Asian Journal of Civil Engineering最新文献

{"title":"An explainable machine learning model for encompassing the mechanical strength of polymer-modified concrete","authors":"Md. Habibur Rahman Sobuz,&nbsp;Mita Khatun,&nbsp;Md. Kawsarul Islam Kabbo,&nbsp;Norsuzailina Mohamed Sutan","doi":"10.1007/s42107-024-01230-6","DOIUrl":"10.1007/s42107-024-01230-6","url":null,"abstract":"<div><p>Polymer-modified concrete (PMC) is an advanced building material with more excellent durability, tensile strength, adhesion, and lesser susceptibility to chemical degradation. Recent developments in machine learning (ML) have shown that prediction of compressive strength (CS) of PMC key input factors needed to obtain an optimized mix design are among the areas of applicability of ML. This study used eight machine learning models, which are Decision Tree, Support Vector Machine, K-Nearest Neighbors, Bagging Regression, XG-Boost, Ada-Boost, Linear Regression, Gradient Boosting to predict compressive strength and perform SHAP (Shapley additive explanation) analysis. These hybrid predictive PMC models were developed using a wide-ranging dataset of 382 experimental data points compiled from the literature. A SHAP interaction plot was also used to show how each feature affected predictions on the model outputs. As highlighted in the results, hybrid models had significantly higher performance than conventional models, and the XG-Boost and decision tree model had the highest accuracy. In particular, the XG-Boost and decision tree model reached R² scores of 0.987 for training and 0.577 for testing, proving its remarkable prediction ability for PMC compressive strength. The SHAP analysis confirmed that coarse aggregate, cement, and SCMs had the most significant influence on CS, with all other variables contributing lower values. The Partial Dependence Plots (PDP) analysis allowed a relatively simple interpretation of the contribution of individual inputs to the CS predictions. These results are useful for construction purposes and provide engineers and builders with first-hand knowledge and insight into the importance of individual components on PMC development and performance.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 2","pages":"931 - 954"},"PeriodicalIF":0.0,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142995807","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":"Enhancing earthquake resilience: a review of friction pendulum seismic isolation techniques","authors":"Yao Domadzra,&nbsp;Mohit Bhandari,&nbsp;Murtaza Hasan","doi":"10.1007/s42107-024-01231-5","DOIUrl":"10.1007/s42107-024-01231-5","url":null,"abstract":"<div><p>Modern earthquake-resistant design has been revolutionized by seismic base isolation technologies, which provide cutting-edge ways to mitigate the destructive power of earthquakes on buildings. This paper delves into the technical aspects and benefits of several friction pendulum systems, including single, double, triple, and quintuple systems. It emphasizes how these systems effectively reduce seismic forces, dissipate energy, and enhance building performance in earthquakes. It takes a look at the novel base isolators' design considerations, force–displacement relationships, and advances in the modeling and design of these systems. The study's overarching goal is to synthesize the existing knowledge about friction pendulum systems in order to aid scientists, engineers, and politicians working on earthquake control building codes. This paper contributes to the ongoing efforts to strengthen structural resilience against seismic events by studying developments, temperature implications, problems, and research gaps in seismic base isolation. Significant reductions in building response, such as acceleration, inter-storey drift ratios, and base shear, has been seen with the adoption of friction pendulum systems.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 3","pages":"989 - 1007"},"PeriodicalIF":0.0,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638459","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":"Enhanced diagnostic approach for multiple damage detection and severity evaluation through EMI-based sensing and artificial neural network model","authors":"Maheshwari Sonker,&nbsp;Rama Shanker","doi":"10.1007/s42107-024-01220-8","DOIUrl":"10.1007/s42107-024-01220-8","url":null,"abstract":"<div><p>Detecting and quantifying multiple damages in structures remains a significant challenge in structural health monitoring (SHM), particularly in complex civil engineering systems. This study presents an experimental approach for the detection of multiple damages and their severity using the Electromechanical Impedance (EMI) technique. The EMI method, which utilizes piezoelectric transducers, offers a sensitive and reliable means to monitor structural integrity by measuring the coupled mechanical and electrical response of structures under various damage conditions. In this research, multiple damage scenarios were simulated in concrete specimens, and the corresponding conductance signatures were recorded. Particularly shifts in conductance values were analyzed to identify and localize damages. Conventional statistical metrics such as root-mean square deviation, correlation coefficient, mean absolute percentage deviation are employed to quantify the changes in conductance signature. Additionally, a methodology for localizing the damage is presented. Additionally, a severity index based on impedance variations was developed to quantify the extent of damage. The experimental results demonstrate the effectiveness of the EMI technique in accurately detecting, locating, and assessing the severity of multiple damages in complex structural systems. Further machine learning approach viz. artificial neural network model was applied to predict the damages. The data trained an artificial neural network model, which found suitable for predicting multiple damages levels. This approach contributes to enhanced safety and reliability in structural health monitoring (SHM) and sustainable building practices by offering a scalable and sustainable approach for real-time durability assessment, performance of concrete structures, contributing to a more sustainable development.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 2","pages":"747 - 760"},"PeriodicalIF":0.0,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142995650","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":"Enhanced prediction of compressive strength in high-strength concrete using a hybrid adaptive boosting - particle swarm optimization","authors":"Duy-Liem Nguyen,&nbsp;Tan-Duy Phan","doi":"10.1007/s42107-024-01233-3","DOIUrl":"10.1007/s42107-024-01233-3","url":null,"abstract":"<div><p>This article accurately predicts the compressive strength of high-strength concrete (HSC) using the proposed hybrid Adaptive Boosting - Particle Swarm Optimization (AB-PSO) model. A dataset consisting of 413 experimentally tested data points, collected from published studies, was used to train and test the hybrid AB-PSO model. The input variables considered were cement (C), fly ash (F), water (W), fine aggregate (S), coarse aggregate (CO), and superplasticizer (SP), with compressive strength as the output prediction. The performance of the hybrid AB-PSO model was evaluated using various statistical coefficients, including R² (coefficient of determination), MSE (mean squared error), MAE (mean absolute error), and RMSE (root mean squared error). A 10-fold cross-validation method was also employed to assess its accuracy. The results demonstrated that the hybrid AB-PSO model achieved high accuracy, with R² values exceeding 0.88 during training and 0.91 during testing. The hybrid AB-PSO model outperformed the default AB paradigm for predicting HSC compressive strength, improving the R² value by 1.03 times. Furthermore, Shapley Additive Explanations (SHAP) and two-way partial dependence plots (PDP-2D) were used to explore the key factors influencing HSC compressive strength. It was found that cement and superplasticizer significantly affected the compressive strength predictions. Finally, an optimal design strategy for achieving the best compressive strength of HSC was analyzed and discussed.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 3","pages":"1059 - 1076"},"PeriodicalIF":0.0,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638460","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":"On the accuracy of CEL blast simulations: validation and application","authors":"Assal Hussein,&nbsp;Paul Heyliger","doi":"10.1007/s42107-024-01226-2","DOIUrl":"10.1007/s42107-024-01226-2","url":null,"abstract":"<div><p>The coupled Eulerian–Lagrangian (CEL) method has shown good capability to simulate large deformation behavior in the blast response of complex structural and multi-physics systems. However, the published literature has not addressed blast wave characteristics in free open-air space or directly compared the results of such studies with experiments. In this study, the authors performed three-dimensional (3-D) non-linear finite element (FE) analysis of CEL model utilizing ABAQUS/Explicit finite element software to estimate blast wave parameters, peak overpressure (<i>P</i>_<i>so</i>), time of arrival (<i>t</i>_<i>a</i>), positive phase duration (<span>({t}_{o}^{+})</span>), and blast shock wave front velocity (<i>U</i>) in comparison to empirical Kingery–Bulmash free air-blast predictions and recently published small-scale blast field test results. The height of spherical and cubical TNT charges (HOE) is 5.0-m inside a Eulerain domain (ED). The air and trinitrotoluene (TNT) charge are modeled using (C3D8R) continuum solid elements and the Eulerian domain is modeled as a volume element using (EC3D8R). The CEL model results show good agreement with Kingery–Bulmash predictions and experimental data for incident peak-overpressure, time of arrival, and blast shock wave velocity of considered scaled distances. However, the CEL model outcomes of positive phase duration showed a difference from Kingery–Bulmash model as high as 55% due to secondary shock waves moving inward and reflected toward the source of burst. Despite the extensive validation of the Kingery–Bulmash empirical model, direct measurements in open-space indicate that incorporating blast wave propagation phenomena is critical in different explosion scenarios, especially when reflection phenomena are probable. As a practical model of the CEL model, the blast response and damage evolution of a X70 steel pipe subjected to contact pipe bomb charge is investigated. This grade of steel pipe is a reliable material and used in oil and gas transmission pipelines. The post-damage simulation showed wall thickness has significant contribution to improve the response of the pipe and blast-post damage evolution. This study aims to highlight the efficiency of coupled Eulerian–Lagrangian (CEL) technique to simulate blast for a better understanding of wave propagation in free space and wave-structure interaction phenomena when blast waves interact with structures.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 2","pages":"843 - 866"},"PeriodicalIF":0.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142995826","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":"Integrating machine learning in digital architecture: enhancing sustainable design and energy efficiency in urban environments","authors":"Ma’in F. Abu-Shaikha,&nbsp;Mutasem A. Al-Karablieh,&nbsp;Akram M. Musa,&nbsp;Maryam I. Almashayikh,&nbsp;Razan Y. Al-Abed","doi":"10.1007/s42107-024-01224-4","DOIUrl":"10.1007/s42107-024-01224-4","url":null,"abstract":"<div><p>The following work applies metaheuristic optimization algorithms—PSO, ACO, Genetic Algorithm, and Enhanced Colliding Bodies Optimization (ECBO)—to the optimum design of a sustainable building with respect to prominent metrics such as energy savings, improvement in indoor comfort, and reduction in carbon footprint. These algorithms are applied to a wide dataset that includes variable intensity factors such as window-to-wall variation ratio, HVAC efficiency, and integration of renewable energy. Results also proved that PSO is the fittest strategy to balance energy efficiency and sustainability, with the highest energy savings of 24.1%. Besides, PSO wasn’t just the fastest convergence rate; it also obtained a Platinum LEED certification. ACO was second in order of magnitude, with high energy savings and carbon footprint reduction values, and also obtained the Platinum LEED certificate. The results obtained for GA were positive from the occupant comfort point of view but were slower in terms of energy savings and convergence speed. In contrast, ECBO had the slowest convergence and lowest energy savings, demonstrating the limitation of the application of ECBO for large-scale multi-objective optimization. These results imply that PSO and ACO would be suitable for practical applications linked to urban sustainable design, while GA and ECBO are more suited for niche applications. The obtained results can provide useful guidelines in developing more energy-efficient and sustainable designs for architects, urban planners, and policymakers.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 2","pages":"813 - 827"},"PeriodicalIF":0.0,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142995710","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":"Modeling compressive strength and environmental impact points of fly ash-admixed concrete using data-driven approaches","authors":"Sandeep Singh,&nbsp;Y. R. Meena,&nbsp;Srinivasa Rao Rapeti,&nbsp;Navin Kedia,&nbsp;Salman Khalaf Issa,&nbsp;Haider M. Abbas","doi":"10.1007/s42107-024-01223-5","DOIUrl":"10.1007/s42107-024-01223-5","url":null,"abstract":"<div><p>This study examined the capability of white-box machine learning methods in the intelligent design of concrete technology. Therefore, three data-driven methods, multivariate adaptive regression splines (MARS), gene expression programming (GEP), and group method of data handling (GMDH) approaches, were adopted to model the compressive strength (CS) and environmental impact points (P) of fly ash admixture concrete. The main feature of the proposed methods is that they provide formulas for predicting CS and P. The study's findings indicated the acceptable performance of the suggested methods in concrete technology. In general, the MARS approach for the estimation of CS is more acute than the GMDH and GEP approaches. In addition, MARS had results similar to those of the evolutionary polynomial regression (EPR) model generated in the earlier research to predict CS. Moreover, the MARS model performs slightly better than EPR for predicting P. It is noteworthy that MARS presented more straightforward equations than EPR for predicting CS and P. Sensitivity analysis indicated a more effective parameter on CS and P. The accuracy of the developed models was assessed through statistical parameters and scatter, Taylor, and Violin plots. The presented predictive models can have practical applications in the construction of buildings.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 2","pages":"795 - 811"},"PeriodicalIF":0.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142995002","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":"Using machine learning approaches for predicting the compressive strength of ultra-high-performance concrete with SHAP analysis","authors":"Suhaib Rasool Wani,&nbsp;Manju Suthar","doi":"10.1007/s42107-024-01195-6","DOIUrl":"10.1007/s42107-024-01195-6","url":null,"abstract":"<div><p>Ultra-high-performance concrete (UHPC) is a special construction material that is renowned for its exceptional mechanical properties and durability. Machine Learning (ML) methodologies have become essential methods for predicting the compressive strength (CS) of UHPC and identifying critical independent variables that are essential for optimal mix design. This study predicted the CS of UHPC using three ML models: artificial neural networks, M5P, and random forest. Various measures, including R, RMSE, RAE, MAE, and RRSE were employed to assess the performance of these models. A total of 810 observations were gathered, with 80% designated for training and the remaining 20% for testing. The RF model demonstrated superior performance compared to the other models, attaining a testing phase R-value of 0.98, MAE of 6.35, RMSE of 8.49, RRSE of 21.90%, and RAE of 21.21% through statistical analysis. The findings indicate that the variable “age” exerted the most significant influence on the model’s dependent variable, evidenced by a SHAP value of 23.06. ML techniques provide substantial benefits to the construction sector by facilitating faster and more accurate evaluations of material attributes.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 1","pages":"373 - 388"},"PeriodicalIF":0.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906047","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":"Optimizing time, cost, environmental impact, and client satisfaction in sustainable construction projects using LHS-NSGA-III: a multi-objective approach","authors":"Amir Prasad Behera,&nbsp;Amit Dhawan,&nbsp;V. Rathinakumar,&nbsp;Manish Bharadwaj,&nbsp;Jay Singh Rajput,&nbsp;Krushna Chandra Sethi","doi":"10.1007/s42107-024-01221-7","DOIUrl":"10.1007/s42107-024-01221-7","url":null,"abstract":"<div><p>This paper introduces a comprehensive multi-objective optimization model for sustainable construction projects, targeting the minimization of project duration, construction cost, environmental impact, and the maximization of client satisfaction. The proposed approach combines Latin Hypercube Sampling (LHS) with the Non-dominated Sorting Genetic Algorithm III (NSGA-III) to form the LHS-NSGA-III framework. This model addresses the growing need for sustainable construction by balancing key trade-offs between time, cost, environmental impact, and client satisfaction. The optimization process begins with LHS, ensuring a diverse and well-distributed initial population, followed by genetic operations such as crossover and mutation to maintain diversity across generations. The model evaluates multiple execution modes for project activities, each with associated resource utilizations that affect time, cost, environmental impact, and satisfaction outcomes. A case study of a one-story building project with 21 activities and five execution modes per activity demonstrates the applicability of the model. The results showcase a set of Pareto-optimal solutions, providing decision-makers with balanced trade-offs across objectives. This LHS-NSGA-III model offers an effective approach for optimizing sustainable construction projects, helping managers achieve efficiency, cost-effectiveness, and higher client satisfaction while minimizing environmental impact.</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 2","pages":"761 - 776"},"PeriodicalIF":0.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142994317","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":"Development and performance evaluation of high-performance concrete enhanced with styrene-butadiene rubber","authors":"Anirudh Sharma,&nbsp;Ram Vilas Meena","doi":"10.1007/s42107-024-01222-6","DOIUrl":"10.1007/s42107-024-01222-6","url":null,"abstract":"<div><p>This study investigates the development and performance assessment of high-performance concrete (HPC) incorporating styrene-butadiene rubber (SBR) latex and silica fume as a partial replacement for cement. Eight concrete mixes were designed with varying SBR latex contents (0–7%) and a constant 10% replacement of cement with silica fume. The concrete mixes were evaluated based on their fresh properties, mechanical strengths, durability, and microstructural characteristics. The results indicate that the inclusion of SBR latex significantly enhances the flexural and tensile strengths, thermal resistance, and durability of the concrete, while the addition of silica fume improves compressive strength and impermeability. Mix 4, containing 3% SBR and 10% silica fume, demonstrated the best overall performance, achieving optimal mechanical properties, including improved flexural and tensile strengths, and enhanced durability under aggressive environmental conditions. Durability tests, including permeability, chemical attack resistance, and freeze–thaw cycles, showed substantial improvements in Mix 4 compared to the control mix. Microstructural analysis using SEM, TGA, and XRD revealed a denser interfacial transition zone (ITZ) and reduced porosity in the SBR-modified concrete, contributing to its superior performance. However, at higher SBR contents, a marginal reduction in compressive strength and workability was observed.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 2","pages":"777 - 794"},"PeriodicalIF":0.0,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142994362","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}