Asian Journal of Civil Engineering最新文献

{"title":"AI-augmented multi-objective structural optimization and statistical evaluation of flat slab systems with and without shear walls in high-rise RCC buildings under seismic loads","authors":"Rashtra Gaurav Singh Chauhan,&nbsp;Yash Mothe,&nbsp;Chayan Gupta","doi":"10.1007/s42107-026-01635-5","DOIUrl":"10.1007/s42107-026-01635-5","url":null,"abstract":"<div>\u0000 \u0000 <p>This study presents a comprehensive comparative analysis of flat slab systems with and without shear walls in reinforced concrete (RCC) multi-storey buildings under seismic loading. Using STAAD.Pro, structural models were developed for 10-, 20-, and 30-storey buildings with constant plan dimensions (20 m × 30 m). The study evaluates the influence of shear walls on key structural responses, including lateral displacement, storey drift, principal stresses, Von Mises and Tresca stresses, and stress concentration at slab corners. The presence of shear walls significantly reduces displacement and stress values, enhancing overall structural stability. To advance prediction capabilities, machine learning models—ANN, SVR, and XGBoost—were trained to estimate maximum principal stress using 500 simulated data points. Among them, XGBoost achieved the highest accuracy (R² = 0.948). Furthermore, an advanced Non-dominated Sorting Genetic Algorithm III (NSGA-III) was applied to perform multi-objective optimization targeting minimization of lateral displacement, principal stress, storey drift, and Von Mises stress. The optimization yielded Pareto-optimal solutions satisfying IS 456:2000 and IS 1893:2016 constraints. The findings offer valuable insights for structural engineers and designers to adopt efficient, earthquake-resistant configurations. The integration of AI and NSGA-III provides a robust framework for future seismic design and optimization of high-rise flat slab systems.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 5","pages":"2641 - 2662"},"PeriodicalIF":0.0,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147631805","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}

{"title":"Optimization of Voronoi tessellation with diagrid & hexagrid using MATLAB","authors":"Niharika Sharma,&nbsp;V. R. Patel,&nbsp;Manvendra Verma","doi":"10.1007/s42107-025-01632-0","DOIUrl":"10.1007/s42107-025-01632-0","url":null,"abstract":"<div>\u0000 \u0000 <p>The search for efficient lateral load–resisting systems in high-rise buildings has led to the exploration of innovative structural geometries such as Voronoi tessellations, diagrids, and hexagrid. This study presents a comparative investigation of these three structural configurations when applied to tall building systems. Using consistent material properties, geometric parameters, and loading conditions, the performance of each system is evaluated in terms of global stiffness, lateral displacement, inter-storey drift, load path efficiency, and structural redundancy. Voronoi-based façades, characterized by irregular cellular patterns, are examined for their potential to distribute stresses organically and enhance structural resilience. Diagrid systems are analyzed for their well-known axial load efficiency and reduced need for vertical columns, while hexagrid systems are assessed for their uniform load distribution and geometric stability. The comparative results highlight the strengths and limitations of each system, offering insights into their suitability for different architectural and performance demands. This study aims to support engineers and designers in selecting optimal structural patterns for modern tall buildings, balancing aesthetics, material efficiency, and structural performance.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 5","pages":"2593 - 2608"},"PeriodicalIF":0.0,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147631679","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}

{"title":"A user-centric machine learning framework for predicting multi-modal accessibility in transit-oriented development zones for sustainable urban construction in tier-2 Indian cities","authors":"Krishna Yadav,&nbsp;Kavita Dehalwar,&nbsp;Shashikant Nishant Sharma","doi":"10.1007/s42107-025-01625-z","DOIUrl":"10.1007/s42107-025-01625-z","url":null,"abstract":"<div>\u0000 \u0000 <p>This study presents a user-centric machine learning (ML) framework for predicting multi-modal accessibility in Transit-Oriented Development (TOD) zones across tier-2 Indian cities. Using survey data from 400 respondents in Bhopal, Jaipur, Nagpur, and Coimbatore, the research integrates socio-demographic, behavioral, and perceptual variables to model accessibility scores that reflect real-world user experiences. Seven supervised ML models—Random Forest, Support Vector Regressor, Artificial Neural Network, Decision Tree, XGBoost, CatBoost, and Extra Trees—were evaluated using R², RMSE, and MAE. XGBoost emerged as the top performer with an R² of 0.879. SHAP analysis revealed integration with public transport, physical access, affordability, safety, and user satisfaction as the most influential features. Sensitivity analysis and Partial Dependence Plots validated the robustness and policy relevance of the top predictors. Further, disaggregated analysis by gender, age, and income highlighted critical equity gaps in perceived accessibility. The framework aligns with SDG 11.2 by providing a scalable decision-support tool to identify intervention points for inclusive and sustainable TOD planning. The study offers actionable insights for urban policymakers, transport planners, and smart city missions, emphasizing the integration of subjective user experience with data-driven predictive modeling to promote equitable urban mobility outcomes.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 5","pages":"2447 - 2464"},"PeriodicalIF":0.0,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147631856","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}

{"title":"Correction: An opposition-enhanced Rao-2 metaheuristic for integrated time–cost–quality–safety optimization","authors":"Sudhanshu Maurya,&nbsp;Ajay Kumar Yadav,&nbsp;Mukesh Joshi,&nbsp;Neha Verma,&nbsp;Gyana Ranjana Panigrahi,&nbsp;Amanullah Noori","doi":"10.1007/s42107-026-01663-1","DOIUrl":"10.1007/s42107-026-01663-1","url":null,"abstract":"","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 5","pages":"2871 - 2871"},"PeriodicalIF":0.0,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147631738","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}

{"title":"Machine-learning-driven modeling of strength in high-performance graphene oxide induced nano-concrete composites","authors":"B. Ramesh Rao,&nbsp;Shriram Marathe,&nbsp;Abhay Sridhar,&nbsp;Akarsh Pattan Kotrabasappa","doi":"10.1007/s42107-026-01636-4","DOIUrl":"10.1007/s42107-026-01636-4","url":null,"abstract":"<div>\u0000 \u0000 <p>The objective of this investigation is to integrate a <i>machine learning</i> (ML) approach with experimental results to predict the 28 day compressive strength of high-performance concrete, which has been designed for use as a structural layer in pavements. This high-performance concrete has been developed by modifying it with <i>silica fume</i> (SF) and <i>graphene oxide</i> (GO). In order to create a predictive data set from experimental testing of all mixes, a comprehensive experimental program that included 750 individual test specimens was conducted. The experimental program tested the control (CC), SF-only (CS1-CS4), GO-only (CG1-CG4), and the optimized factorial mixes of SF-GO (CS1G1-CS4G4) were used to develop the predictive data set. Testing results showed a marked improvement in the mechanical behavior when the optimal dosages of SF = 7% and GO = 0.15%, which resulted in a hybrid mix of CS3G3 with a compressive strength increase of 29.30% compared to the control concrete (77 MPa vs. 59.7 MPa). Also, there were improvements in flexural strength, split-tensile strength, elastic modulus, reduced water absorption, reduced permeable voids, improved fatigue life, and durability (acid and sulfate). Using a train-test ratio of 80:20, ten fold cross-validation to assess all models; eight supervised ML algorithms were tested (Linear Regression, Decision Trees, Support Vector Regressor, AdaBoost Regressor, Random Forest, XGBoost, and Gradient Boosting) with voting algorithm. Performance of each model was determined by four engineering indexes (A10, A20, PI, IA, OBJ) in addition to common statistical evaluation (RMSE, MAE, MSE, R² and CV%). Ensemble models outperformed baseline learners, with <i>Random Forest</i> achieving the highest predictive accuracy (RMSE = 2.47 MPa, R² score = 0.776, A10 ≈ 0.995, A20 = 1.0), closely followed by <i>XGBoos</i>t and <i>Gradient Boosting</i>. Feature-importance ranking consistently identified SF, GO and OPC as the primary predictors governing strength development, reflecting micro and nano-scale synergistic enhancement mechanisms. Violin-plot dispersion, predicted-versus-actual alignment and estimator-sensitivity analysis confirmed the robustness, stability and reproducibility of ensemble-based modelling. Overall, the findings demonstrate that ML- based predictive frameworks, when supported by the experimentally consistent datasets and engineering validation metrics, can provide reliable strength forecasts suitable for performance-based <i>optimization</i> of high-performance nano-concrete mixes.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 5","pages":"2663 - 2680"},"PeriodicalIF":0.0,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147631688","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}

{"title":"Estimation of ultimate shear strength and investigation of the behaviour of composite steel plate shear wall with engineered cementitious composite","authors":"Aliakbar Hayatdavoodi,&nbsp;Shami Nejadi,&nbsp;Harry Far","doi":"10.1007/s42107-026-01643-5","DOIUrl":"10.1007/s42107-026-01643-5","url":null,"abstract":"<div>\u0000 \u0000 <p>Steel shear wall systems have become increasingly prevalent in modern building design as effective elements for resisting lateral loads. Concurrently, reinforced concrete shear walls have been extensively utilized owing to their substantial stiffness and load-resisting capability. Despite their widespread application and advantages, both systems exhibit inherent performance limitations. Reinforced concrete shear walls are vulnerable to tensile cracking in regions subjected to tension, whereas steel shear walls are prone to out-of-plane buckling of the infill steel plate under compressive stresses. Such buckling behavior adversely affects shear stiffness, load-carrying capacity, and energy dissipation performance. Composite steel plate shear walls (CSPSWs) have been developed to overcome these deficiencies by combining the advantageous properties of steel and concrete systems. A typical CSPSW configuration consists of boundary frame components and a steel infill plate that is laterally restrained by reinforced concrete panels attached to one or both sides. In most existing implementations, conventional reinforced concrete serves as the restraining material. The present study evaluates and compares the structural performance of CSPSWs employing conventional reinforced concrete with those incorporating Engineered Cementitious Composite (ECC). The shear behavior of CSPSWs subjected to pure shear loading is investigated through a combination of analytical modeling and numerical simulation. An analytical formulation is proposed to predict the ultimate shear strength of CSPSWs and is subsequently validated using finite element analysis. The comparison demonstrates good agreement between the analytical predictions and numerical results, confirming the reliability of the proposed model. Additionally, the results indicate that the use of ECC as a restraining material leads to significant enhancements in both ultimate shear capacity and ductility when compared to conventional reinforced concrete.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 5","pages":"2787 - 2803"},"PeriodicalIF":0.0,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147631835","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}

{"title":"Numerical investigation of the influence of parameters on concrete-filled circular column and steel box connections","authors":"Ary Shehab Jamil,&nbsp;Mehrzad TahamouliRoudsari,&nbsp;AllahReza Moradi Garoosi,&nbsp;Javad Esfandiari","doi":"10.1007/s42107-026-01639-1","DOIUrl":"10.1007/s42107-026-01639-1","url":null,"abstract":"<div>\u0000 \u0000 <p>This study provides a numerical assessment of a key parameter governing the seismic performance of steel beam-to-circular column connections that utilize a steel box and stiffeners. As a first step, the numerical model was validated against the experimental specimen, and the influence of the stiffener length on the steel box-to-column joint was investigated. The cyclic analyses were conducted to evaluate the influence of the stiffener length-to-column diameter ratio on the connection performance. Subsequently, the effect of this parameter on the ultimate moment and elastic stiffness, was assessed. Based on curve fitting, approximate equations were also introduced to calculate the ultimate moment and elastic stiffness. Finally, the connection rigidity was evaluated according to the AISC code. The results show that increasing the stiffener length leads to a significant reduction in connection stiffness and ultimate moment. This stiffness reduction reached 51% in models with larger sections. Furthermore, in models with larger beam sections, the impact of stiffener length was more pronounced compared to those with smaller sections. The developed analytical expressions for assessing the ultimate moment and elastic stiffness accurately forecast the results for these connections.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 5","pages":"2711 - 2725"},"PeriodicalIF":0.0,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147631845","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}

{"title":"Investigation on the flexural performance of preloaded RC T-beams strengthened by the NSM side-face technique using CFRP laminates","authors":"K. Mallikarjuna,&nbsp;P. M. Ravindra,&nbsp;H. N. Jagannath Reddy,&nbsp;V. Naveen,&nbsp;D. P. Archana","doi":"10.1007/s42107-026-01644-4","DOIUrl":"10.1007/s42107-026-01644-4","url":null,"abstract":"<div><p>Older buildings and structures usually require some form of maintenance and repair due to the harsh environments, natural calamities, varying load requirements, corrosion and poor maintenance. Fiber-reinforced polymer composites (FRPC) have been found to be a viable solution in order to increase the life of the structures and improve their performance. Such materials are significant in strengthening and rehabilitation of reinforced concrete (RC) buildings in making them stronger and more resistant to different load conditions and the unfavorable environment. Six RC T-beams were in this case cast and tested using a two-point loading arrangement. Among these, two T-beams with two different percentages of reinforcement were chosen to serve as control beams. The Near-Surface Mounted Side Face (NSMS) approach was used to strengthen the remaining four T-beams. The remaining four T-beams underwent strengthening using NSMS technique. Two of these beams had a percentage of reinforcement (P_t) of 0.463, while the other two beams had a P_t of 0.825. These beams were tested under two-point loading and the ultimate load, deflection and debonding failure were measured. The experimental study involved the comparison of the performance of the control T-beams, and the strengthened specimens. The findings showed that T-beams reinforced with NSMS method showed a slight increase in performance relative to control beams.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 5","pages":"2805 - 2821"},"PeriodicalIF":0.0,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147631846","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}

{"title":"Sustainable jute fiber-reinforced concrete: an integrated ensemble and interpretable machine learning framework for strength prediction","authors":"P. Kalpana,&nbsp;Swathi Lenka,&nbsp;Kishore Bhamidipati,&nbsp;M. Susmitha,&nbsp;J. Prasanya,&nbsp;Venubabu Rachapudi,&nbsp; Tahera,&nbsp;Srushti V. Hosamath","doi":"10.1007/s42107-026-01640-8","DOIUrl":"10.1007/s42107-026-01640-8","url":null,"abstract":"<div>\u0000 \u0000 <p>Eco-friendly alternatives to expensive synthetic fibers and high-resource materials in concrete have been developing rapidly over recent years. Due to the cost-effective and renewable nature of jute fibers, they have great potential as a source of low-carbon concrete; however, their effects on the mechanical characteristics of concrete are still poorly defined. The integrated framework combining ensemble and interpretable machine learning algorithms is developed to investigate and predict the compressive strength of Jute Fiber-Reinforced Concrete (JFRC), based on a large and highly harmonized database of literature-based experimental data. Four existing Machine Learning (ML) methods, namely the Random Forest-Gradient Boosting (RF-Gb) Ensemble, AdaBoost, CatBoost, and Relevance Vector Machine (RVM), were used. It was determined that CatBoost and the RF-Gb method performed best in terms of predicting experimental results (compressive strength), and that there was excellent correlation between the predicted and actual experimental results. The SHAP-based interpretability analysis also revealed that flexural strength and tensile strength at split-tensile testing were the most significant factors affecting compressive strength, while jute-fiber dosage, workability, and replacement of fine aggregate were secondary influencing factors. These findings show the feasibility of using jute fibers as a sustainable reinforcement in the production of low-carbon concrete and that the application of interpretable ML can provide a means of supporting informed design of mixes for optimized, low-carbon, natural fiber-concrete products. Overall, the present study provides a data-driven and scalable framework that supports the development of high-performance and environmentally responsible, fiber-reinforced composites.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 5","pages":"2727 - 2757"},"PeriodicalIF":0.0,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147631784","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}

{"title":"An opposition-enhanced Rao-2 metaheuristic for integrated time–cost–quality–safety optimization","authors":"Sudhanshu Maurya,&nbsp;Ajay Kumar Yadav,&nbsp;Mukesh Joshi,&nbsp;Neha Verma,&nbsp;Gyana Ranjana Panigrahi,&nbsp;Amanullah Noori","doi":"10.1007/s42107-026-01642-6","DOIUrl":"10.1007/s42107-026-01642-6","url":null,"abstract":"<div>\u0000 \u0000 <p>Efficient management of time, cost, quality, and safety (TCQS) has become increasingly vital in modern construction projects, where rising complexity requires powerful multi-objective optimization tools. This study proposes an Opposition-Enhanced Rao-1 metaheuristic that incorporates a simple, plain opposition-based learning (OBL) mechanism to strengthen the exploratory capability of the traditional Rao-2 algorithm. By applying opposition during population initialization and iterative updating, the enhanced algorithm effectively broadens the search space, reduces premature convergence, and improves the diversity of candidate solutions. A real-world 13-activity building project with multiple execution modes is modeled to represent discrete time–cost trade-offs, quality performance levels, and safety risk ratings, forming a challenging nonlinear multi-objective optimization problem. The performance of the proposed algorithm is evaluated against the classical Rao-2, Latin Hypercube Sampling (LHS)-based NSGA-III, and the Adaptive Opposition Slime Mold Algorithm (AOSMA). Results indicate that the Opposition-Enhanced Rao-2 produces a denser and more uniformly distributed Pareto front while achieving superior improvement in project duration, total cost, overall quality score, and cumulative safety risk. Compared with LHS-based NSGA-III and AOSMA, the proposed approach demonstrates higher convergence precision, stronger stability, and enhanced robustness across multiple simulation runs. Overall, the findings confirm that the Opposition-Enhanced Rao-2 metaheuristic offers a reliable, computationally efficient, and high-performing approach for integrated TCQS optimization, providing decision-makers with well-balanced alternatives for safer, more cost-effective, and higher-quality construction project planning.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 5","pages":"2773 - 2786"},"PeriodicalIF":0.0,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147631757","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}