Asian Journal of Civil Engineering最新文献

{"title":"Building information modeling for predicting seismic demand and energy consumption","authors":"Pandimani,&nbsp;Y. Raviteja,&nbsp;P. Bilgates,&nbsp;S. H. Vamsi Krishna","doi":"10.1007/s42107-025-01267-1","DOIUrl":"10.1007/s42107-025-01267-1","url":null,"abstract":"<div><p>The performance assessment of multi-storey RC buildings under seismic excitation is vital to understanding their vibration behavior and the vulnerable effects on the structural elements. Similarly, energy analysis has emerged as a critical parameter in building design and is increasingly gaining significance amidst the escalating threats of energy crises and global warming. This study emphasizes the seismic demand and energy analysis of an institutional hostel building structure of a 1063m² area. A 3-D concrete structure is modeled in the Staad Program to investigate the structural responsiveness under gravity and lateral forces. The responses like base shear, time period, maximum shear force, and bending moments are predicted. Sensitivity analysis considering zone and soil-type effects is performed to investigate their influence on the seismic response. It can be disclosed that the peak base shear, storey drift, and lateral displacements are induced under soft-soil conditions and zone five. Later, the building’s energy performance was predicted by integrating the Autodesk Green Building Studio (GBS) plugin with the Rivet software. Energy optimization is attained by adjusting the window-wall ratio (WWR), the percentage of window shades, the utilization of occupancy and daylight sensor appliances, and altering the building orientation at ± 15^° intervals. The maximum energy intensity of 1023 MJ/m²/year occurs at a − 90^° rotation relative to the true north whereas, the optimized orientation aligns precisely with the true north direction. The HVAC system consumes maximum electricity energy of 58.40% and others 45.2%, whereas, in contrast, HVAC becomes 3.3% and 96.7% for fuel energy, respectively.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 4","pages":"1565 - 1582"},"PeriodicalIF":0.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698487","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":"Predicting load distribution in tie beam-foundation systems using machine learning and nature-inspired optimization algorithms","authors":"Ahmad S. Alfraihat","doi":"10.1007/s42107-025-01287-x","DOIUrl":"10.1007/s42107-025-01287-x","url":null,"abstract":"<div><p>Accurate load sharing in tie beam foundations represents one of those challenges a structural engineer is bound to encounter regarding the transpiring infrastructure’s structural safety and reliability. Conventional approaches generally remain confined to empirical correlations alone, which are found unable to appropriately address high interaction among the individual properties of the soil and configuration and dimensions of the supporting units and environmental variations. The ML models used in this paper bridge these gaps by combining them with optimization algorithms, increasing the efficiency and accuracy of the predictive performance. Data Description The study dataset contained 21 feature variables representing characteristics related to soil, structural parameters, and environmental conditions. In contrast, one variable is the target, which accounts for the load distribution factor. Three machine learning models are developed for the analysis: Random Forest, Gradient Boosting, and ANNs. Furthermore, optimization algorithms such as SWO, FFOA, EVO, and SAO were implemented to select the features and optimize the hyperparameters to improve performance. The results illustrated that the performance of the models improved much after optimization; ANN outperformed others with the best accuracy of R2 = 0.941 with a minimum error metric of RMSE and MAE. Gradient Boosting and Random Forest also showed enhancements that again evidence the transformation after its optimization. Contributions are threefold: proposing two new optimization techniques and developing a robust predictive framework for the structural engineering domain. In this way, this work underlines the potential currently offered by the combination of ML and optimization in solving complex challenges in engineering. These results set the stage for further research by expanding data sets, considering advanced algorithms, and applying this framework within a wide range of geological contexts that will improve safety and efficiency for engineering practices.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 4","pages":"1787 - 1800"},"PeriodicalIF":0.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698488","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":"Prediction of compressive strength of concrete doped with waste plastic using machine learning-based advanced regularized regression models","authors":"Anish Kumar,&nbsp;Sameer Sen,&nbsp;Sanjeev Sinha,&nbsp;Bimal Kumar,&nbsp;Chaitanya Nidhi","doi":"10.1007/s42107-025-01280-4","DOIUrl":"10.1007/s42107-025-01280-4","url":null,"abstract":"<div><p>This study evaluates the performance of various machine learning based regression models, including Lasso, Ridge, Elastic Net, Non-Parametric, and Linear Regression, in predicting compressive strength (CS). The models were assessed using multiple performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R²), and others. CS trends highlight the influence of curing time and the fly ash-to-waste plastic ratio. Early curing periods (3 and 7 days) show lower strength, which improves significantly over longer durations (28, 56, and 90 days) due to hydration and pozzolanic reactions. Non-Parametric model consistently outperformed the parametric models, achieving the lowest MAE (2.791 during training and 2.436 during testing) and the highest R² value (0.859 in training and 0.863 in testing), highlighting its superior predictive capability. Monotonicity analysis of the Non-Parametric model revealed that CS exhibits a strictly decreasing relationship with % Waste Plastic (R² = 0.911) and a strictly increasing relationship with % Fly Ash (R² = 1), while the relationship with Curing Days follows a non-monotonic trend (R² = 0.6837). Sensitivity analysis further demonstrated that % Plastic Waste has the highest impact on CS (sensitivity = 0.583), followed by Curing Days (0.414), whereas % Fly Ash has a minimal effect (0.002). These findings suggest that Non-Parametric models are more effective in capturing complex relationships in CS prediction, offering valuable insights for optimizing concrete compositions.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 4","pages":"1723 - 1741"},"PeriodicalIF":0.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698437","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":"Lateral-torsional buckling behavior of castellated steel beams with sinusoidal web openings: a parametric study","authors":"Rohit Rajendra Kurlapkar,&nbsp;Varun Jadhav,&nbsp;Ajinkya Hasabe","doi":"10.1007/s42107-025-01268-0","DOIUrl":"10.1007/s42107-025-01268-0","url":null,"abstract":"<div><p>This study presents a comprehensive numerical investigation into the lateral-torsional buckling behavior of castellated steel beams with sinusoidal openings, emphasizing the interaction of critical geometric and material parameters. A detailed Finite Element model was developed to analyze seventy-two beams, with variations in span length, steel grades (250 MPa, 460 MPa, and 690 MPa), web slenderness, and flange slenderness. The results demonstrate that higher steel grades significantly enhance both the load-carrying capacity and buckling resistance, underscoring the importance of material selection in structural performance. Furthermore, optimizing geometric parameters, such as flange-web slenderness ratios, was found to improve overall stability, with short-span beams exhibiting superior resistance to buckling compared to their long-span counterparts. These findings provide valuable insights into the behavior and design of castellated beams, offering a framework for enhancing structural efficiency and reliability in engineering applications.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 4","pages":"1583 - 1594"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698691","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":"Seismic performance assessment of RC buildings under Turkey ground motions designed by Force Based Design and improved performance based plastic design method","authors":"Rohit Vyas,&nbsp;Bush Rc,&nbsp;Abdullah Ansari,&nbsp;Kaushik Gondaliya,&nbsp;Anoop I. Shirkol","doi":"10.1007/s42107-024-01255-x","DOIUrl":"10.1007/s42107-024-01255-x","url":null,"abstract":"<div><p>Performance-Based Plastic Design (PBPD) is a widely used method for improving the seismic performance of structures by allowing controlled nonlinear behavior. The method is based on energy balance principles and a predefined target drift. However, its reliance on lateral load calculations often results in reduced strength of structural members, particularly in low- to medium-rise buildings where beam sections frequently fail to meet safe design requirements. This limitation raises concerns about the safety and reliability of PBPD-designed structures. To address this issue, an improved PBPD method is proposed by incorporating the minimum reinforcement criteria for beams as specified in the Indian design code. A 10-story reinforced concrete special moment-resisting frame was designed using the improved PBPD method and compared with a frame designed using the conventional force-based design approach. Nonlinear Pushover Analysis and Nonlinear Time History Analysis under strong Turkish ground motions were performed to evaluate the seismic performance of both designs. The results indicate that the improved PBPD method significantly enhances the seismic performance of the structure. The maximum considered earthquake level performance point of the PBPD frame lies within the Collapse Prevention range, while its overall drift ratio is 19.82% lower than that of the Force Based Design (FBD) frame. Incremental Dynamic Analysis further shows that only one ground motion exceeded the target drift of 0.02 for the improved PBPD frame. Additionally, fragility analysis demonstrates that the probability of complete failure is reduced to 17.2% for the PBPD frame, indicating superior robustness and reliability compared to the FBD frame.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 3","pages":"1355 - 1371"},"PeriodicalIF":0.0,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638246","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 novel multi-method framework for 3D printed fiber-reinforced polymer concrete utilizing advance additive manufacturing techniques","authors":"Jayant M. Raut,&nbsp;Anjusha Pimpalshende,&nbsp;Mayuri A. Chandak,&nbsp;Tejas R. Patil,&nbsp;Latika Pinjarkar,&nbsp;Sruthi Nair","doi":"10.1007/s42107-025-01273-3","DOIUrl":"10.1007/s42107-025-01273-3","url":null,"abstract":"<div><p>Growing demands for customized, sustainable, and high-performance infrastructure urgently require innovative construction methodologies. Conventional methods for fiber-reinforced polymer concrete are not efficient in material usage, are inconsistent in their mechanical properties, and fail to satisfy the complex structural demands. The current methods of 3D printing are often affected by delamination of layers, bad alignment of fibers, and relatively high rates of defects, which adversely affect the structural integrity and efficiency of the printed components. To address these challenges, we propose a novel multi-method framework utilizing advance additive manufacturing techniques for 3D printed fiber-reinforced polymer concrete. Our research introduces four additional mechanisms: GCMME (Gradient-Controlled Deposition via Multi-Material Extrusion) for smooth material transitions with functional graded properties, DFAM (Directional Fiber Alignment Mechanism) for optimal reinforcement along stress trajectories, ARCS (Adaptive Rheology Control System) for viscosity modulation and self-healing capabilities, and AQA-PDM (AI-Based Quality Assurance and Predictive Defect Mitigation) for real-time defect detection and quality control. All the above-mentioned mechanisms can be used simultaneously to allow for the mass production of customised structural parts with outstanding mechanical properties. Significant results include tensile strength greater than 12 MPa, compressive strength greater than 50 MPa, enhanced flexural strength by about 35%, and the defects density of less than 0.5%. The material wastage is minimized by up to 25%. Moreover, self-healing efficiency in closure is more than 60% as well. This integrated method significantly enhances performance, accuracy, and sustainability in modular construction and thus provides a transforming solution for the infrastructure development process.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 4","pages":"1655 - 1668"},"PeriodicalIF":0.0,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698641","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 based prediction model for the compressive strength of fly ash reinforced concrete: an exploration of varying cement replacements and water-cement ratios","authors":"Rohit Kumar Mishra,&nbsp;Arun Kumar Mishra","doi":"10.1007/s42107-025-01266-2","DOIUrl":"10.1007/s42107-025-01266-2","url":null,"abstract":"<div><p>This research explores the effect of fly ash replacement (0–50%) and various water-cement (W/C) ratios on the compressive strength of concrete. Experiments were conducted to evaluate compressive strength at different curing times (7, 28, 90, and 120 days) and W/C ratios (0.35, 0.45, 0.50). The results indicate that fly ash replacement reduces early-age compressive strength, with 50% fly ash mixes achieving around 12 MPa at 7 days compared to over 30 MPa for 0% fly ash, due to slower pozzolanic reactions. SVM-RBF, Random Forest, XGBoost, and Linear Regression based prediction models of compressive strength were developed. The performance of models was assessed using key performance metrics like MAE, MSE, RMSE, MSLE, RMSLE, R², MAPE, Willmott’s Index of Agreement, Mielke &amp; Berry Index, and Legates &amp; McCabe’s Index alongside Taylor diagrams, which revealed that SVM-RBF was the most reliable model, providing the best accuracy in both training (R² = 0.991) and testing phases (R² = 0.958). Sensitivity analysis indicated that curing days and water-cement ratio were the most influential factors on compressive strength, with curing days showing the highest normalized sensitivity index. Monotonicity analysis revealed optimal ranges for fly ash (~ 20%) and W/C ratio (~ 0.40) for maximizing compressive strength, with strength diminishing beyond these values due to increased porosity. The findings underscore the significance of fly ash content and W/C ratio in optimizing concrete strength, with machine learning providing valuable insights into predicting and understanding the behavior of concrete mixtures.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 4","pages":"1543 - 1564"},"PeriodicalIF":0.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698596","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-based assessment of seismic performance in fiber-reinforced polymer (FRP) retrofitted bridges","authors":"Mu’taz Abuassi,&nbsp;Majdi Bisharah","doi":"10.1007/s42107-024-01217-3","DOIUrl":"10.1007/s42107-024-01217-3","url":null,"abstract":"<div><p>It proposes machine learning for the seismic performance estimation of FRP-retrofitted bridges using a Mayfly Optimization Algorithm (MOA) optimized Convolutional Neural Networks (CNN). A very impressive CNN model was proposed with an R² score of nearly 0.92 for Damage Index and 0.88 for Repair Costs by achieving RMSE values very low as 0.05 and 158.11, respectively. For the classification tasks, the model obtained an overall accuracy of 91% in predicting Failure Mode with a precision of 0.89, recall 0.87, and F1-score of 0.88. The most relevant factors for the prediction of the Damage Index, as determined from the feature importance analysis using SHAP, were PGA, Bridge Type, and FRP Layer Count. Pier Height and Span Length were critical features in determining which type of failure mode exists, such as Column Shear Failure. Model discriminative explainability techniques with LIME ratified such predictions' accuracy by improving the practicality of the model's handling in real engineering scenario applications.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 3","pages":"975 - 987"},"PeriodicalIF":0.0,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638571","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":"Comparative seismic analysis of symmetrical and asymmetrical G + 7 structures using STAAD.Pro: insights into performance and material efficiency","authors":"Esar Ahmad,&nbsp;Lizina Khatua,&nbsp;Krushna Chandra Sethi,&nbsp;Miguel Villagómez-Galindo,&nbsp;Abhishek Upadhyay,&nbsp;Kuldeep Pathak","doi":"10.1007/s42107-025-01262-6","DOIUrl":"10.1007/s42107-025-01262-6","url":null,"abstract":"<div><p>This study examines the seismic load effects on symmetrical and asymmetrical structures using STAAD.Pro, following the guidelines of IS 1893 (Part 1): 2016. The research compares the seismic performance of four different G + 7 structural configurations: two symmetrical (rectangular and cross plus) and two asymmetrical (T-shaped and U-shaped) buildings, all situated in seismic Zone 4 on soft soil. The analysis focuses on key parameters such as storey drift, displacement, base shear, and internal forces. Results indicate that symmetrical structures exhibit superior seismic performance, with lower storey drift, displacement, and base shear values compared to asymmetrical structures. Specifically, the rectangular symmetric model showed the least plate stress and the lowest storey displacement, demonstrating its resilience to seismic forces. Conversely, the U-shaped and T-shaped asymmetrical structures experienced higher stress and displacement, highlighting their vulnerability. Additionally, the study assessed the material efficiency of each configuration, revealing that symmetrical structures not only perform better but also require less concrete and steel, making them more cost-effective. Hypothesis testing also revealed that these differences are statistically significant, with symmetrical models, particularly the rectangular configuration, demonstrating enhanced seismic resilience. These findings emphasize the importance of symmetry in seismic design and offer practical insights for engineers and designers to optimize building configurations for enhanced earthquake resistance.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 4","pages":"1495 - 1509"},"PeriodicalIF":0.0,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698439","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":"Predictive modeling of compressive strength in glass powder blended pervious concrete","authors":"Navaratnarajah Sathiparan,&nbsp;Daniel Niruban Subramaniam","doi":"10.1007/s42107-024-01257-9","DOIUrl":"10.1007/s42107-024-01257-9","url":null,"abstract":"<div><p>Pervious concrete, known for its high porosity, is crucial in sustainable construction and effective stormwater management. This study explores the predicting compressive strength of glass powder blended pervious concrete. A comprehensive methodology was employed, utilizing machine learning algorithms - specifically Support Vector Regression (SVR), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGB) - to predict the compressive strength. The models were trained on a diverse dataset that included parameters such as water-to-cement ratio, binder content, and curing conditions. Results indicated that the SVR model outperformed others, achieving high predictive accuracy with minimal error margins. Additionally, sensitivity analysis underscored the significant impact of the curing period and admixture content on compressive strength. This research underscores the potential of using crushed glass powder in pervious concrete, promoting both enhanced performance and sustainability in modern construction practices.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 4","pages":"1449 - 1464"},"PeriodicalIF":0.0,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698438","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}