Asian Journal of Civil Engineering最新文献_第10页

Smart multi-objective scheduling in construction using LHS-NSGA-III for sustainable project delivery with time cost and environmental impact optimization 基于LHS-NSGA-III的施工智能多目标调度，实现时间成本和环境影响优化的可持续项目交付

Asian Journal of Civil Engineering Pub Date : 2025-10-17 DOI: 10.1007/s42107-025-01565-8

Sanjay Singh Bhadouriya, Manoj Sharma

{"title":"Smart multi-objective scheduling in construction using LHS-NSGA-III for sustainable project delivery with time cost and environmental impact optimization","authors":"Sanjay Singh Bhadouriya, Manoj Sharma","doi":"10.1007/s42107-025-01565-8","DOIUrl":"10.1007/s42107-025-01565-8","url":null,"abstract":"<div><p>The construction industry plays a pivotal role in socio-economic development but remains a major contributor to environmental degradation due to emissions, noise, and excessive resource consumption. Traditional scheduling frameworks primarily focus on minimizing project duration and cost, often overlooking environmental sustainability. This study proposes a novel hybrid multi-objective optimization model the Latin Hypercube Sampling–Non-dominated Sorting Genetic Algorithm III (LHS-NSGA-III), which integrates Latin hypercube sampling for improved population diversity with NSGA-III for robust many-objective optimization. The developed resource-constrained time-cost-environmental trade-off (RCTCET) model simultaneously minimizes project completion time (PCT), project completion cost (PCC), and project environmental impact (PEI), enabling informed and sustainable decision-making. A comprehensive case study involving 25 interdependent construction activities, each with multiple execution modes and diverse environmental footprints, is used to validate the model’s applicability. The optimization process generates a diverse set of Pareto-optimal solutions, which are further analyzed using clustering, trade-off plots, and correlation analysis. Comparative evaluation with established metaheuristics demonstrates the superiority of the proposed approach in terms of solution diversity, convergence, and hypervolume metrics. This research establishes the feasibility and effectiveness of incorporating environmental objectives into construction scheduling and provides a scalable framework for sustainable project delivery in alignment with global environmental performance targets.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 3","pages":"1351 - 1368"},"PeriodicalIF":0.0,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147339858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modified adaptive weight Rao-2 algorithm for construction time-cost trade-off optimization problems 基于改进自适应加权Rao-2算法的施工时间成本权衡优化问题

Asian Journal of Civil Engineering Pub Date : 2025-10-16 DOI: 10.1007/s42107-025-01557-8

Rakesh Gupta, Anil Rajpoot, Radhe Shyam, Bimalendu Dash, Bayram Ateş, Krushna Chandra Sethi

{"title":"Modified adaptive weight Rao-2 algorithm for construction time-cost trade-off optimization problems","authors":"Rakesh Gupta, Anil Rajpoot, Radhe Shyam, Bimalendu Dash, Bayram Ateş, Krushna Chandra Sethi","doi":"10.1007/s42107-025-01557-8","DOIUrl":"10.1007/s42107-025-01557-8","url":null,"abstract":"<div>\u0000 \u0000 <p>The Modified Adaptive Weight Approach (MAWA) is a widely used and relatively straightforward method for addressing time–cost optimization problems, which are typically formulated as multi-objective optimization tasks. Metaheuristic algorithms are particularly effective for these problems since they iteratively refine a randomly generated population of candidate solutions. However, a noted drawback of the standard MAWA is its assignment of uniform weight factors to all solutions, without considering their individual fitness or distribution in the search space. To overcome this limitation, this study introduces a novel multi-objective framework that integrates the Rao-2 algorithm with MAWA, yielding a set of Pareto-optimal solutions. The performance of this hybrid MAWA–Rao-2 model was evaluated using benchmark construction project case studies from the literature, each consisting of 146 activities. The obtained results were compared with Hybrid heuristic meta-heuristic (HHMH), non-dominated sorting TLBO, non-dominated sorting Aquila optimizer, non-dominated sorting AOA reported in the literature. Findings demonstrate that the MAWA–Rao-2 algorithm serves as a robust and efficient approach for solving time–cost trade-off problems (TCTP) in construction engineering and management.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 3","pages":"1209 - 1219"},"PeriodicalIF":0.0,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147339400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction and optimization of self-compacting geopolymer concrete with and without steel fibres using response surface methodology 用响应面法预测和优化含和不含钢纤维的自密实地聚合物混凝土

Asian Journal of Civil Engineering Pub Date : 2025-10-16 DOI: 10.1007/s42107-025-01559-6

Rohan Sawant, Deepa A. Joshi, Radhika Menon

{"title":"Prediction and optimization of self-compacting geopolymer concrete with and without steel fibres using response surface methodology","authors":"Rohan Sawant, Deepa A. Joshi, Radhika Menon","doi":"10.1007/s42107-025-01559-6","DOIUrl":"10.1007/s42107-025-01559-6","url":null,"abstract":"<div>\u0000 \u0000 <p>This study uses statistical modelling and Response Surface Methodology (RSM) to evaluate the design, optimisation, and performance evaluation of Fly Ash-GGBS-based Self-Compacting Geopolymer Concrete (SCGPC), both with and without steel fibres. The self-compatibility requirements were considered when creating mixtures in compliance with EFNARC guidelines. Three types of concrete were made: conventional self-compacting concrete (SCC), self-compacting geopolymer fibre-reinforced concrete (SCGPFRC) with varying percentages of steel fibre, and self-compacting concrete using fly ash and GGBS as binders. Both fresh and hardened properties were evaluated, and the material’s durability was determined through abrasion resistance testing. RSM was used in conjunction with a quadratic model to explore the effect of input variables on compressive, flexural, and split tensile strengths. Although the model’s prediction dependability was restricted, it had appropriate precision. When compared to the SCC and SCGPFRC processes, the SCGPC demonstrated significantly improved flowability and passing ability. The addition of steel fibres resulted in an increase in flexural and split tensile strengths of 16.23% and 41.90%, respectively, at an optimal fibre content of 1.65% (SCGPFRC2), despite the fact that compressive strength decreased somewhat in SCGPC compared to SCC. Despite the fact that the statistical model’s prediction accuracy varies slightly, the experimental results show that SCGPFRC has excellent mechanical performance and durability, establishing it as an environmentally friendly and high-performing material for structural applications.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 3","pages":"1237 - 1254"},"PeriodicalIF":0.0,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147339399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vibration control of reinforced concrete high-rise building with end shear walls under seismic loads 地震荷载作用下端部剪力墙钢筋混凝土高层建筑的振动控制

Asian Journal of Civil Engineering Pub Date : 2025-10-16 DOI: 10.1007/s42107-025-01560-z

Mehran Akhavan Salmassi, Paweł Ciężkowski, Damian Markuszewski

{"title":"Vibration control of reinforced concrete high-rise building with end shear walls under seismic loads","authors":"Mehran Akhavan Salmassi, Paweł Ciężkowski, Damian Markuszewski","doi":"10.1007/s42107-025-01560-z","DOIUrl":"10.1007/s42107-025-01560-z","url":null,"abstract":"<div>\u0000 \u0000 <p>Vibration control in tall buildings is a critical aspect of modern structural design, directly influencing safety, occupant comfort, and long-term durability. Due to their slender and flexible geometry, high-rise structures are especially susceptible to dynamic forces such as wind and seismic activity, which can induce resonance and lead to structural damage or collapse. One effective strategy for mitigating such vibrations involves the use of end shear walls specialized shear wall elements that connect the extremities of reinforced concrete core walls across all floors. These walls enhance diaphragm stiffness and reduce stress concentrations at shear wall ends, contributing to improved dynamic stability. This research examines how end shear walls affect the seismic behavior of two 30-story structures, utilizing far-field earthquake records and nonlinear dynamic analysis. The model incorporating end shear walls demonstrated a 22% reduction in peak acceleration, a 76% drop in variance, and a 51% decrease in standard deviation in the X-direction, as confirmed through SPSS analysis. Q-Q plots further revealed up to 38% vibration reduction, underscoring the effectiveness of end shear walls in enhancing seismic resilience and improving overall dynamic performance in high-rise structures.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 3","pages":"1255 - 1269"},"PeriodicalIF":0.0,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147339405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating machine learning and deep learning for XRD data: predictive regression and image-based classification 结合机器学习和深度学习的XRD数据：预测回归和基于图像的分类

Asian Journal of Civil Engineering Pub Date : 2025-10-15 DOI: 10.1007/s42107-025-01550-1

P. Sai Vineela, B. Narendra Kumar, Bhupesh Deka

{"title":"Integrating machine learning and deep learning for XRD data: predictive regression and image-based classification","authors":"P. Sai Vineela, B. Narendra Kumar, Bhupesh Deka","doi":"10.1007/s42107-025-01550-1","DOIUrl":"10.1007/s42107-025-01550-1","url":null,"abstract":"<div>\u0000 \u0000 <p>The complex, nonlinear, and random interactions between diffraction parameters and the phase composition of altered concrete systems are often not adequately represented by standard modelling methods. The objective of this research is to employ data from X-Ray Diffraction (XRD) to test the applicability of advanced machine learning methods to phase identification and classification in concrete modified with magnesium chloride and ground granulated blast furnace slag (GGBS). After preparing, curing, and testing various mixes of concrete using different proportions of GGBS and magnesium chloride to measure their compressive strength, the most suitable combination was selected for further microstructural analysis. The two models trained were Vision Transformer (ViT) and Extreme Gradient Boosting (XGBoost). Applied and tested on the 2θ intensity profiles obtained in XRD data. In XGBoost, the original XRD dataset was used as input for the model. As an additional improvement to the dataset, a synthetic dataset is available. It was synthesised with the help of Generative Adversarial Networks (GANs). Both datasets were similar in the measurement of the Mean square error MSE and R<sup>2</sup> values. Additionally, the performance of the vision transformer is also superior to that of the Convolutional Neural Network (CNN). The declassification of XRD images of conventional concrete and magnesium chloride-modified concrete was then verified using a confusion matrix. The findings reveal that XGBoost and vision can provide comparable results. A transformer can be a helpful method for accurately interpreting XRD data, providing new insights. It allows identifying and defining phases more precisely in cement-based materials, which is also in terms of classification.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 3","pages":"1089 - 1109"},"PeriodicalIF":0.0,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147339639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Balancing complexity in retrofitting: an opposition-based NSGA-III framework for time, cost, quality, energy, safety, environment, and client satisfaction 平衡改造的复杂性：基于时间、成本、质量、能源、安全、环境和客户满意度的反对意见NSGA-III框架

Asian Journal of Civil Engineering Pub Date : 2025-10-15 DOI: 10.1007/s42107-025-01563-w

Miguel Villagómez-Galindo, Ana Beatriz Martínez-Valencia, Sudhanshu Maurya, Sushma Jat, Gaurav Shrivastava, T. C. Manjunath

{"title":"Balancing complexity in retrofitting: an opposition-based NSGA-III framework for time, cost, quality, energy, safety, environment, and client satisfaction","authors":"Miguel Villagómez-Galindo, Ana Beatriz Martínez-Valencia, Sudhanshu Maurya, Sushma Jat, Gaurav Shrivastava, T. C. Manjunath","doi":"10.1007/s42107-025-01563-w","DOIUrl":"10.1007/s42107-025-01563-w","url":null,"abstract":"<div><p>This study presents a comprehensive multi-objective optimization framework for retrofitting projects by integrating seven critical performance dimensions: time, cost, quality, energy consumption, safety, environmental impact, and client satisfaction. A novel opposition-based non-dominated sorting genetic algorithm III (OBNSGA-III) is proposed to address the high dimensionality and complex trade-offs inherent in retrofitting decisions. Key innovations include the dual application of opposition-based learning during population initialization and offspring generation, the use of a bivariate normal distribution to model quality as a function of time and cost, and the application of fuzzy logic for safety risk evaluation. The proposed framework is validated using a real-world case study involving 11 retrofitting aspects and 33 intervention options. The OBNSGA-III algorithm successfully generated 18 Pareto-optimal solutions. Among them, the best-performing solution achieved a project duration of 30 days, a quality index of 0.913, and a client satisfaction score of 4.7, outperforming benchmark algorithms such as NSGA-III, MOPSO, and OB-MODE across 13 standard performance indicators, including hypervolume (0.92) and generational distance (1.35). These results underscore the model’s ability to deliver diverse, high-quality trade-off solutions under real-world constraints. The TCQESEC framework provides a robust decision-support tool for project managers and policymakers, enabling sustainable, efficient, and client-centric retrofitting strategies in complex urban infrastructure environments.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 3","pages":"1313 - 1328"},"PeriodicalIF":0.0,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147339630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Metaheuristic-optimized neural networks for punching shear capacity prediction in recycled aggregate concrete RC flat slabs 再生骨料混凝土混凝土平板冲剪承载力预测的元启发式优化神经网络

Asian Journal of Civil Engineering Pub Date : 2025-10-15 DOI: 10.1007/s42107-025-01554-x

Albaraa Alasskar, Shambhu Sharan Mishra

{"title":"Metaheuristic-optimized neural networks for punching shear capacity prediction in recycled aggregate concrete RC flat slabs","authors":"Albaraa Alasskar, Shambhu Sharan Mishra","doi":"10.1007/s42107-025-01554-x","DOIUrl":"10.1007/s42107-025-01554-x","url":null,"abstract":"<div><p>This research investigates the application of hybrid artificial neural network (ANN) models that are optimized using Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) algorithms to predict the punching shear capacity (PSC) of flat slabs made of recycled aggregate concrete (RAC). To ensure robust model development, a database of 101 experimental specimens was compiled, processed, and divided into training (70%) and testing (30%) datasets. Regression error characteristic (REC) curves, Taylor diagrams, and statistical indices (R2, RMSE, MAE, WMAPE, WI, and LMI) were used to extensively assess the model’s accuracy, external validation, and uncertainty analysis. ANN-ABC has the best predictive value, as it delivered the lowest values of errors and R<sup>2</sup> of 0.9593 (training) and 0.9527 (testing). The robustness of its analysis was also supported by narrow uncertainty bounds, favourable rankings across all evaluation criteria, and analysis of the REC curve (AUC = 0.755 during training and 0.563 during testing). On the contrary, ANN-PSO worked with moderate accuracy, whereas ANN-GWO worked with the lowest accuracy. The sensitivity analysis showed that effective depth, reinforcement area, and water-to-cement ratio were the most sensitive parameters that control PSC behavior. Unlike previous PSC prediction studies that relied mainly on tree-based or kernel-based ML methods, this work is the first to benchmark swarm-intelligence-optimized ANNs (ANN-ABC, ANN-PSO, ANN-GWO). This hybridization improves predictive accuracy while ensuring robustness and interpretability for RAC structural applications.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 3","pages":"1167 - 1182"},"PeriodicalIF":0.0,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147339637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Metaheuristic-trained adaptive activation neural networks for reliable buckling resistance prediction of high strength steel columns 基于元启发式训练的自适应激活神经网络的高强钢柱抗屈曲抗力可靠预测

Asian Journal of Civil Engineering Pub Date : 2025-10-15 DOI: 10.1007/s42107-025-01561-y

Snehal K. Kamble, Sangita Meshram, Pallavi S. Chakole, Minakshi Chauragade, Lowlesh N. Yadav, Priti Golar, Nisha Gongal, Vidhi Pitroda, Archana N. Mungle, Alaka Das

{"title":"Metaheuristic-trained adaptive activation neural networks for reliable buckling resistance prediction of high strength steel columns","authors":"Snehal K. Kamble, Sangita Meshram, Pallavi S. Chakole, Minakshi Chauragade, Lowlesh N. Yadav, Priti Golar, Nisha Gongal, Vidhi Pitroda, Archana N. Mungle, Alaka Das","doi":"10.1007/s42107-025-01561-y","DOIUrl":"10.1007/s42107-025-01561-y","url":null,"abstract":"<div><p>Paper presents a validated method of metaheuristic-trained adaptive activation neural networks that are supposed to integrate mechanics with imperfections and reliability calibration into a unified process. The method starts with a physics-regularized column state encoder that carries equilibrium and energy consistency while compactly summarizing geometry, residual stresses, imperfections, and boundary conditions. A state latent informs a stability-energy guided adaptive activation network, where metaheuristic tuning of nonlinear activations is supposed to adjust predictions toward mechanical principles and increase fidelity. The third stage, imperfection manifold synthesizer, produces statistically and physically realistic imperfection fields conditioned on the latent states and stability sensitivities thereby enlarging sparse experimental catalogs. Building on this, a reliability-preserving resistance calibrator that solves the inverse reliability problem and stretches strength reduction factors and dispersion surfaces smoothed out across shapes and load conditions completes the process. Finally, a code-integrable decision map constructor compress calibrates surface into interpretable rule tables and charts while verifying reliability against adversarial imperfections in process. Across pooled datasets of hot-rolled and welded sections, mean resistance errors attained by the method is almost 3% with calibrated uncertainty coverage and reliability factors well positioned with respect to code target requirements. Beyond accuracy, the framework adds a transferable latent state, a defensible reliability link, and compact design maps on the path toward safer and more implementable design standards.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 3","pages":"1271 - 1287"},"PeriodicalIF":0.0,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147339642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Experimental insights and hybridized ensemble machine learning validation of fiber reinforced geopolymer concrete strength 纤维增强地聚合物混凝土强度的实验见解和混合集成机器学习验证

Asian Journal of Civil Engineering Pub Date : 2025-10-15 DOI: 10.1007/s42107-025-01562-x

Suebha Khatoon, Kaliluthin A K, Sanjog Chhetri Sapkota

{"title":"Experimental insights and hybridized ensemble machine learning validation of fiber reinforced geopolymer concrete strength","authors":"Suebha Khatoon, Kaliluthin A K, Sanjog Chhetri Sapkota","doi":"10.1007/s42107-025-01562-x","DOIUrl":"10.1007/s42107-025-01562-x","url":null,"abstract":"<div><p>The present study intends to study the mechanical properties of fiber-reinforced geopolymer concrete (FRGC) experimentally and validate the results using state-of-the-art machine learning. At 28 days, compressive strength (CS) of 72.6 MPa, and split tensile strength (STS) of 9.4 MPa were recorded. The CS deteriorated with a further increase in the NF content due to the cluster formation, while the STS increased with increasing fiber dosage. The hybrid ML models, including Random Forest (RF) and XGBoost (XGB), were fine-tuned using Grid Search (GS) and Giant Armadillo (GA) algorithm based on 5-fold cross-validation. The GA-XGB model had maximum accuracy to predict CS (R² = 0.988, RMSE = 0.032 MPa) and STS (R² = 0.985, RMSE = 0.029 MPa) in testing sets. SHAP analysis supported molarity and SS/SH as the influencing factor for CS, and meanwhile, NF for STS. Shapley additive explanations (SHAP), Partial dependence (PDP) and Individual Conditional Expectation (ICE) plots confirmed these trends by showing the nonlinear effects of each independent variable on strength predictions. A graphical user interface (GUI) was also designed to aid practical use, which the user can use to insert long short mix parameters and receive instant predictions for CS and STS. The close match between experimental measurements and ML predictions, along with the importance of explainability and GUI integration, proves that the developed FRGPC design framework is robust, transparent and usable in real applications.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 3","pages":"1289 - 1312"},"PeriodicalIF":0.0,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147339640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing the accuracy of punching shear predictions in reinforced concrete flat slabs by refining the constitutive laws of concrete 通过细化混凝土的本构规律，提高钢筋混凝土平板冲剪预测的准确性

Asian Journal of Civil Engineering Pub Date : 2025-10-15 DOI: 10.1007/s42107-025-01553-y

Albaraa Alasskar, Shambhu Sharan Mishra

{"title":"Enhancing the accuracy of punching shear predictions in reinforced concrete flat slabs by refining the constitutive laws of concrete","authors":"Albaraa Alasskar, Shambhu Sharan Mishra","doi":"10.1007/s42107-025-01553-y","DOIUrl":"10.1007/s42107-025-01553-y","url":null,"abstract":"<div>\u0000 \u0000 <p>This study aims to enhance the predictive accuracy of punching shear strength in reinforced concrete flat slabs by refining constitutive laws of concrete through the integration of key material properties. Specifically, compressive strength, tensile strength, and fracture energy parameters are intrinsically linked to aggregate characteristics, are incorporated due to their significant influence on inclined shear crack surface roughness and aggregate interlock governing shear transfer mechanisms. Two modified constitutive laws were refined based on insights from existing literature. The first model modifies the tensile stress-displacement relationship, while the second adjusts the peak strain at compressive strength and the critical crack opening displacement. These refined models were applied in the finite element software ABAQUS/Explicit and validated against experimental slab tests reported in the literature. Results demonstrate that the proposed models significantly improve the accuracy of predicted failure loads, which show deviations of only 5.6% and 2.7% from experimental outcomes for the first and second models, respectively. Compared to the reference model by Beaulieu and Polak (2023), the first and second models achieved improvements in failure load prediction accuracy by 9.6% and 12.2%, respectively. Likewise, displacement prediction accuracy improved by 9.9% and 16.2%. These findings underscore the importance of incorporating aggregate-related parameters into constitutive models for more reliable structural performance predictions in RC flat slabs.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 3","pages":"1145 - 1165"},"PeriodicalIF":0.0,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147339641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0