Asian Journal of Civil Engineering最新文献

This study investigates the integration of quality and client satisfaction into resource-constrained time-cost trade-off optimization for construction projects. Utilizing the Non-dominated Sorting Genetic Algorithm III (NSGA-III), a multi-objective trade-off model (MOTM) is developed to optimize the resource-constrained time-cost-quality-client satisfaction trade-off (RCTCQCST). Through a case study of a one-storey building construction project involving 21 activities with five execution modes each, the model’s effectiveness is demonstrated. The case study results yield Pareto-optimal combinations of execution modes, ensuring resource-efficient project execution, and demonstrate the NSGA-III-based MOTM’s effectiveness in balancing objectives under resource constraints. Besides, a weighted sum technique is employed to pick one solution from Pareto-optimal solutions for the execution of project. Comparative analysis against existing scheduling models shows that the NSGA-III-based MOTM performs better in achieving optimal trade-offs. The implications of this study suggest that incorporating quality and client satisfaction into the optimization process can significantly enhance project outcomes, offering a robust decision-making tool for project managers to achieve a comprehensive balance between time, cost, quality, and client satisfaction.

Graphical Abstract

Shell structures are essential components in many industries, including aerospace, automotive, and civil engineering, due to their lightweight properties and ability to resist diverse loads. With the increasing construction of large-scale buildings, the strategic and economic significance of these structures has risen sharply. However, under certain loading conditions, shell structures may be subject to significant deformations, compromising their structural integrity. Therefore, incorporating stiffeners, such as ring stiffeners, has become a popular design technique to make shell structures more rigid and capable of holding more weight while reducing large deformations. Recent advances in finite element analysis have enabled comprehensive studies of stiffened shells. This study focuses on modeling and analyzing the stiffened shell using a three-dimensional finite element (solid element) for both the shell and stiffeners in ABAQUS software. The main objective of this paper is to evaluate the effect of various stiffener geometries and thicknesses on the deformation of cylindrical shells under concentrated loading and different boundary conditions. The study examines stiffener configurations, such as rectangular, I, Tee, and channel shapes, to assess their impact on reducing displacements and enhancing performance. The results show that three-dimensional finite elements are very efficient in modeling stiffened shell structures, and ring stiffeners are also very useful in reducing the shell’s deflections. This study provides insights into optimizing stiffened shell designs to increase their structural integrity and resistance to deformation.

Gaussian process regression (GPR) models, with their desirable mathematical properties and outstanding practical performance, are increasingly favored in statistics, engineering, and other domains. Despite their advantages, challenges arise when applying GPR to extensive datasets with repeated observations. This study aims to develop models for predicting Finland's soft-sensitive clays’ undrained shear strength (Su). The study presents the first correlation equations for Su of Finnish clays, derived from a multivariate dataset compiled using field and laboratory measurements from 24 locations across Finland. The dataset includes key parameters such as Su from field vane tests, reconsolidation stress, vertical effective stress, liquid limit, plastic limit, natural water content, and sensitivity. The GPR model demonstrated high accuracy, with a mean squared error (MSE) of 0.11% and a correlation coefficient (R²) of 0.98, indicating excellent predictive performance. These findings highlight the strong interactions between Su, consolidation stresses, and index parameters, establishing a robust foundation for practical GPR implementation. The GPR model is recommended for forecasting Su due to its high learning performance and ability to display prediction outputs and intervals. This research has significant implications for various civil engineering applications, including transportation, geotechnical, construction, and structural engineering, offering a valuable tool for improving engineering practices and decision-making.

The effective retrofitting of ventilation systems is essential for enhancing indoor air quality, energy efficiency, noise reduction, maintenance ease, aesthetics, and reducing the carbon footprint of buildings. This study presents the development of a resource-constrained time–cost trade-off optimization model for ventilation system retrofitting using the non-dominated sorting genetic algorithm III (NSGA-III). The model integrates various retrofitting options, categorized into ventilation capacity enhancement, energy efficiency improvements, air quality enhancements, noise reduction measures, maintenance facilitation, aesthetics improvements, and carbon footprint reduction strategies, each characterized by its retrofitting duration and associated cost. The objective is to identify optimal combinations of retrofitting options that minimize project completion time and cost while adhering to resource constraints. The NSGA-III optimization process generates Pareto-efficient solutions, providing decision-makers with a spectrum of optimal trade-offs. Model validation and performance metrics-based comparative analysis between the developed and existing models demonstrate the superior effectiveness of the proposed model in solving trade-off problems. The study employs a weighted sum method to select one solution from the set of Pareto-optimal solutions, illustrating the effectiveness of NSGA-III in balancing project timelines and costs. This research offers a robust methodological framework that enhances decision-making in the construction industry, contributing to global sustainable development goals.

This study presents a comprehensive exploration into predicting the compressive strength of concrete by incorporating waste iron as a partial substitute for sand, employing a backpropagation neural network (BPNN) model. The optimized BPNN model, fine-tuned with intricate hyperparameters, demonstrates exceptional predictive accuracy, achieving an R² score of 0.9272 on the test set. Low mean squared error (MSE), Root Mean squared error (RMSE), Mean absolute error (MAE), and mean absolute percentage error (MAPE) values underscore the model's proficiency in minimizing prediction errors. The hyperparameter optimization process results in a complex neural network architecture, highlighting the intricate nature of capturing the nuances of concrete compressive strength. Visualization tools, including actual versus predicted plots and radar plots, offer clear insights into the model’s consistent excellence across various metrics. The analysis not only validates the model's precision but also provides a visually intuitive representation of its performance. Global sensitivity analysis reveals that the percentage of iron waste (‘Iron Waste (%)’) emerges as a pivotal factor, with ST and S1 values of 0.668864 and 0.643553, respectively, influencing the variability in compressive strength predictions. ‘Age of concrete’ of the concrete follows as the second most influential factor, with ST and S1 values of 0.344926 and 0.321598, respectively. This study contributes to understanding the intricate relationships between input features and concrete compressive strength, emphasizing the importance of considering the proportion of iron waste in sustainable concrete mixtures. Overall, the findings provide valuable insights for optimizing concrete formulations and advancing eco-friendly construction practices.

This paper introduces an innovative resource-constrained time–cost-quality trade-off optimization model (RCTCQ-TOOM) designed specifically for retrofitting planning projects in densely populated areas such as India. The model integrates seven critical aspects of retrofitting and leverages the advanced NSGA-III algorithm to find Pareto-optimal solutions that effectively balance project completion time, cost, and quality constraints. A case of retrofitting project of Gwalior, India, demonstrates the real-world applicability and effectiveness of RCTCQ-TOOM in providing valuable decision support for stakeholders. The study showcases how the model can optimize retrofitting projects by presenting a diverse set of superior-quality solutions along Pareto-optimal front within a reasonable computational timeframe. The paper also includes a comparative analysis with other multi-objective optimization methods, such as Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Ant Colony Optimization (MOACO), and Multi-Objective Teaching–Learning-Based Optimization (MOTLBO). This analysis highlights NSGA-III's superior performance in achieving both convergence and diversity of optimal solutions. The findings indicate that NSGA-III effectively balances time, cost, and quality aspects, making it a robust tool for optimizing retrofitting projects. The RCTCQ-TOOM, combined with the NSGA-III algorithm, promotes sustainability and resilience in urban development by providing a comprehensive and efficient optimization framework.

Graphical abstract

The incorporation of various modifiers such as rubber, plastic, fibers, and anti-stripping agents has demonstrated favourable effects on the mechanical properties of bituminous mixes, including Marshall stability (MS) and indirect tensile strength (ITS), thereby addressing various challenges associated with conventional bitumen. Recent research has notably focused on predicting the mechanical performance of both unmodified and modified bituminous mixes using advanced machine learning (ML) techniques, offering potential solutions to issues encountered in classical laboratory experiments. The present comprehensive review synthesizes the existing literature on ML techniques for predicting MS and ITS of bituminous mixes. Initially, it reviews the range of inputs utilized and suggests missing inputs. The impact of optimal user-defined parameters on discrete model performance, along with model comparison relying on statistical metrics, is analysed to recognize ML models with adequate predictive potential. Additionally, the paper examines the validation aspect of the model dataset in terms of experiments, providing insights for model developments in future. Overall, this study aims to deliver an overview of the present status of ML models for predicting MS and ITS, highlighting research gaps for model development and attaining anticipated performance. Hence, the condensed knowledge will prove invaluable in directing future research efforts towards the development of sustainable and efficient modified bituminous mixes.

This study investigates the seismic performance of reinforced concrete structures with masonry infill walls under different distribution arrangements. Over the past six decades, the interaction between reinforced concrete frames and infill walls has been crucial due to its impact on structural behavior during earthquakes. This study utilizes pushover and fragility analyses to assess the effects of various infill wall distributions on reinforced concrete frames. The results demonstrate that the presence of infill walls significantly enhances lateral stiffness and alters the overall structural response. Results indicate that symmetrically distributed infill walls significantly increase lateral stiffness and base shear capacity compared to models without infill walls, while asymmetrical or partially filled models show lesser improvements. Fragility analysis shows varying collapse probabilities and damage susceptibility based on infill wall distribution, with symmetric arrangements demonstrating lower collapse likelihood and higher safety levels. Models with infill walls on one side or lacking them on the ground floor exhibit higher fragility, especially under severe damage scenarios, highlighting the critical role of symmetric infill wall placement in enhancing seismic resilience and ensuring building safety in earthquake-prone regions.

The advent and progress of machine learning (ML) have profoundly influenced civil engineering, especially in forecasting concrete's mechanical properties. This research focuses on predicting the fly ash (FA) concrete compressive strength (CS) using six different ML models: linear regression (LR), decision tree (DT), random forest (RF), extreme Ggradient boosting (XGB), support vector regression (SVR), and artificial neural network (ANN). A dataset comprising 1089 records, each with 12 input features, including the chemical compositions of FA, was used to train these models. The models' performance was assessed and compared using mean square error (MSE), mean absolute error (MAE), and the coefficient of determination (R²), with validation achieved through the K-fold cross-validation method. Among all the models evaluated, XGB was the most accurate, attaining an R² value of 0.95. To interpret and understand the ML model predictions, Shapley Additive Explanations (SHAP) analysis was employed. It revealed that curing days, water-binder ratio, cement content, and superplasticizer are the most critical factors in predicting the FA concrete CS. These results indicate the potential of ML models, especially extreme gradient boosting, in accurately predicting concrete strength, promoting more efficient and effective use of FA in construction. Additionally, a graphical user interface (GUI) was created to enhance user interaction with the prediction models, improving the utility and accessibility of ML applications.

The numerous investigations of soft computing algorithms have been done to forecast the flexural strength (FS) of concrete using waste foundry sand (WFS). This research aims to study the application of soft-computing techniques, including random forest (RF), Reduced error pruning tree (REP tree), artificial neural network (ANN), Random tree (RT) and M5P-based model, in forecasting the FS of concrete. For this aim, a dataset of 158 experimental results with a wide range of FS values, ranging from FS 2.21 MPa to 6.98 MPa, was collected from existing literature. The input parameters for the soft computing models included the curing period (CP), slump (SL), fine aggregates (FA), coarse aggregates (CA), water/cement ratio (W/C), cement (C), waste foundry sand (WFS) content, sand (S), WFS blended with other substances (WFS/other), and water (W), with the FS of concrete as the output parameter. Various performance indices were employed to assess the reliability and accuracy of each model, including mean absolute error (MAE), relative root mean square error (RRMSE), coefficient of correlation (R), Nash–Sutcliffe model efficiency coefficient (NSE), root-mean-squared error (RMSE), Wilmott index (WI), and relative absolute error (RAE). Results from the RF model showed high accuracy with R values of 0.9936 and 0.9789, MAE values of 0.1186 and 0.2348, WI values of 0.996 and 0.987, RMSE values of 0.1538 and 0.2931, RAE values of 11.33% and 20.54%, NSE values of 0.986 and 0.953, and RRMSE values of 12.04% and 21.59% during the training as well as testing stages, respectively. The REP Tree model also displayed competitive predictive capability compared to ANN, RT, and M5P models. A sensitivity analysis revealed that the curing period (CP) was the most influential parameter in forecasting FS using the RF-based model. The research emphasises the efficiency of soft computing methods, specifically the random forest, in perfectly assessing the FS of concrete through the utilisation of waste foundry sand. Moreover, it provides researchers with a faster and more economical method to assess the impact of waste foundry sand and additional variables on FS estimation, hence avoiding the necessity for laborious and expensive experimental investigations.