software ABAQUS/CAE was selected, which is general-purpose analysis software having the capability of solving the elastic and inelastic problems of the static and dynamic response of components [37].

ABAQUS/CAE 6.14-1 VERSION was used for modeling and analysis of beam-column joint using RPC.

### 3.1.3. Concrete Damage Plasticity Model

The concrete damage plasticity (CDP) model was selected as it has the capability and potential for modeling reinforced concrete and other quasi-brittle material for different types of structure. CDP model can define the nonlinear behavior of the RPC beam-column joint. Additionally, it takes into account the isotropic damage elasticity concepts with isotropic tensile and compressive plasticity. It also considers the degradation of elastic stiffness produced by plastic straining both in compression and tension [38]. The CDP model can show damage characteristics of a material. The main failure mechanism that this model assumes is the tensile cracking and the compressive crushing [39].

Different parameters required in the CDP model were studied and selected based on available literature both for conventional as well RPC specimens. The dilation angle for the model was taken as 36◦ . It is the angle obtained due to a change in volumetric strain produced due to plastic shearing. It depends on the angle of internal friction. Dilation angle controls the amount of plastic volumetric strain produced due to plastic shearing. Normally dilation angle is taken between 30◦ and 40◦ for concrete to avoid large variation between experimental work and numerical modeling. For the seismic design of reinforced concrete, the value of dilation angle is normally between 35◦ to 38◦ [40]. Moreover, eccentricity is the deviation from the center. The default value for eccentricity was taken, i.e., 0.1. If the value

is increased by 0.1 the curvature of flow potential is increased. If the value is decreased from the default value, the convergence problem may occur if confinement pressure is not high enough. Furthermore, the ratio of biaxial loading (*fb* ) to uniaxial loading (*fc*0) is normally taken as 1 or greater than 1. In this case default value was taken i.e., *fb*/*fc*o = 1.16. K is the shape factor and default value for K = 0.667 [41]. The viscosity parameter shows the amount of flow potential in a material. A lower viscosity parameter value is better as higher values result in a high force of reaction. Therefore, the viscosity parameter, in this case, was taken as 0.001 [42]. *εc*1 is the strain at peak stress *εcu* is the ultimate strainat which concrete fails Equations (2) and (3) are only pertinent to concrete having a cylindrical compressive strength of 50 MPa and cube compressive strength of 60 MPa at the most. On the basis of a list of the experimental results, Kmiecik and Kamiński [44] proposed the quite accurate approximating Equations (4) and (5): 1 = 0.0014 ሾ2 െ expሺെ0.024 ሻ െ expሺ0.140 ሻ (4) = 0.004 െ 0.0011ሾ1 െ expሺെ0.0215 ሻሿ (5)

The compressive behavior of concrete was calculated by using the relations of Euro-

Other values showing the position of characteristics points are strain *εc*1 at average

= 22ሺ0.1ሻ.ଷ (1)

1 = 0.7ሺሻ0.31 (2)

= 0.35% (3)

### *3.2. Compressive and Tensile Behavior Determination by Using Eurocode* Knowing the values of the output in Equations (4) and (5) one can determine the

*Crystals* **2021**, *11*, x FOR PEER REVIEW 10 of 22

3.2.1. Compressive Behavior

where:

where:

code [43] given in Equation (1).

*fcm* (MPa) is the compressive strength *Ecm* (GPa) is the modulus of elasticity

compressive strength and ultimate strain *εcu* at 0.

Compressive behavior and tensile behavior of both normal concrete and RPC were determined by using EN 1992 Eurocode 2: Design of concrete structures part 1–1 [43]. It describes different principles and requirements for the safety, serviceability, and durability of concrete structures with specific provisions of buildings. Eurocode 2 applies to the design of civil engineering works such as buildings, roads, bridges, etc. It is applied to plain, reinforced, and prestressed concretes. It complies with the specifications and requirements given in EN 1992-1-1 about safety, serviceability of the structures, the basis of their design, and verification of structures given in EN 1990; basis of structural design [38]. Compressive and tensile stress-strain curves are shown in Figures 11 and 12, respectively. The limitation of the Eurocode 2 for concrete structures is that it is concerned only with the requirements for resistance, safety, serviceability, durability, and fire resistance of the structures. Moreover, it does not consider the other requirements like thermal or sound insulation, etc. [38]. points at which the graphs intersect. Compressive stress values can be determined at any point using these relations [43]. According to Eurocode EN 1992-1-1 = ሺ െ ଶሻ/ሺ1 ሺെ2ሻ (6) where: = 1.05 ∗ ൬ 1 ൰ (7) and: = (8) *Crystals* **2021**, *11*, x FOR PEER REVIEW 11 of 22

**Figure 11. Figure 11.** Stress-strain diagra Stress-strain diagram for analysis of concrete using Eurocode 2 [ m for analysis of concrete using Eurocode 2 [43]. 43].

**Figure 12.** Tensile stress-strain curve of concrete from Eurocode [43]. **Figure 12.** Tensile stress-strain curve of concrete from Eurocode [43].

For the determination of the complete stress-strain curve for compressive behavior and the tensile behavior of normal concrete and RPC, Eurocode has been used which is capable of determining the actual response of structures closer to the experimental setup.

Simple modifications were incorporated into the Equations (1), (3), and (12). These equations were utilized in assigning material properties during numerical modeling in ABAQUS to obtain more realistic results of RPC concrete. Modified equations are shown

After the completion of assembly, a step was formed. In steps, a time period was provided for which the load is applied to the assembly. The load was then applied to the designated location according to the magnitude of the sample and boundary conditions were applied according to experimental work in which two specimens (CC\_S1 and RPC\_S1) have hinge boundary condition, i.e., (U1 = U2 = UR3 = 0) while the other two specimens have fixed boundary condition (U1 = U2 = U3 = UR1 = UR2 = UR3 = 0) at column end while roller support (U1 = UR2 = UR3 = 0) at the beam end in all specimens. "U" refers

= 22ሺ0.13ሻ.ଷ (13)

 = 0.40% (14) = 0.40 ∗ .ହ (15)

for the determination of stress-strain curves of RPC.

in Equations (13)–(15).

*3.3. Steps and Boundary Conditions*

3.2.1. Compressive Behavior

The compressive behavior of concrete was calculated by using the relations of Eurocode [43] given in Equation (1).

$$\text{Ecm} = \text{22}(0.1fcm)^{0.3} \tag{1}$$

where:

*fcm* (MPa) is the compressive strength

*Ecm* (GPa) is the modulus of elasticity

Other values showing the position of characteristics points are strain *εc*1 at average compressive strength and ultimate strain *εcu* at 0.

$$
\epsilon \varepsilon 1 = 0.7(fcm)^{0.31} \tag{2}
$$

$$
\varepsilon\omega = 0.35\% \tag{3}
$$

where:

*εc*1 is the strain at peak stress

*εcu* is the ultimate strain at which concrete fails

Equations (2) and (3) are only pertinent to concrete having a cylindrical compressive strength of 50 MPa and cube compressive strength of 60 MPa at the most. On the basis of a list of the experimental results, Kmiecik and Kami´nski [44] proposed the quite accurate approximating Equations (4) and (5):

$$
\epsilon \epsilon 1 = 0.0014 \left[ 2 - \exp(-0.024 \, fcm) - \exp(0.140 \, fcm) \right] \tag{4}
$$

$$
\varepsilon u = 0.004 - 0.0011[1 - \exp(-0.0215 \, fcm)]\tag{5}
$$

Knowing the values of the output in Equations (4) and (5) one can determine the points at which the graphs intersect. Compressive stress values can be determined at any point using these relations [43].

According to Eurocode EN 1992-1-1

$$\sigma c = f c m (k \eta - \eta^2) / (1 + (k - 2)\eta \tag{6}$$

where:

$$k = 1.05 \ast Ecn \left(\frac{\varepsilon c1}{fcm}\right) \tag{7}$$

and:

$$
\eta = \frac{\varepsilon c}{\varepsilon cl} \tag{8}
$$

3.2.2. Tensile Behaviors

The tensile behavior of concrete was calculated by using the Equations (9)–(12). If *εt* ≤ *εcr*

*σt* = *Ec* ∗ *εt* (9)

and if *εt* > *εcr*

$$
\sigma t = f \text{cm}(\frac{\varepsilon \sigma}{\varepsilon t})^{0.4} \tag{10}
$$

$$ft = 0.33 \* ft^{0.5} \tag{11}$$

$$ftr = 0.30fck^{2/3} \tag{12}$$

For the determination of the complete stress-strain curve for compressive behavior and the tensile behavior of normal concrete and RPC, Eurocode has been used which is capable of determining the actual response of structures closer to the experimental setup. As RPC is a composite material and there is no official code for RPC developed yet, therefore small modifications based on literature have been made in normal concrete formulas for the determination of stress-strain curves of RPC.

Simple modifications were incorporated into the Equations (1), (3), and (12). These equations were utilized in assigning material properties during numerical modeling in ABAQUS to obtain more realistic results of RPC concrete. Modified equations are shown in Equations (13)–(15).

$$\text{Ecm} = \text{22}(0.13fcm)^{0.3} \tag{13}$$

$$
\varepsilon\upsilon = 0.40\% \tag{14}
$$

$$ft = 0.40 \* fc^{0.5} \tag{15}$$

### *3.3. Steps and Boundary Conditions*

After the completion of assembly, a step was formed. In steps, a time period was provided for which the load is applied to the assembly. The load was then applied to the designated location according to the magnitude of the sample and boundary conditions were applied according to experimental work in which two specimens (CC\_S1 and RPC\_S1) have hinge boundary condition, i.e., (U1 = U2 = UR3 = 0) while the other two specimens have fixed boundary condition (U1 = U2 = U3 = UR1 = UR2 = UR3 = 0) at column end while roller support (U1 = UR2 = UR3 = 0) at the beam end in all specimens. "U" refers to translatory motion while "UR" refers to rotation of the support. Both the boundary conditions for the column were studied and their effect on the strength and load values were observed. *Crystals* **2021**, *11*, x FOR PEER REVIEW 12 of 22 to translatory motion while "UR" refers to rotation of the support. Both the boundary conditions for the column were studied and their effect on the strength and load values were observed. *3.4. Meshing*

### *3.4. Meshing* Meshing is the process of dividing the whole finite element model into a smaller

Meshing is the process of dividing the whole finite element model into a smaller number of chunks by the formation of different nodes at different points. Meshing is an important process as it allows us to apply load and find displacement or any other desired result at any point in the model. The greater the size of the mesh, the smaller will be the number of iterations taken to analyze the whole model and vice versa. In a greater size mesh, a lesser number of nodes are formed, hence the number of iterations and time of analysis is reduced. In our case, the size of the mesh taken was 25 mm, 40 mm, and 50 mm. Independent types of meshing for concrete and steel are selected in Figures 13–15 [42]. number of chunks by the formation of different nodes at different points. Meshing is an important process as it allows us to apply load and find displacement or any other desired result at any point in the model. The greater the size of the mesh, the smaller will be the number of iterations taken to analyze the whole model and vice versa. In a greater size mesh, a lesser number of nodes are formed, hence the number of iterations and time of analysis is reduced. In our case, the size of the mesh taken was 25 mm, 40 mm, and 50 mm. Independent types of meshing for concrete and steel are selected in Figures 13–15 [42].

**Figure 13.** Meshing of 25 mm size for concrete. **Figure 13.** Meshing of 25 mm size for concrete.

**Figure 14.** Meshing of steel embedded in concrete.

**Figure 14. Figure 14.**  Meshing of steel embedded in concrete. Meshing of steel embedded in concrete.

**Figure 13.** Meshing of 25 mm size for concrete.

**Figure 15.** Typical view of Abaqus model of reinforcement. **Figure 15.** Typical view of Abaqus model of reinforcement.

### *3.5. Load 3.5. Load*

were observed.

*3.4. Meshing*

[42].

A series of analysis was performed on conventional concrete controlled specimens and RPC specimens to simulate and predict the actual response of linear and nonlinear behavior on beam-column joint and to show the behavior of RPC in improving the shear strength deformation against different structural loading. A monotonic load of 0 to 20 kN was applied at the top of the exterior joint for all specimens till the specimens reached the ultimate value. After the application of load, step was created for static analysis. Time period and increment values were given to all specimens**.** Figures 16 and 17 show the analysis of RPC samples with fixed and hinge boundary conditions, respectively. As seen in Figure 16, a small amount of buckling was observed when the column boundary condition was kept fixed, whereas no buckling was observed in the case of hinge column conditions as seen in Figure 17. A series of analysis was performed on conventional concrete controlled specimens and RPC specimens to simulate and predict the actual response of linear and nonlinear behavior on beam-column joint and to show the behavior of RPC in improving the shear strength deformation against different structural loading. A monotonic load of 0 to 20 kN was applied at the top of the exterior joint for all specimens till the specimens reached the ultimate value. After the application of load, step was created for static analysis. Time period and increment values were given to all specimens. Figures 16 and 17 show the analysis of RPC samples with fixed and hinge boundary conditions, respectively. As seen in Figure 16, a small amount of buckling was observed when the column boundary condition was kept fixed, whereas no buckling was observed in the case of hinge column conditions as seen in Figure 17.

to translatory motion while "UR" refers to rotation of the support. Both the boundary conditions for the column were studied and their effect on the strength and load values

Meshing is the process of dividing the whole finite element model into a smaller number of chunks by the formation of different nodes at different points. Meshing is an important process as it allows us to apply load and find displacement or any other desired result at any point in the model. The greater the size of the mesh, the smaller will be the number of iterations taken to analyze the whole model and vice versa. In a greater size mesh, a lesser number of nodes are formed, hence the number of iterations and time of analysis is reduced. In our case, the size of the mesh taken was 25 mm, 40 mm, and 50 mm. Independent types of meshing for concrete and steel are selected in Figures 13–15

conditions as seen in Figure 17.

**Figure 15.** Typical view of Abaqus model of reinforcement.

A series of analysis was performed on conventional concrete controlled specimens and RPC specimens to simulate and predict the actual response of linear and nonlinear behavior on beam-column joint and to show the behavior of RPC in improving the shear strength deformation against different structural loading. A monotonic load of 0 to 20 kN was applied at the top of the exterior joint for all specimens till the specimens reached the ultimate value. After the application of load, step was created for static analysis. Time period and increment values were given to all specimens**.** Figures 16 and 17 show the analysis of RPC samples with fixed and hinge boundary conditions, respectively. As seen in Figure 16, a small amount of buckling was observed when the column boundary condition was kept fixed, whereas no buckling was observed in the case of hinge column

*3.5. Load*

**Figure 16.** Analysis of RPC with fixed column boundary condition. **Figure 16.** Analysis of RPC with fixed column boundary condition.

**Figure 17.** Analysis of concrete with hinge column boundary condition. **Figure 17.** Analysis of concrete with hinge column boundary condition.

### **4. Results and Discussion 4. Results and Discussion**

Experimental results of shear strength-deformation improvement for vulnerable beam-column connection using RPC were used to validate the developed FEM approach. Different parameters from experimental work were used in numerical modeling. This approach provided a more realistic response simulation of the actual beam-column joint. The numerical results were compared with experimental results for the verification of the model as shown in Table 3. There was a negligible deviation of numerical results from experimental results for controlled concrete samples in case of maximum loads. It was 1.13% and 0.63% for CC\_S1 and CC\_S2, respectively. For RPC samples, the divergence was comparatively higher. It was 6.05% for RPC\_S1 and 6.73% for RPC\_S2. Maximum displacement variation was 7.06%, 4.18%, 3.12%, and 6.54% for CC\_S1, CC\_S2, RPC\_S1, and RPC\_S2, respectively. The maximum variation observed was 7.06% for CC\_S1 for displacement. This shows that numerical results were in strong agreement with the experi-Experimental results of shear strength-deformation improvement for vulnerable beamcolumn connection using RPC were used to validate the developed FEM approach. Different parameters from experimental work were used in numerical modeling. This approach provided a more realistic response simulation of the actual beam-column joint. The numerical results were compared with experimental results for the verification of the model as shown in Table 3. There was a negligible deviation of numerical results from experimental results for controlled concrete samples in case of maximum loads. It was 1.13% and 0.63% for CC\_S1 and CC\_S2, respectively. For RPC samples, the divergence was comparatively higher. It was 6.05% for RPC\_S1 and 6.73% for RPC\_S2. Maximum displacement variation was 7.06%, 4.18%, 3.12%, and 6.54% for CC\_S1, CC\_S2, RPC\_S1, and RPC\_S2, respectively. The maximum variation observed was 7.06% for CC\_S1 for displacement. This shows that numerical results were in strong agreement with the experimental results.

The comparison of the load-displacement curve obtained from experimental and ABAQUS simulations are shown in Figures 18–21. The shape of the ABAQUS simulations curves is quite close to the experimental curves. The maximum average discrepancy between modeling and experimental results of conventional concrete was 3–7%. Almost linear behavior was obtained using ABAQUS modeling for CC\_S1 whereas in experimental work the pattern of the graph showed nonlinearity which might be due to non-uniform

mental results. **Table 3.** Comparison of load and displacement between experimental and modeling.

CC\_S1 21.03 27,497.88 15,500 15,675.21 35.98 33.44 2.54

RPC\_S2 45.24 37,433.67 16,020 17,097.80 43.33 40.50 2.83

4.1.1. Conventional Concrete Controlled Specimens

increment of load in the experimental setup.

*4.1. Load Displacement Curve*

### *4.1. Load Displacement Curve*

## 4.1.1. Conventional Concrete Controlled Specimens

The comparison of the load-displacement curve obtained from experimental and ABAQUS simulations are shown in Figures 18–21. The shape of the ABAQUS simulations curves is quite close to the experimental curves. The maximum average discrepancy between modeling and experimental results of conventional concrete was 3–7%. Almost linear behavior was obtained using ABAQUS modeling for CC\_S1 whereas in experimental work the pattern of the graph showed nonlinearity which might be due to non-uniform increment of load in the experimental setup. *Crystals* **2021**, *11*, x FOR PEER REVIEW 15 of 22

**Figure 18.** Displacement at beam end (roller support) with a hinge boundary condition at column **Figure 18.** Displacement at beam end (roller support) with a hinge boundary condition at column end for CC\_S1 (mesh size 25 mm). 5

### 4.1.2. RPC Specimens 0

end for CC\_S1 (mesh size 25 mm).

15 20 CC S2 Modelling CC S2 Experimental The maximum discrepancy between modeling and experimental results of RPC in the case of RPC\_S1 was 6.05% while that of RPC\_S2 was 6.7%. The deviation of experimental results from modeling in RPC\_S1 was due to non-uniform increment of load and time period in the experimental setup while RPC\_S2 showed quite accurate results. Mesh size effect was studied for RPC specimens and compared with the experimental results Figures 22 and 23. Mesh size 25 was considered for RPC\_S1 and mesh size 40 for RPC\_S2 for comparison with the experimental values. **Figure 18.** Displacement at beam end (roller support) with a hinge boundary condition at column end for CC\_S1 (mesh size 25 mm). 0 10 20 30 40 Dispacement (mm)

the case of RPC\_S1 was 6.05% while that of RPC\_S2 was 6.7%. The deviation of experimental results from modeling in RPC\_S1 was due to non-uniform increment of load and time period in the experimental setup while RPC\_S2 showed quite accurate results. Mesh **Figure 19.** Displacement at beam end (roller support) with a fixed boundary condition at column end for CC\_S2 (mesh size 25 mm). **Figure 19.** Displacement at beam end (roller support) with a fixed boundary condition at column end for CC\_S2 (mesh size 25 mm).

size effect was studied for RPC specimens and compared with the experimental results

The maximum discrepancy between modeling and experimental results of RPC in the case of RPC\_S1 was 6.05% while that of RPC\_S2 was 6.7%. The deviation of experimental results from modeling in RPC\_S1 was due to non-uniform increment of load and time period in the experimental setup while RPC\_S2 showed quite accurate results. Mesh size effect was studied for RPC specimens and compared with the experimental results Figures 22 and 23. Mesh size 25 was considered for RPC\_S1 and mesh size 40 for RPC\_S2

for comparison with the experimental values.

for comparison with the experimental values.

4.1.2. RPC Specimens

**Figure 20.** Displacement at beam end (roller support) with hinge boundary condition at column end for RPC\_S1. **Figure 20.** Displacement at beam end (roller support) with hinge boundary condition at column end for RPC\_S1. 5

### *4.2. Comparison between Conventional Concrete and RPC Specimens* 0 10 20 30 40 50

20

0

25 RPC S1 EXP RPC mesh 25 mm RPC mesh 40 mm RPC mesh 45 mm Comparison between conventional concrete and RPC specimen is shown in Figures 22 and 23. RPC specimens took 10–15% more load as compared to conventional concrete-controlled specimens. Delayed peaks were obtained for RPC specimens which shows delayed damaging effect in the samples. **Figure 20.** Displacement at beam end (roller support) with hinge boundary condition at column end for RPC\_S1. Displacement (mm)

22 and 23. RPC specimens took 10–15% more load as compared to conventional concretecontrolled specimens. Delayed peaks were obtained for RPC specimens which shows delayed damaging effect in the samples. **Figure 21.** Displacement at beam end (roller support) with fixed boundary condition at column end for RPC\_S2. **Figure 21.** Displacement at beam end (roller support) with fixed boundary condition at column end for RPC\_S2.

Comparison between conventional concrete and RPC specimen is shown in Figures 22 and 23. RPC specimens took 10–15% more load as compared to conventional concretecontrolled specimens. Delayed peaks were obtained for RPC specimens which shows de-

layed damaging effect in the samples.

*4.2. Comparison between Conventional Concrete and RPC Specimens*

**Figure 22.** Comparison between concrete and RPC specimens for S1 (column hinge condition). **Figure 22.** Comparison between concrete and RPC specimens for S1 (column hinge condition).

### CC S2 Modelling CC S2 Experimental *4.3. Stiffness of Concrete and RPC Specimens* 10 Load (KN)

10 15 20 Load (KN) RPC S2 Modelling RPC S2 Experimental For the validation of the model, the stiffness of all specimens was calculated. It can be seen from Tables 4 and 5 that the initial stiffness in the RPC specimens is low in comparison to conventional concrete. Moreover, it can be observed that as the load increased the structure lost its rigidity and stiffness (the ability of a structure to resist deformation when subjected to the applied force). However, after 20% of loading RPC was still taking more load in comparison to conventional concrete as shown in Tables 4 and 5. It can also be observed from Figures 24–26 that the initial stiffness of RPC specimens is low compared to controlled concrete specimens. Figures 25 and 26 depict that as the load was increased to 25% and 50% of the ultimate load, RPC showed to have high stiffness comparatively. **Figure 22.** Comparison between concrete and RPC specimens for S1 (column hinge condition). 0 5 0 10 20 30 40 Displacement (mm)

pared to controlled concrete specimens. Figures 25 and 26 depict that as the load was increased to 25% and 50% of the ultimate load, RPC showed to have high stiffness compar-**Figure 23.** Comparison between concrete and RPC specimens for S2 (column fixed condition). **Figure 23.** Comparison between concrete and RPC specimens for S2 (column fixed condition).

be observed from Figures 24–26 that the initial stiffness of RPC specimens is low compared to controlled concrete specimens. Figures 25 and 26 depict that as the load was increased to 25% and 50% of the ultimate load, RPC showed to have high stiffness compar-

For the validation of the model, the stiffness of all specimens was calculated. It can be seen from Tables 4 and 5 that the initial stiffness in the RPC specimens is low in comparison to conventional concrete. Moreover, it can be observed that as the load increased the structure lost its rigidity and stiffness (the ability of a structure to resist deformation when subjected to the applied force). However, after 20% of loading RPC was still taking

*4.3. Stiffness of Concrete and RPC Specimens*

atively.

atively.

**Table 4.** Stiffness of all specimens from experimental work.

*Crystals* **2021**, *11*, x FOR PEER REVIEW 18 of 22

**Table 5.** Stiffness of all specimens at different loading rates (numerically). 50% 1.48 1.96 2.53 2.43

CC\_S1\_EXP CC\_S1\_MOD CC\_S2\_EXP CC\_S2\_MOD

**Stiffness (KN/mm)**

**Figure 24.** Initial stiffness of all specimens at 5% of ultimate loading. **Figure 24.** Initial stiffness of all specimens at 5% of ultimate loading.

### 223

**Stiffness (KN/mm)**

RPC\_S1\_EXP RPC\_S1\_MOD RPC\_S2\_EXP RPC\_S2\_MOD

**Figure 25.** Stiffness of all specimens at 25% of ultimate loading. **Figure 25.** Stiffness of all specimens at 25% of ultimate loading. CC\_S1\_EXP CC\_S1\_MOD CC\_S2\_EXP CC\_S2\_MOD

### *4.4. Ductility of Concrete and RPC Specimens* 5

1.28

1.48

0

(17), respectively.

1

1

1.96 2.53 1.83 2.43 1.94 2 3 CC\_S1\_EXP CC\_S1\_MOD CC\_S2\_EXP CC\_S2\_MOD RPC\_S1\_EXP RPC\_S1\_MOD RPC\_S2\_EXP RPC\_S2\_MOD The ductility displacement factor (R), as depicted in Figure 27, according to the Committee Euro International Du Beton, 1996, is defined as the ratio between failure displacement and yield displacement. The yield displacement is the lateral displacement at 80% of the ultimate load at ascending part of the curve while the failure displacement is the lateral displacement at 80% of the ultimate load at descending part of the curve [26]. Ductility factor R and ductility displacement (DD) can be obtained from Equations (16) and (17), respectively. 2.24 2.49 3.23 3.06 4.59 4.11 4.38 4.04 2 3 4

$$R = \frac{\Delta f}{\Delta y} \tag{16}$$

where ∆*f* = failure displacement ∆*y* = yield displacement 0

**Figure 25.** Stiffness of all specimens at 25% of ultimate loading.

$$DD = \frac{\Delta i}{\Delta y} \tag{17}$$

∆ (16)

**Stiffness (KN/mm)** where ∆*i* = maximum displacement in any cycle I ∆*y* = yield displacement

1

1.47

**Figure 26.** Stiffness of all specimens at 50% of ultimate loading. *4.4. Ductility of Concrete and RPC Specimens*  The ductility displacement factor (R), as depicted in Figure 27, according to the Committee Euro International Du Beton, 1996, is defined as the ratio between failure displacement and yield displacement. The yield displacement is the lateral displacement at 80% of the ultimate load at ascending part of the curve while the failure displacement is the lateral displacement at 80% of the ultimate load at descending part of the curve [26]. Ductility factor R and ductility displacement (DD) can be obtained from Equations (16) and (17), respectively. = ∆ ∆ (16) 1.48 1.28 1.96 1.47 2.53 1.83 2.43 1.94 0 1 2 3 1 CC\_S1\_EXP CC\_S1\_MOD CC\_S2\_EXP CC\_S2\_MOD RPC\_S1\_EXP RPC\_S1\_MOD RPC\_S2\_EXP RPC\_S2\_MOD

where ∆*f* = failure displacement ∆*y* = yield displacement **Stiffness (KN/mm)**

The ductility displacement factor (R), as depicted in Figure 27, according to the Committee Euro International Du Beton, 1996, is defined as the ratio between failure displacement and yield displacement. The yield displacement is the lateral displacement at 80% of the ultimate load at ascending part of the curve while the failure displacement is the lateral displacement at 80% of the ultimate load at descending part of the curve [26]. Ductility factor R and ductility displacement (DD) can be obtained from Equations (16) and

> = ∆

**Figure 26.** Stiffness of all specimens at 50% of ultimate loading. **Figure 26.** Stiffness of all specimens at 50% of ultimate loading.

where ∆*f* = failure displacement ∆*y* = yield displacement

**Figure 27.** Ductility displacement. **Figure 27.** Ductility displacement.

Table 6 displays ductility factor R and displacement ductility (DD) for all the specimens both for experimental and numerical modeling. Experimental results showed an increase of 27% and 11% in R for S1 and S2, respectively. Similarly, DD for RPC was enhanced by 29% and 12% for S1 and S2, respectively. The same trend was shown by numerical modeling results. Table 6 displays ductility factor R and displacement ductility (DD) for all the specimens both for experimental and numerical modeling. Experimental results showed an increase of 27% and 11% in R for S1 and S2, respectively. Similarly, DD for RPC was enhanced by 29% and 12% for S1 and S2, respectively. The same trend was shown by numerical modeling results.

=

where ∆*i* = maximum displacement in any cycle I ∆*y* = yield displacement

∆

∆ (17)

### **Table 6.** Ductility factor and ductility displacement. **Table 6.** Ductility factor and ductility displacement.

### A series of analysis were performed on conventional concrete and RPC beam-column **5. Conclusions**

**5. Conclusions**

joint specimens. Following conclusions were made based on experimental and numerical testing. 1. The use of RPC only in the joint region increased the overall strength of the structure A series of analysis were performed on conventional concrete and RPC beam-column joint specimens. Following conclusions were made based on experimental and numerical testing.

6. To obtain actual results, displacement control analysis should be used rather than load control analysis. With displacement control analysis it is easier to obtain the converged solutions in ABAQUS in case of highly nonlinear problems.

### **6. Recommendations**

Based on this research following recommendations can be used for future research work.

In the case of RPC, a decrease in the initial stiffness of the specimen was observed in the joint region as coarse aggregates were not used. Therefore, the use of suitable size of coarse aggregate will not only increase the initial stiffness but also increase the strength of the structure.

Steel fibers of a longer length should be used so that they can help in controlling the crack from widening. In the case of RPC, shear cracking was observed in the joint region. Combining the RPC technique with some other technique will convert the failure mechanism from the joint to the beam through their combined effect.

Besides the CDP model smeared crack modeling and brittle concrete modeling of the RPC can be used to determine the complex behavior of RPC in structure.

As there is no official Eurocode for RPC. Therefore, the development of Eurocode for RPC with and without steel fibers will enable us to clearly understand the complicated behavior of the material.

**Author Contributions:** Supervision, review, and editing, A.N.; data curation and methodology, M.A.; investigation and review, M.F.J.; conceptualization, data analysis, writing original draft preparation, M.F.J.; formal analysis and modeling, F.A.; validation, proofreading, review, M.A.M. and N.I.V. All authors have read and agreed to the published version of the manuscript.

**Funding:** The research is partially funded by the Ministry of Science and Higher Education of the Russian Federation under the strategic academic leadership program 'Priority 2030' (Agreement 075-15-2021-1333 dated 30 September 2021).

**Data Availability Statement:** The data used in this research was collected from published literature.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

**Ruihe Zhou \* , Longhui Guo and Rongbao Hong**

School of Civil Engineering and Architecture, Anhui University of Science and Technology, Huainan 232001, China<EMAIL_ADDRESS>(L.G<EMAIL_ADDRESS>(R.H.) **\*** Correspondence<EMAIL_ADDRESS>Tel.: +86-187-5602-3671

**Abstract:** In order to study the energy evolution characteristics and damage constitutive relationship of siltstone, the conventional triaxial compression tests of siltstone under different confining pressures are performed, and the evolution laws of input energy, elastic strain energy and dissipative energy of siltstone with axial strain and confining pressure are analyzed. According to the test results, the judgment criterion of the rock damage threshold is improved, and an improved three-shear energy yield criterion is proposed., The damage constitutive equation of siltstone is established based on the damage mechanics theory through the principle of minimum energy consumption and by considering the residual strength of rock, and lastly, the rationality of the model is verified by experimental data. The results reveal that (1) both the input energy and dissipative energy gradually increase with the increase of axial strain, and the elastic strain energy first increases and then decreases with the increase of axial strain, and reaches its maximum at the peak. (2) The input energy and dissipation energy increase exponentially with the increase of the confining pressure, and the elastic strain energy increases linearly with the increase of confining pressure. (3) According to the linear relationship between the sum of shear strain energy and hydrostatic pressure, an improved three-shear energy yield criterion is established. (4) The model curve can better describe the strain softening stage and the residual strength characteristics of siltstone. The relative standard deviation between the model results and the test results is only 4.35%, which verifies the rationality and feasibility of the statistical damage constitutive model that is established in this paper.

**Keywords:** energy evolution; minimum energy dissipation principle; three-shear energy yield criterion; damage variable; constitutive model