Asian Journal of Civil Engineering最新文献_第7页

Explainable LightGBM model for predicting compressive strength of silica fume modified high-volume fly ash concrete 硅灰改性大体积粉煤灰混凝土抗压强度的可解释LightGBM模型

Asian Journal of Civil Engineering Pub Date : 2025-06-23 DOI: 10.1007/s42107-025-01411-x

Anish Kumar, Sameer Sen, Sanjeev Sinha, Bimal Kumar, Chaitanya Nidhi

{"title":"Explainable LightGBM model for predicting compressive strength of silica fume modified high-volume fly ash concrete","authors":"Anish Kumar, Sameer Sen, Sanjeev Sinha, Bimal Kumar, Chaitanya Nidhi","doi":"10.1007/s42107-025-01411-x","DOIUrl":"10.1007/s42107-025-01411-x","url":null,"abstract":"<div><p>This research introduces a robust machine learning framework for estimating the compressive strength of concrete, utilizing a Light Gradient Boosting Machine (LightGBM) regression algorithm. The model was developed using a diverse dataset that included different mix proportions of fly ash, silica fume, cement, fine and coarse aggregates, along with varying curing durations. After a thorough hyperparameter optimization process, the final model incorporated a learning rate of 0.1, 200 boosting iterations, an unrestricted tree depth, and 31 maximum leaf nodes. The model demonstrated strong predictive accuracy, achieving an R² value of 0.99 on the training set and 0.97 on the testing set, with corresponding Mean Absolute Errors (MAE) of 0.70 MPa and 1.35 MPa. Feature importance derived from SHAP values highlighted curing duration, silica fume percentage, and cement content as primary contributors to strength outcomes. Additional interpretation through partial dependence plots and monotonicity analysis showed that the model’s predictions aligned with expected trends in concrete behavior. Sensitivity testing indicated that changes in silica fume content and coarse aggregate proportion produced the most significant fluctuations in predicted strength.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 9","pages":"3951 - 3968"},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145168473","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 NSGA-III and improved arithmetic optimization algorithm (IAOA) approach for time, cost, and resource allocation 一种混合NSGA-III和改进的算法优化算法（iaaa）的时间、成本和资源分配方法

Asian Journal of Civil Engineering Pub Date : 2025-06-23 DOI: 10.1007/s42107-025-01391-y

Vu Hong Son Pham, Ngoc Thao Phuong Hoang, Duc Anh Tuan Le

引用次数: 0

Review on sustainability in 3D concrete printing: focus on waste utilization and life cycle assessment 混凝土3D打印的可持续性研究综述：重点关注废物利用和生命周期评估

Asian Journal of Civil Engineering Pub Date : 2025-06-23 DOI: 10.1007/s42107-025-01408-6

Vishakha Sakhare, Neha Khairnar, Ulka Dahatonde, Shilpa Mashalkar

{"title":"Review on sustainability in 3D concrete printing: focus on waste utilization and life cycle assessment","authors":"Vishakha Sakhare, Neha Khairnar, Ulka Dahatonde, Shilpa Mashalkar","doi":"10.1007/s42107-025-01408-6","DOIUrl":"10.1007/s42107-025-01408-6","url":null,"abstract":"<div><p>Constructions using 3D printing are supposed to have potential benefits in sustainability, increased construction productivity, resource efficiency. Having spectacular benefits in the construction era, it announces many demanding challenges in the selection of appropriate material. When agro-industrial wastes are incorporated into concrete materials, 3D printing may also have significant benefits for sustainability. This paper's primary goal is to examine the sustainability aspects considered in delivering 3D printed structured mainly from raw precursors and life cycle assessment perspective. Initial data bases collected from search engines like google scholar, science direct was further scrutinized for selecting the papers. This study reviews mix composition used for printing 3D assembly using waste material like Granulated blast furnace slag, fly ash, silica fume etc. Effectiveness of the waste materials contributing to mechanical and fresh characteristics are explored. Industrial waste mostly be dumped found to have significant impact printability properties also contributing to sustainability aspects. Nonetheless, life cycle assessment (LCA) results are used to show how using wastes in 3D printing concrete materials affects environment in comparison to using traditional materials. Study deals with three pillars of sustainability i.e., Economical, environmental and sustainability. Outcome of the paper will help the researcher to optimally choose the waste precursors to deliver quality 3D printed structure with sustainable perspective.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 9","pages":"3589 - 3605"},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145167948","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

Crack detection and categorisation on steel surfaces using machine learning techniques 使用机器学习技术对钢表面进行裂纹检测和分类

Asian Journal of Civil Engineering Pub Date : 2025-06-23 DOI: 10.1007/s42107-025-01405-9

Maheswara Rao Bandi, Laxmi Narayana Pasupuleti, Anup Kumar Sah, Hari Jyothula

{"title":"Crack detection and categorisation on steel surfaces using machine learning techniques","authors":"Maheswara Rao Bandi, Laxmi Narayana Pasupuleti, Anup Kumar Sah, Hari Jyothula","doi":"10.1007/s42107-025-01405-9","DOIUrl":"10.1007/s42107-025-01405-9","url":null,"abstract":"<div><p>The improved methods for fracture localization and detection in civil construction and other industries have led to an increase in the prevalence of crack detection. It is crucial to identify and maintain the integrity of cracks on steel surfaces to ensure structural safety. Conventional gradient-based and evolutionary algorithmic methods are crucial for detecting and assessing damage. Deep learning methodologies are being utilized more frequently in the field of structural damage identification. We train and evaluate the photos at varying ratios, utilizing CNN-based ResNet-50 and AlexNet algorithms. Initially, we constructed the training dataset for the model and classified the damage into three categories: steel beam, steel plate, and corroded steel. This study employed two neural networks, ResNet-50 and AlexNet, to classify crack images and identify damages. Additionally, train the constructed CNN using images with a resolution of 224 × 224 pixels for ResNet-50 and 227 × 227 pixels for AlexNet. Upon completion of the training and validation processes for ResNet-50, the peak average accuracy was attained utilizing 80% of the training dataset. Similarly, we achieved the highest accuracy with 80% of the training data after conducting training for AlexNet.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 9","pages":"3901 - 3914"},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145167949","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

Time–cost trade-off optimization in generalized construction projects using an opposition learning-augmented multi-objective Jaya algorithm 基于对立学习增强多目标Jaya算法的广义建设项目时间成本权衡优化

Asian Journal of Civil Engineering Pub Date : 2025-06-23 DOI: 10.1007/s42107-025-01401-z

Sudhanshu Maurya, Bayram Ateş, T. C. Manjunath, Mohammad Azim Eirgash

{"title":"Time–cost trade-off optimization in generalized construction projects using an opposition learning-augmented multi-objective Jaya algorithm","authors":"Sudhanshu Maurya, Bayram Ateş, T. C. Manjunath, Mohammad Azim Eirgash","doi":"10.1007/s42107-025-01401-z","DOIUrl":"10.1007/s42107-025-01401-z","url":null,"abstract":"<div><p>This study introduces a multi-objective Jaya (MOO-Jaya) algorithm to unravel time–cost trade-off problems (TCTPs) in construction project scheduling. The model integrates opposition-based learning (OBL) to enhance population initialization and generation jumping mechanisms, thereby improving solution diversity and convergence efficiency. To evaluate performance, the MOO-Jaya algorithm is tested on a real-world construction project comprising 29 activities with complex precedence constraints. The project accounts for generalized precedence relationships (GPRs), including start-to-start (SS), start-to-finish (SF), finish-to-start (FS), and finish-to-finish (FF) activity dependencies, with both positive and negative lag times, enabling realistic modeling of activity overlapping and schedule compression. Computational results are benchmarked against established metaheuristics like the non-dominated sorting genetic algorithm II (NSGA-II), hybrid genetic algorithm with quantum simulated annealing (HGAQSA), and the core Jaya algorithm. The suggested algorithm demonstrates superior Pareto front convergence, solution diversity, and computational efficiency compared to these counterparts. Findings underscore its practical applicability in addressing multi-criteria decision-making problems, offering project planners a robust tool for optimizing time and cost objectives under complex scheduling constraints. </p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 9","pages":"4009 - 4022"},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145168821","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

Formulation of an efficient strain-based finite element for large deflection analysis and free vibration of plates 一种有效的基于应变的大挠度分析和板的自由振动有限元公式

Asian Journal of Civil Engineering Pub Date : 2025-06-22 DOI: 10.1007/s42107-025-01400-0

Abdulrahman M. AL-Nadhari, Djamal Hamadi, Maria Legouirah

{"title":"Formulation of an efficient strain-based finite element for large deflection analysis and free vibration of plates","authors":"Abdulrahman M. AL-Nadhari, Djamal Hamadi, Maria Legouirah","doi":"10.1007/s42107-025-01400-0","DOIUrl":"10.1007/s42107-025-01400-0","url":null,"abstract":"<div><p>This study presents the development and evaluation of a finite element based on the strain-based approach for the nonlinear analysis of plate structures. Unlike traditional displacement-based methods, the strain-based formulation allows enhanced control over the deformation field, offering improved accuracy and adaptability, particularly under complex loading and boundary conditions. The proposed element combines a membrane component for capturing large displacement effects and a bending component derived from Reissner–Mindlin theory, making it suitable for both static and free vibration analyses. A comprehensive set of numerical examples is conducted to assess the performance of the element, including square plates with various boundary conditions, trapezoidal plates, and plates with concentrated or uniformly distributed loads. Comparative studies demonstrate excellent agreement with experimental results, nonlinear analytical solutions, and benchmark finite element models such as ABAQUS S4R. The results confirm the element’s high accuracy, robust convergence behavior across regular and irregular meshes, and its effectiveness in modeling both standard and irregular geometries. This research highlights the potential of the strain-based approach for advanced nonlinear plate analysis and extends its applicability beyond linear regimes.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 9","pages":"3821 - 3841"},"PeriodicalIF":0.0,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145168194","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 and numerical investigation of reinforced cellular lightweight concrete beams under cyclic loading: performance assessment and regression analysis using response surface methodology 循环荷载下钢筋蜂窝轻量混凝土梁的试验与数值研究：使用响应面方法进行性能评估与回归分析

Asian Journal of Civil Engineering Pub Date : 2025-06-22 DOI: 10.1007/s42107-025-01399-4

Amarjeet Pandey, Anurag Sharma, Mahasakti Mahamaya

{"title":"Experimental and numerical investigation of reinforced cellular lightweight concrete beams under cyclic loading: performance assessment and regression analysis using response surface methodology","authors":"Amarjeet Pandey, Anurag Sharma, Mahasakti Mahamaya","doi":"10.1007/s42107-025-01399-4","DOIUrl":"10.1007/s42107-025-01399-4","url":null,"abstract":"<div><p>This research focuses on the development of lightweight concrete using industrial waste materials such as fly ash and Ground Granulated Blast Furnace Slag as partial replacements to promote sustainability in construction. The primary objective was to reduce the overall density of concrete without compromising its mechanical strength, making it viable for both structural and non-structural applications. Various mix proportions were tested, and the compressive and flexural strengths of cube and beam specimens were evaluated at 7 and 28 days of curing. The results demonstrated a significant reduction in weight, with optimized mixes maintaining satisfactory strength levels. Among them, the RC3 mix exhibited superior performance in both strength and density reduction. To further optimize the mix design and predict performance outcomes, Response Surface Methodology was employed alongside machine learning techniques, producing 100 predictive solutions. Contour and 3D surface plots provided insights into the interactions between replacement content and mechanical properties. Additionally, predicted versus actual strength graphs showed strong alignment, validating the model's accuracy. This study underscores the potential of lightweight concrete made with industrial byproducts as an eco-friendly alternative, offering a balance between structural integrity and reduced material usage, and contributing to the advancement of sustainable construction practices.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 9","pages":"3993 - 4007"},"PeriodicalIF":0.0,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145168193","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

AI-enhanced reinforced concrete with SCM and AZO nanoparticles for superior mechanical and antibacterial performance 人工智能增强钢筋混凝土与SCM和AZO纳米颗粒具有卓越的机械和抗菌性能

Asian Journal of Civil Engineering Pub Date : 2025-06-21 DOI: 10.1007/s42107-025-01403-x

Amol Shivaji Mali, Shailesh Ghodke, Utkarsh Maheshwari, Kirti Zare, Vikas Pralhad Dive

{"title":"AI-enhanced reinforced concrete with SCM and AZO nanoparticles for superior mechanical and antibacterial performance","authors":"Amol Shivaji Mali, Shailesh Ghodke, Utkarsh Maheshwari, Kirti Zare, Vikas Pralhad Dive","doi":"10.1007/s42107-025-01403-x","DOIUrl":"10.1007/s42107-025-01403-x","url":null,"abstract":"<div><p>This study investigates the effects of supplementary cementitious material and aluminum zinc oxide (AZO) nanoparticles on concrete performance. Ordinary Portland cement (OPC) was replaced with microsilica, metakaolin, clinoptilolite, and AZO to analyze mechanical properties and antibacterial effectiveness. Compressive strength, ultrasonic pulse velocity (UPV), rapid chloride penetration test (RCPT), and bacterial removal efficiency were evaluated. The optimal formulation (73.6% OPC, 19% microsilica, 4% metakaolin, 1% clinoptilolite, and 2.4% AZO) achieved superior 28-day compressive strength (31.65 MPa), excellent homogeneity (index 0.975), and very low chloride penetrability. This composition demonstrated remarkable antibacterial properties, with up to 98.7% removal of methicillin-resistant Staphylococcus aureus after 30 min of UV exposure, while maintaining practical application timeframes. AI/ML models were developed to predict concrete properties, with random forest (RF) showing the highest accuracy (R<sup>2</sup> > 0.97). Feature importance analysis identified AZO content as the most significant predictor (32.8–36.9%) across all models. Microstructural characterization revealed that 2.4% AZO content enhanced surface hydrophobicity (contact angle 115.67°) and reduced porosity by 34.6%. This research establishes an optimal concrete formulation for mechanical properties and significant antibacterial capabilities for healthcare environments.</p><h3>Graphical Abstract</h3>\u0000<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 9","pages":"3865 - 3887"},"PeriodicalIF":0.0,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s42107-025-01403-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145168262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning-driven optimization of concrete mixes incorporating treated sludge 采用处理污泥的混凝土混合料的机器学习驱动优化

Asian Journal of Civil Engineering Pub Date : 2025-06-20 DOI: 10.1007/s42107-025-01404-w

Hrishikesh Kumar Singh, Aditya Verma, Salil Kumar Gupta, Divyansh Singh, Deepak V. P. Suman

{"title":"Machine learning-driven optimization of concrete mixes incorporating treated sludge","authors":"Hrishikesh Kumar Singh, Aditya Verma, Salil Kumar Gupta, Divyansh Singh, Deepak V. P. Suman","doi":"10.1007/s42107-025-01404-w","DOIUrl":"10.1007/s42107-025-01404-w","url":null,"abstract":"<div><p>This study investigates the mechanical performance of concrete incorporating treated sludge as a partial cement replacement, analyzing compressive, flexural, and split tensile strengths across various curing durations. Experimental results demonstrate strength improvement with extended curing time and optimal water-to-cement (W/C) ratios. While 10%?15% cement replacement maintains structural viability, higher substitution levels (beyond 17.5%) lead to strength deterioration, with concrete exceeding 22.5% sludge replacement exhibiting limited structural feasibility. To enhance predictive accuracy and optimize sustainable concrete mix design, machine learning models are employed to estimate compressive strength. machine learning models. Extra Trees Regressor, AdaBoost, Random Forest, and Gradient Boosting Regressor were developed to predict compressive strength. Among these, the Gradient Boosting and Random Forest models demonstrated the highest predictive accuracy, with R<sup>2</sup> values of 0.975 and 0.977, respectively. The study confirms that integrating machine learning with experimental methods offers a robust, data-driven approach for optimizing concrete mix design and supports the sustainable reuse of sewage sludge in construction applications.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 9","pages":"3889 - 3899"},"PeriodicalIF":0.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145166830","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

Predictive optimized model for seismic damage in reinforcement girder bridge 钢筋梁桥震害预测优化模型

Asian Journal of Civil Engineering Pub Date : 2025-06-20 DOI: 10.1007/s42107-025-01395-8

Goushiya Sayyed, M. D. Ahirrao, V. D. Jaysingpure, Sachin C. Deshmukh

{"title":"Predictive optimized model for seismic damage in reinforcement girder bridge","authors":"Goushiya Sayyed, M. D. Ahirrao, V. D. Jaysingpure, Sachin C. Deshmukh","doi":"10.1007/s42107-025-01395-8","DOIUrl":"10.1007/s42107-025-01395-8","url":null,"abstract":"<div><p>This study aims to develop an innovative Zebra Regression Prediction Rule (ZRPR) for evaluating seismic damage in reinforced girder bridges subjected to lateral seismic loads under three-directional conditions. This investigation evaluates the reinforced concrete Girder Bridge exposed to seismic loads in three different formats. Using an advanced technique, the model for dependability evaluation on the bridge is carried out using ANSYS software. The model is analyzed using the Zebra Regression Prediction Rule (ZRPR), which is the suggested approach. It is an innovative technique used in seismic analysis to determine the probable damage to the structural components. The seismic load is applied in three different manners: lateral oscillating loads from both directions and lateral oscillating loads in individual directions separately. The behavior pattern of the bridge is also studied. The linear inlet in peak ground acceleration is employed by implementing the oscillations in the reinforced girder bridge structure in accordance with the ordinary elemental earthquake viability evaluation. The results indicate that while the bridge undergoes lateral seismic forces on both sides, damage begins to occur when the peak ground acceleration reaches 1.5 m/s<sup>2</sup>, and the center midspan is the location where the damage first appears. When the lateral seismic load is perpendicular to the bridge’s length, damage begins at a ground-peak acceleration of 1.7 m/s<sup>2</sup>.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 9","pages":"3761 - 3771"},"PeriodicalIF":0.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145167780","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