Asian Journal of Civil Engineering最新文献_第4页

A review on joint and section optimization for steel warehouse

Asian Journal of Civil Engineering Pub Date : 2025-01-10 DOI: 10.1007/s42107-025-01263-5

Musaddiq S. Momin, R. D. Patil

{"title":"A review on joint and section optimization for steel warehouse","authors":"Musaddiq S. Momin, R. D. Patil","doi":"10.1007/s42107-025-01263-5","DOIUrl":"10.1007/s42107-025-01263-5","url":null,"abstract":"<div><p>The review focuses on the design and optimization of steel structures using section and joint optimization parameters. Steel buildings are not only easy to construct but also provide superior strength. Additionally, their scrap retains value after demolition, making them a cost-effective option. Optimization offers an economical solution while reducing the overall weight of the structure. This review concentrates on the design and optimization of steel warehouse frames. The optimization process involves optimization of sections using sectional optimization concept and joint optimization will also be performed for steel warehouse structures. Conventional and pre-engineered frames will be designed using Staad Pro software with optimization using genetic algorithm. Joint design will be done by Idea Statica software and optimization will be carried in Ram Connection software. While optimization of steel structures has been conducted extensively, this study evaluates the suitability of different approaches based on section and joint optimization. In this work, the design of steel members in compliance with Indian standards, will be undertaken for both conventional and pre-engineered structures. The study aims to analyze, design, optimize and compare the economic and lightweight solutions for steel warehouses with a focus on parameters like section dimensions, frame weights, and joint design parameters.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 4","pages":"1373 - 1379"},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698632","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

A hybrid light GBM and Harris Hawks optimization approach for forecasting construction project performance: enhancing schedule and budget predictions 混合轻型GBM和哈里斯鹰优化方法预测建设项目绩效：增强进度和预算预测

Asian Journal of Civil Engineering Pub Date : 2025-01-09 DOI: 10.1007/s42107-024-01207-5

Mu’taz Abuassi, Bader Aldeen Almahameed, Majdi Bisharah, Mo’ath Abu Da’abis

{"title":"A hybrid light GBM and Harris Hawks optimization approach for forecasting construction project performance: enhancing schedule and budget predictions","authors":"Mu’taz Abuassi, Bader Aldeen Almahameed, Majdi Bisharah, Mo’ath Abu Da’abis","doi":"10.1007/s42107-024-01207-5","DOIUrl":"10.1007/s42107-024-01207-5","url":null,"abstract":"<div><p>The study investigates machine learning applications in civil engineering, which are biased towards construction management. The hybrid model was developed for better schedule deviation and budget overrun performance, based on Harris Hawks Optimization combined with Light GBM. Using HHO for feature selection, the model identified the most influencing factors like Project Size, Risk Score, and Change Orders. This optimized the prediction process. This hybrid approach outperformed the traditional machine learning models, including Random Forest and XGBoost, by an optimum RMSE of 15.32 days schedule deviations and $25,840 budget overruns, proving more accurate and efficient. Therefore, this underpins the potential AI-driven solutions for improving project planning, risk mitigation, and decision-making within construction management. Future work will need to refine models as artificial intelligence becomes integrated into practice within civil engineering. Additional predictive variables will be further investigated while extending the approach to other areas of construction management and civil engineering applications.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 2","pages":"577 - 591"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142994634","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

Predicting compressive strength of concrete using advanced machine learning techniques: a combined dataset approach 利用先进的机器学习技术预测混凝土抗压强度：一种组合数据集方法

Asian Journal of Civil Engineering Pub Date : 2025-01-08 DOI: 10.1007/s42107-024-01247-x

Abinash Mandal

{"title":"Predicting compressive strength of concrete using advanced machine learning techniques: a combined dataset approach","authors":"Abinash Mandal","doi":"10.1007/s42107-024-01247-x","DOIUrl":"10.1007/s42107-024-01247-x","url":null,"abstract":"<div><p>Assessing the compressive strength of concrete is crucial to ensure safety in civil engineering projects. Conventional methods often rely on manual testing and empirical formulae, which can be time-consuming and error-prone, respectively. In this study, the advanced machine learning techniques are employed to predict the strength. The paper explores multiple base models, such as linear regression (including polynomial features up to degree 3), decision trees, support vector machines, and k-nearest neighbors. Hyperparameter tuning is utilized to improve the models and cross-validation is carried out to check any overfitting issues. In addition, artificial neural networks and ensemble learning methods such as voting, stacking, random forest, gradient boosting, and XGBoost are implemented. Two datasets from different sources are utilized in this study. Results indicate that models trained on one dataset do not perform satisfactorily on second dataset and vice-versa, due to covariant shift in the datasets. In fact, this approach implied that rather than relying on advanced machine learning models, linear regression gave approximate results. After combining these datasets, the models were successful in generalizing over wider range of features. The results show that gradient boosting achieved the highest accuracy with an R<sup>2</sup> score of 0.93 and an RMSE of 3.54 for the training data of combined datasets. The paper further delves into finding the lower and upper bound of the predictions with 95% confidence interval using bootstrapping technique. The author recognizes the necessity of diverse datasets to improve model generalization. However, if the models are trained on limited datasets, and inference is to be made on those with different distributions of features than training data, then the prediction interval can be the indication of the confidence of the models. Further for inference on new unseen data, Mahalanobis distance is measured to indicate whether the data is outlier, thus improving the reliability.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 3","pages":"1225 - 1241"},"PeriodicalIF":0.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638508","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

Multi-objective optimization of responsible sourcing, consumption, and production in construction supply chains: an NSGA-III approach toward achieving SDG 12

Asian Journal of Civil Engineering Pub Date : 2025-01-08 DOI: 10.1007/s42107-024-01252-0

Manish Bharadwaj, Manoj Patwardhan, Kamal Sharma

{"title":"Multi-objective optimization of responsible sourcing, consumption, and production in construction supply chains: an NSGA-III approach toward achieving SDG 12","authors":"Manish Bharadwaj, Manoj Patwardhan, Kamal Sharma","doi":"10.1007/s42107-024-01252-0","DOIUrl":"10.1007/s42107-024-01252-0","url":null,"abstract":"<div><p>This study presents a multi-objective optimization approach for enhancing responsible sourcing, consumption, and production in construction supply chains, aligning with Sustainable Development Goal 12 (SDG 12). Using the Non-Dominated Sorting Genetic Algorithm III (NSGA-III), the research addresses complex trade-offs between environmental impact, cost-effectiveness, and social responsibility in construction projects. Data from industry case studies, including real-world construction projects, and simulations reflecting varying material costs, emissions regulations, and logistical challenges were used to validate the model. The findings reveal Pareto-efficient solutions, with up to a 9.4% reduction in carbon emissions and 3.3% cost savings while achieving a 7% improvement in social responsibility metrics. Sensitivity analysis demonstrates the model’s robustness to changes in material costs and supply chain disruptions. These results underscore NSGA-III’s effectiveness in generating optimized solutions that minimize environmental footprint, enhance resource efficiency, and promote ethical practices. This research provides actionable insights for construction firms and policymakers, offering a scalable model to integrate sustainable practices into construction supply chains and advance SDG 12 objectives.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 3","pages":"1305 - 1319"},"PeriodicalIF":0.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638581","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

Development and evaluation of a Novel strain-based rectangular membrane element for static and free vibration analysis

Asian Journal of Civil Engineering Pub Date : 2025-01-07 DOI: 10.1007/s42107-024-01254-y

Randa Bourenane, Sifeddine Abderrahmani, Abdulrahman M. AL-Nadhari

引用次数: 0

Support vector machine-based prediction model for the compressive strength for concrete reinforced with waste plastic and fly ash 基于支持向量机的废塑料和粉煤灰加固混凝土抗压强度预测模型

Asian Journal of Civil Engineering Pub Date : 2025-01-06 DOI: 10.1007/s42107-024-01256-w

Anish Kumar, Sameer Sen, Sanjeev Sinha

{"title":"Support vector machine-based prediction model for the compressive strength for concrete reinforced with waste plastic and fly ash","authors":"Anish Kumar, Sameer Sen, Sanjeev Sinha","doi":"10.1007/s42107-024-01256-w","DOIUrl":"10.1007/s42107-024-01256-w","url":null,"abstract":"<div><p>In the current study, the effect of the inclusion of waste plastic in different quantities (0–10%) on the compressive strength of fly ash reinforced concrete is explored. Compressive strength decreases with increasing plastic waste due to its inert, hydrophobic nature and poor bonding within the concrete matrix, while 10% fly ash improves strength slightly through pozzolanic reactions that densify the matrix and reduce voids. Support vector machine (SVM) was explored as a potential machine learning technique for accurately predicting and modeling the compressive strength of concrete. Predictive models were developed using SVM-radial basis function (RBF), SVM-linear, SVM-power and linear regression. The models were analyzed using performance metrics such as mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), mean squared logarithmic error (MSLE), root mean squared logarithmic error (RMSLE), coefficient of determination (R<sup>2</sup>), mean absolute percentage error (MAPE), Willmott's index of agreement, Mielke and Berry index, and Legates and McCabe's index. Taylors diagram was also used to analyze the models. SVM-RBF model outperformed all other models with an R<sup>2</sup> value of 0.969 and 0.771 in training and testing respectively. Sensitivity analysis revealed that % plastic waste is the most influential parameter in predicting the compressive strength of concrete with a score of 59.04%.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 4","pages":"1429 - 1447"},"PeriodicalIF":0.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698574","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

Assessing the impact of corner rounding and geogrid orientation on the structural response of externally confined masonry columns

Asian Journal of Civil Engineering Pub Date : 2025-01-03 DOI: 10.1007/s42107-024-01246-y

Sherry Rose Jose, Job Thomas

{"title":"Assessing the impact of corner rounding and geogrid orientation on the structural response of externally confined masonry columns","authors":"Sherry Rose Jose, Job Thomas","doi":"10.1007/s42107-024-01246-y","DOIUrl":"10.1007/s42107-024-01246-y","url":null,"abstract":"<div><p>This paper presents an experimental evidence on the integrity of masonry columns externally wrapped with biaxial geogrid embedded in cementitious mortar. The specimens in research was grouped into two sets. First eighteen specimens compares the performance of columns confined with geogrid versus CFRP. While the next eighteen specimens investigates the effect of geogrid orientation on confinement. These comparative study are applied on two sets of columns: one with sharp cornered square columns and another with rounded square columns with 20 mm corner radius. A total of thirty six columns were tested. The columns measured 230 mm in width, 230 mm in depth and 760 mm in height. The failure mode, peak load, load displacement curve and strain energy are compared and discussed. The test results revealed that geogrid confinement improves both load carrying capacity, ductility and energy absorption capacity of masonry columns relative to CFRP confined and unconfined masonry condition. Experimental study on orientation angle of geogrid ribs revealed its impact on load capacity, energy absorption and ductility. Analytical models available in various literatures were used to predict the ultimate stress of masonry column. These were then compared to predict the reliability of various models for geogrid confinement.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 3","pages":"1209 - 1223"},"PeriodicalIF":0.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638267","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

Performance of a multi-story reinforced concrete flat plate slab building with and without steel bracing under Seismic loading

Asian Journal of Civil Engineering Pub Date : 2024-12-29 DOI: 10.1007/s42107-024-01253-z

Ibrahim S. I. Harba, Abdulkhalik J. Abdulridha, Ahmed A. M. Al-Shaar

{"title":"Performance of a multi-story reinforced concrete flat plate slab building with and without steel bracing under Seismic loading","authors":"Ibrahim S. I. Harba, Abdulkhalik J. Abdulridha, Ahmed A. M. Al-Shaar","doi":"10.1007/s42107-024-01253-z","DOIUrl":"10.1007/s42107-024-01253-z","url":null,"abstract":"<div><p>This study evaluates the seismic performance of multi-story reinforced concrete flat plate slab buildings with and without steel bracing. The structural behavior of 4, 6, and 8-story buildings under seismic loading was analyzed using ETABS software, incorporating data from Halabjah, Chi-Chi, and Kobe earthquakes. Nonlinear dynamic and pushover analyses assessed parameters such as story drift, roof displacement, and strain responses. Steel bracing configurations (center core, exterior corner, and exterior side) significantly improved seismic resilience by enhancing lateral stiffness, reducing displacement, and controlling drift. Results showed center-core bracing as the most effective configuration, achieving displacement reductions up to 52.32% and drift reductions up to 59.98%. Bracing also minimized the formation of plastic hinges, enhancing energy dissipation and structural integrity. The study highlights the impact of brace placement, building height, and earthquake intensity on seismic performance. At the same time, shorter buildings exhibited more pronounced benefits and taller structures required optimized bracing strategies to increase flexibility and lateral force demands. These findings emphasize the necessity of steel bracing for improving seismic safety and resilience in reinforced concrete buildings, particularly in earthquake-prone regions, and provide insights for future seismic design and retrofitting practices.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 3","pages":"1321 - 1338"},"PeriodicalIF":0.0,"publicationDate":"2024-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638568","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 investigation on mechanical properties of lightweight reactive powder concrete using lightweight expanded clay sand 轻质膨胀粘土砂轻质活性粉混凝土力学性能试验研究

Asian Journal of Civil Engineering Pub Date : 2024-12-27 DOI: 10.1007/s42107-024-01229-z

Ahmadshah Abrahimi, V. Bhikshma

{"title":"Experimental investigation on mechanical properties of lightweight reactive powder concrete using lightweight expanded clay sand","authors":"Ahmadshah Abrahimi, V. Bhikshma","doi":"10.1007/s42107-024-01229-z","DOIUrl":"10.1007/s42107-024-01229-z","url":null,"abstract":"<div><p>This study investigates the mechanical properties of lightweight reactive powder concrete (LWRPC) under normal curing conditions, with a focus on grades M70, M80, and M90. The research was conducted in two phases. In the first phase, conventional reactive powder concrete (RPC) was formulated using quartz sand and 0–30% supplementary cementitious materials (microsilica and alccofine), guided by the Elkem Material Mix Analyzer (EMMA) and the modified Andreassen model. In the second phase, lightweight expanded clay sand (LECS) was incorporated to develop LWRPC, and its mechanical properties were assessed. The study developed mix proportions for the specified grades and identified 10% microsilica and 20% alccofine as an effective blend for improving strength and workability, while LECS contributed to a more than 20% reduction in density. The developed LWRPC grades achieved 86–90% of its 28-day compressive strength within 7 days, with an average density of 1893 kg/m<sup>3</sup>, 22% lower than corresponding normal high-strength concrete (NHSC) grades, resulting in a 35% increase in structural efficiency. The modulus of elasticity of LWRPC was found to be 10% higher than high-strength lightweight concrete (HSLWC) in the literature. Additionally, flexural and splitting tensile strengths revealed improvements of 24% and 63%, respectively, compared to HSLWC, and 11% and 22% relative to NHSC grades. Although LWRPC has a higher cost ($239/m<sup>3</sup>) approximately three times that of NHSC, the results demonstrate that it offers superior structural performance, positioning it as a high-performance lightweight concrete.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 2","pages":"913 - 930"},"PeriodicalIF":0.0,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142995589","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 machine learning for optimizing sustainable interior design: enhancing aesthetic and functional rehabilitation in housing projects 优化可持续室内设计的元启发式机器学习：增强住房项目的美学和功能修复

Asian Journal of Civil Engineering Pub Date : 2024-12-27 DOI: 10.1007/s42107-024-01225-3

Mayyadah Fahmi Hussein, Mazin Arabasy, Mohammad Abukeshek, Tamer Shraa

{"title":"Metaheuristic machine learning for optimizing sustainable interior design: enhancing aesthetic and functional rehabilitation in housing projects","authors":"Mayyadah Fahmi Hussein, Mazin Arabasy, Mohammad Abukeshek, Tamer Shraa","doi":"10.1007/s42107-024-01225-3","DOIUrl":"10.1007/s42107-024-01225-3","url":null,"abstract":"<div><p>The paper investigates the amalgamation of LightGBM and Enhanced Colliding Bodies Optimization (ECBO) to establish a resilient framework for sustainable interior design optimization in residential projects. The main goal is to harmonize aesthetic appeal, functionality, and energy efficiency by applying modern machine learning and metaheuristic optimization methods. LightGBM was utilized for predictive modeling of essential design outcomes, achieving good prediction accuracy, with <i>R</i>-squared values of 0.892 for energy savings, 0.839 for functional enhancements, and 0.782 for aesthetics. Critical elements, including sustainable materials, project budget, and energy efficiency ratings, surfaced as pivotal influences on design improvements. The ECBO further refined these design elements, yielding a 28.13% enhancement in aesthetic evaluations, a 22.86% gain in functionality, a 41.56% advancement in energy savings, and a 29.17% decrease in carbon footprint. Compared to conventional algorithms such as Particle Swarm Optimization and Genetic Algorithm, the ECBO exhibited enhanced convergence velocity and solution efficacy. This study presents a thorough, data-centric methodology for sustainable interior design, offering an efficient framework for attaining many design objectives in housing rehabilitation.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 2","pages":"829 - 842"},"PeriodicalIF":0.0,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142995648","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