Asian Journal of Civil Engineering最新文献

{"title":"Sensitivity-based prediction of self-compacting concrete strength using hybrid modeling techniques","authors":"Sham Hara Mohammed,&nbsp;Lalan Barzan Hussein,&nbsp;Ahmed Salih Mohammed","doi":"10.1007/s42107-025-01383-y","DOIUrl":"10.1007/s42107-025-01383-y","url":null,"abstract":"<div><p>Concrete is the most extensively used construction material worldwide due to its strength and durability. Concrete requires proper compaction to perform effectively. The compaction process requires skilled workers, extra time, and vibrators to minimize voids in the concrete and achieve the necessary strength and durability. Using self-compacting concrete (SCC) instead of normal concrete results in higher compressive strength (CS) and durability. Self-compacting concrete significantly benefits the construction process, including cost and time reduction, minimized labor, and improved overall performance. However, evaluating the compressive strength is crucial to ensure the SSC’s durability. Based on a sensitivity analysis chart in this study, some factors significantly influence the CS, such as age, coarse aggregate, and fly ash. This study investigates the effect of those factors on the CS of SCC by applying several modeling techniques for 123 different mixtures. The predictive modeling techniques used to predict the CS of SCC include Linear Regression, Non-Linear Regression, Multi-Linear Regression, Logarithmic, Pure Quadratic, Artificial Neural Network, and the M5P-tree. The independent variables in the obtained dataset are Cement with its value ranging between 141.5–530 kg/m³, Limestone 0–200 kg/m³, Fly Ash 0–275 kg/m³, Fine Aggregate 464.4–1014 kg/m³, Coarse Aggregate 480–957 kg/m³, Water 49.53–252 kg/m³, Super Plasticize 0.30–4.70 %, Fiber 0–80 kg/m³, and Age 1–56 days. The value of CS, which is a dependent variable in this study, is between 19.8 and 75.2 MPa. Among the models that have been evaluated, the Artificial Neural Network (ANN) model demonstrated the highest accuracy in predicting compressive strength, with superior results across all evaluation criteria. Also, the Multi-Linear Regression model (MLR) showed a high performance. The evaluation of residual error confirmed that the ANN model provided the smallest error compared to the other models. This study's findings highlight the models that accurately predicted the CS of SCC, along with the factors that had the most tremendous impact on the CS of SCC. </p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 8","pages":"3485 - 3506"},"PeriodicalIF":0.0,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145163233","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 ANN, MLR, and NLR models for Compressive strength performance in fly ash based self compacting concrete","authors":"Monali Wagh,&nbsp;Sujin George,&nbsp;Sameer Algburi,&nbsp;Charuta Waghmare,&nbsp;Tripti Gupta,&nbsp;Amruta Yadav,&nbsp;Salah J. Mohammed,&nbsp;Ali Majdi","doi":"10.1007/s42107-025-01385-w","DOIUrl":"10.1007/s42107-025-01385-w","url":null,"abstract":"<div><p>Self-compacting concrete (SCC) blended with fly ash (FA) presents a promising low-carbon alternative to traditional concrete, enhancing both workability and long-term durability. Yet, the prediction of its compressive strength (CS) remains challenging due to complex mix interactions. This study presents a comparative modeling framework using Multi-Linear Regression (MLR), Nonlinear Regression (NLR), and Artificial Neural Networks (ANN) to estimate the CS of FA-modified SCC based on key input variables: cement (C), water-to-binder ratio (w/b), fly ash content (FA), sand (S), coarse aggregate (CA), and superplasticizer (SPA dataset of 270 mixes was statistically analyzed, divided into 70% training and 30% testing subsets, and validated using R², RMSE, and MAE. The results revealed that the ANN model outperformed both NLR and MLR, achieving superior accuracy (R² = 0.95, RMSE = 3.49 MPa, MAE = 2.45 MPa) and consistent residual behavior within (± 20%) tolerance bands. In contrast, the NLR and MLR models exhibited broader error ranges and lower predictive reliability. The ANN’s adaptability to nonlinear, multivariate Furthermore, residual error analysis and model robustness across low, medium, and high-strength ranges were evaluated. These findings demonstrate the usefulness of data driven advanced models to resolve the complexities in the modern cementitious materials and thus serve as scientific basis for the improvement of the design of SCC with high performance and high eco-efficiency.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 8","pages":"3519 - 3532"},"PeriodicalIF":0.0,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145163235","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":"Hybrid quantum inspired multi-objective optimization framework for self-healing concrete using AI-driven metaheuristics","authors":"Aarti Karandikar,&nbsp;Ashwini V. Zadgaonkar,&nbsp;Rohit Pawar,&nbsp;Ashwini C. Gote,&nbsp;Tejas R. Patil,&nbsp;Haytham F. Isleem","doi":"10.1007/s42107-025-01382-z","DOIUrl":"10.1007/s42107-025-01382-z","url":null,"abstract":"<div><p>Designing a self-healing concrete that is going to be sustainable, self-sufficient in costs, and most importantly durable and strong throughout its desired lifecycle is the only solution to an ever-increasing complex set of infrastructure demands coupled with environmental constraints. These concrete mixture designs, involving complex, non-linear, multi-objective nature, often face optimization techniques of existing methods. Such traditional metaheuristics, though very useful, are not adaptable, slow in convergence, and not efficient in exploring large solution spaces under stringent performance constraints. This work presents a hybrid AI-quantum inspired multi-objective optimization framework for self-healing concrete design to deal with those challenges. The model integrates four developed computational techniques: (1) Quantum Inspired Differential Evolution with Adaptive Learning Mechanism (QIDE-ALM), improving exploration–exploitation balance using quantum bit-flipping and adaptive feedback; (2) Quantum-Accelerated Multi-Objective Particle Swarm Optimization (Q-MOPSO) that uses quantum tunneling to escape local optima and to speed up convergence; (3) Quantum-Driven Surrogate Modeling which uses quantum support vector machine and quantum neural network to reduce the computational burden on fast performance outcome prediction; and (4) Quantum Inspired Neural Networks for Multi-Objective Optimization (QINN-MO), dynamically learning complex relationships among mixture components by quantum Inspired weight modulation and architecture adaptations. Iterative implementation of this integrated model combines global searching with quick convergence and assessment, in conjunction with intelligent learning, generating Pareto-optimal concrete designs. The initial results show a tremendous improvement in performances: compressive strength of 50–55 MPa, healing efficiency in the range of 90–95%, and lifecycle cost reduction of up to 20%. This framework is expected to prove potent, scalable, and computationally efficient in advancing concrete technology, thus entirely revolutionizing practices in civil infrastructure through intelligent process engineering of quantum-enhanced materials. </p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 8","pages":"3471 - 3483"},"PeriodicalIF":0.0,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145163236","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":"Risk-integrated scheduling for commercial building construction: a BIM and Monte Carlo simulation approach","authors":"Shobhit Chaturvedi,&nbsp;Dev Gheewala,&nbsp;Sanket Vegad,&nbsp;Elangovan Rajasekar,&nbsp;Debasis Sarkar","doi":"10.1007/s42107-025-01387-8","DOIUrl":"10.1007/s42107-025-01387-8","url":null,"abstract":"<div><p>This study presents a risk-integrated scheduling framework for commercial building projects by incorporating Building Information Modelling (BIM), Monte Carlo simulations, and risk analysis using Autodesk Revit, Primavera P6 and Risk Analyzer software. The six-step methodology, comprising deterministic scheduling, uncertainty integration, risk assessment, mitigation, and sensitivity analysis—was applied to systematically evaluate project risk and uncertainties. The BIM modelling process enabled 3D visualization, precise quantity estimation, and structured sequencing, establishing a baseline project duration of 391 days and a cost of ₹51.6 million. Monte Carlo simulations incorporating activity uncertainties indicated a 3.1% increase in the mean project duration to 411 days, with the maximum extending by 15.85% to 453 days, while project costs fluctuated, with the mean rising by 5.05% to ₹54.2 million and the maximum reaching ₹56.9 million. When risk factors such as labour shortages, extreme weather, and regulatory delays were included, the mean duration surged by 61.13% to 630 days, and the maximum increased by 87.21% to 732 days, with costs escalating to a mean of ₹61.8 million (19.85% increase) and a maximum of ₹66.4 million (28.65% increase). Sensitivity analysis identified excavation and structural works as critical contributors to delays and cost overruns. Implementing structured mitigation strategies with a ₹0.6 million budget resulted in mean cost savings of ₹6.28 million (10.14%) and maximum savings of ₹6.98 million (11.28%), while reducing the mean project duration by 21.75% to 493 days. These findings highlight the effectiveness of integrating BIM and Monte Carlo simulations in improving risk-informed decision-making, optimizing resource allocation, and enhancing project resilience.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 8","pages":"3553 - 3571"},"PeriodicalIF":0.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161841","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 and validation of compressive strength of metakaolin-based mortars using machine learning","authors":"Eugenia Naranjo,&nbsp;Nestor Ulloa,&nbsp;Kerly Mishell Vaca Vallejo,&nbsp;Rómulo Rivera,&nbsp;Félix García,&nbsp;Miguel Pérez,&nbsp;Byron Gabriel Vaca Vallejo","doi":"10.1007/s42107-025-01380-1","DOIUrl":"10.1007/s42107-025-01380-1","url":null,"abstract":"<div><p>Metakaolin (MK)-based cement mortar plays a crucial role in the development of sustainable concrete structures due to its several environmental and performance benefits. It promotes sustainable concrete structures by improving durability, reducing environmental impacts, enhancing material efficiency, and supporting the circular economy in construction.In this research, a comparative study between eight ML classification techniques such as GB, CN2, NB, SVM, SGD, KNN, Tree and RF and one symbolic regression technique such as the RSM has been presented to estimate thecompressive strength of meta-kaolin-based mortarconsidering mixture components contents and its age. A total of 424 records were collected from literature for compressive strength for different mixing ratios of metakaolin-based mortarsat different ages and divided into training set (318 records = 75%) and validation set (106 records = 25%). At the end of the model protocol, SVM andKNN models showed an excellent accuracy of about 92%, while Tree and GB models showed very good accuracies of about 90%. Also, RF and CN2 models showed good accuracy level of about 76–88% and finally NB and SGD produced unacceptable accuracy of less than 60%. Both the correlation matrix and sensitivity analysis results indicated that Age, W/B, and MK/B are the most influential inputs with relative importance of 25% each, then B/S with relative importance of 15%, and SPand Fcem with relative importance of 7% each.Conversely, the RSM model with only two trees and four levels which increased up to four trees and eight levels produced an F value of 32.64, P values less than 0.0500, R² of 0.9422 and Adeq Precision of 31.678. This provides a robust framework for optimizing the mix design. The high R² indicates that the model explains 94.22% of the variance in the MK-based cement mortar compressive strength, making it highly reliable for predicting concrete performance. </p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 8","pages":"3423 - 3451"},"PeriodicalIF":0.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145162207","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 retrofitting through multi-objective optimization: a time–cost–energy framework using opposition-based NSGA-III","authors":"Batchu P. R. V. S. Priyatham,&nbsp;Krushna Chandra Sethi","doi":"10.1007/s42107-025-01359-y","DOIUrl":"10.1007/s42107-025-01359-y","url":null,"abstract":"<div><p>Retrofitting projects are critical for enhancing the functionality, safety, and sustainability of existing infrastructures. However, selecting appropriate retrofitting strategies involves complex trade-offs between time, cost, and energy consumption. This study presents a comprehensive time–cost-energy consumption trade-off (TCECT) optimization model using the opposition-based non-dominated sorting genetic algorithm III (OBNSGA-III). The model considers multiple retrofitting aspects, each having several execution options defined by distinct time durations, costs, and energy usage. The objective is to simultaneously minimize project completion time, overall cost, and energy consumption while considering precedence relationships and limited discrete resources. OBNSGA-III integrates opposition-based learning (OBL) into the NSGA-III framework for improved initialization and generation jumping, enhancing convergence and diversity of solutions. A real-world case study with eleven retrofitting aspects demonstrates the model’s practical applicability. The results yielded 22 Pareto-optimal solutions, allowing decision-makers to select balanced trade-offs based on project priorities. Compared to other optimization models such as NSGA-III, MOPSO, and OB-MODE, the proposed approach shows superior performance across key indicators including hypervolume, generational distance, and solution spread. The findings affirm the model’s effectiveness in aiding sustainable, cost-efficient, and timely execution of retrofitting projects. This research contributes to the advancement of decision-support systems for infrastructure project planning and optimization.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 8","pages":"3181 - 3195"},"PeriodicalIF":0.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145162205","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 comparison of symmetrical and asymmetrical low-, mid-, and high-rise Rc structures: a height-based evaluation of structural behavior and material efficiency","authors":"Kuldeep Pathak,&nbsp;Rakesh Gupta","doi":"10.1007/s42107-025-01381-0","DOIUrl":"10.1007/s42107-025-01381-0","url":null,"abstract":"<div><p>This study investigates the seismic performance of twelve reinforced concrete (RC) building configurations—categorized as symmetrical and asymmetrical forms across low-, mid-, and high-rise heights—using STAAD.Pro simulation in compliance with IS 1893:2016. The models include rectangular, cross-plus (symmetrical), T-shape, and U-shape (asymmetrical) plans, each evaluated under consistent material and geometric parameters. Key seismic performance indicators such as natural time period, base shear, maximum storey drift, lateral displacement, plate stress, and structural material usage were analyzed. The study reveals that asymmetrical buildings consistently exhibit higher drift, displacement, and internal forces compared to their symmetrical counterparts, especially in higher-rise configurations. A comprehensive material efficiency evaluation also indicates that asymmetrical structures consume significantly more concrete and steel to meet stability requirements. Statistical validation through two-sample t-tests confirms that these performance differences are significant (<i>p</i> &lt; 0.05). Additionally, consolidated performance trends across heights highlight a nonlinear escalation of seismic demands with building height and asymmetry. The findings emphasize the necessity of incorporating symmetry and compact plan geometries into seismic design to enhance performance and material efficiency. This research contributes practical insights for structural engineers and urban planners in optimizing RC building configurations for earthquake-prone regions.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 8","pages":"3453 - 3470"},"PeriodicalIF":0.0,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161563","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 boosting models for predicting flexural strength of steel fiber reinforced concrete","authors":"M. Sudheer,&nbsp;B. D. V. Chandra Mohan Rao","doi":"10.1007/s42107-025-01384-x","DOIUrl":"10.1007/s42107-025-01384-x","url":null,"abstract":"<div><p>Steel Fibre Reinforced Concrete (SFRC) is a composite material that exhibits increased toughness, crack resistance and post-cracking behavior as a result of steel fibers. However, compared to regular concrete, the development of strength prediction algorithms for SFRC is still in its infancy because of its complexity and the lack of available data. Flexural strength is an important parameter in the structural durability of SFRC, especially in pavements, tunnel linings and precast structures. The performance of two Machine Learning methods, Extreme Gradient Boosting (XGBoost) and Gradient Boosting Machine (GBM) is investigated in this research work to predict the flexural strength of steel fiber-reinforced concrete. Machine learning has been demonstrated to be a useful tool in civil engineering to simulate the complex, nonlinear behavior of materials such as SFRC. To investigate this capability, a database containing ninety two experimental observations compiled from the literature on the flexural strength of SFRC was used for model training and testing. Gradient Boosting algorithm predicts the flexural strength of SFRC with R² score and RMSE values of 0.992 and 0.242 for training data and 0.941 and 0.851 for testing data respectively. Extreme Gradient Boosting algorithm predicts the flexural strength of SFRC with R² score and RMSE values of 0.993 and 0.239 for training data and 0.933 and 0.902 for testing data respectively. The findings indicated that both GBM and XGBoost had high predictive accuracy.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 8","pages":"3507 - 3517"},"PeriodicalIF":0.0,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161432","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-driven XGBoost model with metaheuristic algorithms for assessing compressive strength of high-performance concrete","authors":"Amit Kumar Rai,&nbsp;Shiv Shankar Kumar","doi":"10.1007/s42107-025-01379-8","DOIUrl":"10.1007/s42107-025-01379-8","url":null,"abstract":"<div><p>Compressive strength (CS) is a key property of concrete mix, but the determination of CS requires costly and time intensive experimental procedures. Leveraging machine learning (ML) techniques for CS prediction can enhance accuracy and reliability while reducing the extensive need for laboratory testing.This study utilizes high performance concrete mix data set and employs the Extreme Gradient Boosting (XGBoost) ML model coupled with six metaheuristic optimization techniques such as genetic algorithm (GA), Grey Wolf Optimization (GWO), Beetle Antennae Search (BAS), Bayesian Optimization (BO), Particle Swarm Optimization (PSO) and Optina to fine tune its hyperparameters. The objective of the present work is to enhance the performance of ML model while ensuring robust generalization and computational efficiency. The performance of the tuned XGBoost models was assessed using evaluation metrics such as the coefficient of determination (<span>(hbox {R}^{2})</span>), root mean square error (RMSE), and the time taken for optimization (in seconds). Among these hybrid models XGBoost-Optuna emerged as the best performing model while achieving the highest accuracy with testing <span>(hbox {R}^{2})</span> (0.9345), minimal overfitting and the fastest optimization time (61.32 s). However, XGBoost-GWO demonstrated comparable accuracy for testing <span>(hbox {R}^{2})</span> of 0.9344 and generalization capability but required significantly higher optimization time (2821.25 s). XGBoost-Bayesian performed best against overfitting of the model but had a lower <span>(hbox {R}^{2})</span> value of 0.9260 compared to other models. XGBoost-BAS offered a balanced trade off between accuracy and optimization time but did not outperform machine learning model optimized by Optuna.Overall, XGBoost-Optuna proved to be the most optimal choice by offering an excellent balance of accuracy, generalization, and computational efficiency which makes it a robust solution for predictive modeling in concrete mix design. </p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 8","pages":"3401 - 3421"},"PeriodicalIF":0.0,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161561","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 new non-conforming finite element for free vibration analysis of thin plates with and without cutouts: ABAQUS implementation using the UEL subroutine","authors":"Lazhar Derradji,&nbsp;Toufik Maalem,&nbsp;Tarek Merzouki","doi":"10.1007/s42107-025-01376-x","DOIUrl":"10.1007/s42107-025-01376-x","url":null,"abstract":"<div><p>This paper presents a novel strain-based finite element (NSBPE4K) developed for the free vibration analysis of thin plates, both with and without cutouts. The element incorporates three primary degrees-of-freedom per node: a transverse displacement (w) and two normal rotations (θx, θy) about the x and y axes, respectively. The displacement field is formulated based on assumed functions for the strain components, ensuring the compatibility equations are satisfied. The non-conforming element was successfully implemented in the ABAQUS software using the UEL subroutine (user element). Free vibration analysis results demonstrate the exceptional efficiency and accuracy of the new element. The results obtained with the present element excel those obtained with standard ABAQUS elements and other non-conforming elements found in the literature. This superiority is noticeable in free vibration scenarios, demonstrating the effectiveness of the proposed finite element for accurate and reliable simulation of the vibrational behavior of thin plates.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 8","pages":"3341 - 3352"},"PeriodicalIF":0.0,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145171903","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}