Asian Journal of Civil Engineering最新文献

The study investigates the seismic performance of fixed base and base-isolated regular reinforced concrete (RC) frames (G+21 floors) using SAP 2000. High-rise buildings in seismic zones require innovative design approaches to mitigate earthquake-induced damages. Base isolation is a promising technique that decouples the structure from ground motions, potentially reducing seismic forces and enhancing performance. This research focuses on comparative analysis through detailed modeling and simulations. Two structural models—fixed base and base-isolated—are developed in SAP 2000. The base-isolated model incorporates elastomeric bearings to absorb seismic energy. The study evaluates seismic response parameters, including story displacements, base shear forces, inter-story drift ratios, and natural frequencies. Results indicate significant improvements in the seismic performance of the base-isolated structure compared to the fixed base. Maximum lateral displacements and inter-story drift ratios are considerably lower in the base-isolated model, demonstrating enhanced stability and reduced damage potential. Base shear forces are also substantially reduced, highlighting the effectiveness of base isolation in dissipating seismic energy. The natural frequency analysis shows a shift to lower values for the base-isolated structure, confirming the increased flexibility and energy absorption capacity. The findings underscore the potential of base isolation to improve seismic resilience in high-rise buildings, providing valuable insights for engineers and designers in seismic-prone regions. Future research should explore various isolation materials and configurations to optimize performance further.

The vulnerability of secondary systems (SS) to seismic activities has become a critical area of research due to their potential for significant damage even under low-intensity seismic waves, particularly those caused by underground blast induced ground motion (UBIGM). Unlike the extensively studied Primary System (PS), SS are prone to significant damage, necessitating a deeper understanding of their dynamic responses. The study introduces a novel modeling approach for analyzing the response of secondary structures (SS) under underground blast-induced ground motion (UBIGM). Utilizing MATLAB code for the Newmark’s Beta method, this research evaluates the peak acceleration of SS, considering variables such as mass ratio, explosive mass, and the transmission medium of the blast wave. The results reveal that peak accelerations of SS are 5.8 to 6.0 times higher when the blast waves travel through soil compared to rock, underscoring soil's amplifying effect on ground motion. Furthermore, linear regression analysis identifies the primary factors influencing SS response, leading to the development of a predictive equation for peak acceleration. These findings are instrumental in improving the design and survivability of SS against underground blast-induced excitations, thereby contributing to the overall safety and stability of structures in seismic-prone areas.

Using industrial soil waste or secondary materials for making cement and concrete has encouraged the construction industry because it uses fewer natural resources. High-performance concrete (HPC) is recognized for its exceptional strength and sturdiness compared to conventional concrete. Accurate prediction of the compressive concentration of HPC is vital for optimizing the concrete mix design and ensuring structural integrity. Machine learning (ML) techniques have shown promise in predicting concrete properties, including compressive strength. This research focuses on various ML techniques for their suitability in predicting the compressive dilution of HPC. In this research, the Extended Deep Neural Network (EDNN) technique is used to analyze the strengths, limitations, and performance of different ML algorithms and identify the most effective methods for this specific prediction task. However, there is a problem with accuracy. Therefore, our research approach is the EDNN-centred strength characteristics prediction of HPC. In the suggested approach, data is initially acquired. Afterward, the data is pre-processed through normalization and removing missing data. Thus, the data are fed into the EDNN algorithm, which forecasts the strength characteristics of the particular mixed input designs. With the Multi-Objective Jellyfish Optimization (MOJO) technique, the value of weight is initialized in the EDNN. The activation function is the Gaussian radial function. In the experimental analysis, the implementation of the suggested EDNN is evaluated to the performance of the prevailing algorithms. When compared to current research methodologies, the proposed method performs better in this regard.

Reinforced concrete columns are the most important load-bearing structural components in the buildings. These columns require retrofitting due to multiple reasons like poor design, inadequate materials, weak construction and improper quality control. This research involves retrofitting of reinforced concrete columns subjected to biaxial loads by using of enhanced ferrocement jacketing. Fifteen reinforced concrete columns are cast in 150 × 150 × 1700 mm including 250 × 250 × 250 mm concrete brackets at each end. They divided into three groups each with four columns in addition to the three control specimens. The three groups are preloaded up to 65 and 85% of the total failure loads of control specimens. After that, the first group retrofitted using traditional ferrocement consists of normal cement-sand mortar and reinforced with steel wire mesh. The second group retrofitted with modified mortar and steel wire mesh reinforcement. While the third group of columns retrofitted with modified mortar and reinforced with fiber glass mesh. All the columns are then biaxially loaded till failure with two different eccentricity values 30 and 70 mm. The results show that using enhanced ferrocement jacketing increases the load carrying capacity of retrofitted columns comparing to the control specimens with different percent of enhancement up to 30.6% for the column retrofitted with modified mortar and fiber glass mesh. Also, it develops the failure behavior, ductility ratio and cracks resistance of the retrofitted columns.

Bolted connections are widely used in steel structures. Detection of bolt loosening is the prime concern in the bolted joints to avoid sudden failure leading to catastrophe. Loosening of the bolts causes interfacial movement by reducing the pre-torque when subjected to vibrations due to dynamic loads. With the advent of computing capabilities, sensor technologies, and machine learning model accuracy in bolt loosening detection, damage recognition efficiency in bolted joints has increased. Integrating deep learning with machine vision, effective models can be proposed without human interventions. The present paper summarizes the research review on bolt loosening detection using machine vision and deep learning techniques from the past decade.

This comprehensive research traces the evolution of concrete technology, focusing on nanotechnology, specifically nano-silica, as a highly promising avenue for enhancing concrete properties. The study systematically compares traditional concrete with nano-silica-reinforced concrete, shedding light on the pivotal roles of superplasticizers and nano-silica in determining compressive strength over a curing period ranging from 1 to 365 days. The analysis encompasses key factors such as the water-to-cement ratio, cement content (C), and content (S), gravel content (G), superplasticizer (SP), and Nano silica (NS), totaling 820 meticulously collected, analyzed, and modeled datasets.This research employs extensive datasets and diverse modeling techniques to predict compressive strength accurately. Key findings underscore the influence of the water-cement ratio and superplasticizers in traditional concrete, while nano-silica consistently interacts with other factors, except for curing time. The study presents numerical models for compressive strength estimation and contributes to sustainable construction practices. Utilizing statistical modeling, the research establishes optimal models with minimal root mean square error (RMSE). Correlation analysis reveals nuanced connections between traditional and nano-silica-containing concrete, with a marginal strength difference not exceeding 5 MPa. Various models, including nonlinear regression, full quadratic models, and an artificial neural network (ANN), are employed to predict compressive strength. Significantly, the study finds that the Artificial Neural Network (ANN) model consistently outperforms other models in predicting the compressive strength of conventional concrete, while the Full Quadratic (FQ) model exhibits remarkable consistency, especially in forecasting the strength of traditional concrete. Sensitivity analysis underscores the pivotal roles of factors such as water-cement ratio, cement content, and superplasticizer in influencing model accuracy. Notably, nano-silica, identified through sensitivity analysis, significantly contributes to predictive accuracy, highlighting its unique and influential role in shaping concrete strength. This research deepens our understanding of the multifaceted factors influencing nano-silica-infused concrete strength, emphasizing the necessity to consider multiple variables for precise predictions.

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.