Asian Journal of Civil Engineering最新文献

This study examines the properties of high-performance fiber-reinforced concrete (HPFRC) mixes fabricated with five different replacements (0%, 5%,15%,20%, and 25%) of cement with volcanic pumice powder (VPP)and 0.5% and 1% of steel fiber. The outcomes reveal that the VPP and steel fiber blends exhibited significantly higher compressive and splitting tensile strength than the control mix, where a decline in workability and enhancement in density was registered. The HPFRC fabricated with 10% VPP and 1% steel fiber produced the best mechanical performance results among all the combinations. Furthermore, to predict the natural and mechanical properties of the HPFRC as a result of the influencing factors, extensive comparative modeling was performed, and various predictive models were proposed using regressions and machine learning (ML) techniques, i.e., artificial neural network (ANN), random forest (RF). Root-mean-squared error, mean absolute percentage error, and coefficient of determination were just a few of the metrics used to assess the quality of the models. RF was shown to have the highest R² and the lowest Root Mean Squared Error (RMSE), considering it the most effective model. Considering a strategy for environmental sustainability, this study highlights the importance of minimizing carbon footprint by lowering cement consumption.

This study aims to assess the impact of claims on construction project performance and evaluate the effectiveness of change management strategies. Using a quantitative approach, data was collected via a detailed questionnaire distributed to industry professionals, including consultants, contractors, project managers, and owners. The data was rigorously cleaned and analyzed using the Light GBM model optimized with the Locust Swarm Algorithm. Key findings reveal that delay claims increase project timelines by 20% and costs by 15%. Effective change management strategies significantly mitigate these impacts, with structured frameworks improving accuracy by 25%, precision by 20%, recall by 22%, and F1 scores by 23%. The optimized machine learning model showed a 15% improvement in accuracy and a 12% improvement in precision over non-optimized models. This study contributes to construction management by highlighting the critical role of robust change management in mitigating claim impacts and enhancing project performance. It also demonstrates the transformative potential of AI and ML in civil engineering, facilitating data-driven decision-making, optimizing resource allocation, and improving overall project outcomes.

Graphical Abstract

In modern construction, pipes and ducts are necessary for computer networking, electrical systems, air conditioning, water distribution, sewage management, and critical services. These conduits, which typically have diameters between a few millimeters and half a meter, can weaken the beam strength, increase deflection, encourage cracking, and decrease stiffness, all of which can compromise the structural integrity of buildings. One creative and affordable way to overcome these obstacles is to retrofit concrete structures with CFRP sheets. This technology has many advantages, including a favourable strength‒weight ratio, resistance to corrosion, remarkable fatigue durability, simple installation, and minimal impact on existing structural parts. The current research examines the performance of reinforced cement concrete (RCC) beams featuring various openings—rectangular, rounded rectangular, elliptical, and circular—in the shear zone. This study assesses the performance of three different CFRP reinforcement procedures via ANSYS software. It considers three different wrapping methods compared with a control beam and an opening without wrapping. The analysis focuses on finite element analysis (FEA) to observe stress variations under applied loads, enabling comparisons of different beam deflections. According to the analytical data, using CFRP reinforcement around apertures—both internally and externally—significantly increases the load-carrying capacity, which is nearly identical to that of the control beam—especially for circular holes where there is a more equal distribution of stress. Additionally, the generation of beam deflection data through ANSYS FEA simulations is explored, which is followed by training an artificial neural network (ANN) model in MATLAB and Python. The resulting ANN model serves as a rapid and accurate alternative to traditional FEA in structural analysis by effectively predicting beam deflections across various scenarios. This research contributes valuable insights into improving structural resilience in contemporary construction practices, particularly regarding the integration of essential services.

In a dynamic shift toward the digitalization of the construction industry, this research heralds a novel data-centric methodology that merges the innovative realms of 4D simulation with process mining to enhance, predict, and analyze the execution phases of construction projects. This pioneering study stands at the forefront of construction project management, offering a sophisticated tool designed to streamline project execution by enabling managers to simulate project workflows, identify potential pitfalls, and foresee critical project parameters including timelines, resource distribution, and potential risks. At its core, the methodology integrates a time-enriched 3D model with the meticulous analysis of project management data through advanced data mining techniques. This approach not only aims to refine the prediction and management of construction risks but also to optimize project execution, thereby elevating the efficiency and output of construction endeavors. The research is structured to unfold in meticulously planned stages, focusing on the synthesis of 4D models with data mining processes, the crafting of predictive algorithms, and their validation in real-world settings. Through this strategic timeline, the research aspires to validate each component of the proposed method, ensuring its efficacy and applicability in the broader construction sector. Furthermore, by bridging the gap between temporal simulation and process analysis, this study is poised to contribute valuable insights and open new avenues for innovation within both the academic sphere and the construction industry at large.

This study addresses a crucial research gap by investigating the optimal position of shear walls, the ideal shear wall-floor area ratio in building design, and their correlation with building height using non-linear analysis (Static and Dynamic). The results, including capacity curves, inter-story drift, and performance levels from both nonlinear static analysis and nonlinear dynamic analysis, are explored. Adopting principles of performance-based seismic design, the study reflects a comprehensive approach to seismic analysis and mitigation. The findings underscore that elevating the shear wall ratio not only enhances structural rigidity but also improves reliability in terms of inter-story drift, playing a crucial role in achieving the desired performance level during the design process. For a 7-story structure, a 1.00% shear wall–floor ratio is crucial, while a 1.5% ratio is essential for a 14-story structure to meet design conditions. The study highlights the intricate interplay among shear wall–floor ratios, optimal shear wall positions, and their correlation with building height as pivotal factors or main criteria influencing performance and structural integrity. Additionally, the presence of shear walls adopting compound forms (Box, U, and L) enhances reliability, while incomplete shear walls within the frame degrade half-filled frame stiffness, impacting short beam integrity. Furthermore, the study confirms the reliability of both nonlinear dynamic analysis and nonlinear static analysis, providing valuable insights into optimizing building designs for enhanced structural performance.

Walls with openings, such as doors or windows, are a common feature in building construction. These openings, regardless of their size, are strategically positioned on each floor to fulfill ventilation or other functional needs. This study primarily aimed to investigate the structural performance of 3D-printed walls with door openings under uniaxial loads. The research focused on three types of walls with an opening: unreinforced, reversed U-bar-reinforced, and reversed U-bar with rebar-reinforced walls. All walls were measured 2000 mm in width, 1310 mm in height, and 120 mm in thickness, with an opening size of 1200 mm in width and 1000 mm in height. The study examined the load-vertical deflection behavior and cracking behavior of the tested walls. It was found that reinforcing the walls improved their stiffness and cracking behavior compared to the unreinforced wall. Moreover, it was observed that vertical cracks, along with small stepped diagonal cracks induced by horizontal stress, were prevalent in the tested walls. Both the reversed U-bar with rebar-reinforced and unreinforced 3D-printed walls with an opening exhibited brittle failure, characterized by significant spalling of the 3D-printed mortar layer surfaces on the column part near the opening edge corner. For the wall with only the reversed U-bar-reinforced, the test was stopped due to safety concerns before failure occurred. The reversed U-bar with the rebar-reinforced wall exhibited a lower ultimate load at failure than the unreinforced wall. This reduction in ultimate load is attributed to higher stress concentrations around the grouted regions within the reinforced wall which causes the earlier failures. Additionally, the failure of the reversed U-bar with the rebar-reinforced wall was observed at the location where the grouted core was incompletely filled.

Despite having several qualities, the high thermal conductivity of concrete is considered as its shortcoming in tropical and subtropical countries, where temperature may reach a record high of up to 50 °C. This study deals with the experimental and numerical investigations to improve the heat insulation properties of hardened concrete hollow blocks by selecting a suitable insulation material at the ambient temperature range of 35 to 50° C. A total of ninety-six blocks were cast and tested. The dimensions of the outer moulds were 12” × 12” × 6” whereas the dimensions of the inner steel moulds (hollow section) were varied and categorised into three different batches. Each block was stuffed with the loose form of mineral wool which served as an insulating material. After preparation, the blocks were placed in the open air under direct exposure to sunlight. The difference in the temperature on the top and bottom surfaces of the blocks was recorded through several readings with regular intervals of time and compared to measure the amount of heat insulated by the mineral wool. Findings showed that with the temperature rise, insulated large hollow blocks stiffed and resisted more heat than medium and small insulated hollow blocks. It was also found that the control specimen (blocks with no insulation material) insulated less heat than when filled with mineral wool. The heat transfer coefficient for all categories of tested specimens was also calculated theoretically by making variations in the hollow space filled with mineral wool. The maximum temperature difference was more than 20 °C when the ambient temperature was 52 °C. A two-dimensional finite element (FE) model was developed and validated against the experimental results. The FE model showed close agreement with experimental results.

The quest to enhance the strength of concrete, while at the same time reducing the environmental impacts occasioned by its use, has become quite imperative in sustainable construction. Traditional approaches toward supplementary cementitious materials optimization have often fallen short in revealing synergistic interactions that maximize mechanical properties. The current research overcomes these limitations by considering combined effects of different SCMs on concrete strength levels, using advanced artificial intelligence techniques. Current methods often make assumptions with respect to linearity of the models or simple interaction effects that insufficiently represent the multi-level, nonlinear relationships between SCMs and concrete properties. Moreover, integration of microstructural analysis into predictive models is poorly explored. In this paper, a hybrid GBM-CNN methodology is proposed to model complicated interactions within SCM compositions. GBMs are competent in dealing with numerical features, such as SCM proportions, curing time, and temperature, which hold nonlinear relationships in tabular data samples. Meanwhile, CNNs process microstructural images to extract spatial features correlating to mechanical properties. These models will predict the concrete strengths by fusing their outputs using an ensemble method expected to have an R’2 of about 0.85 and an RMSE of about 2 MPa levels. The complexity of the data is managed by using multi-modal data analytics, wherein feature engineering techniques are integrated with Principal Component Analysis, thereby improving the quality of the data while bringing down its dimensionality to retain only the most vital information to explain 95% of data variance. Further, polynomial regression models with regularization—that includes non-linear interaction terms of SCMs, curing conditions, and engineered features—will be built, which highlights the key interaction terms statistically significant with p Value < 0.05. In the field of sustainability, LCA and multi-objective optimization—for example, NSGA-II—are applied for estimating and optimizing the environmental impact, cost, and performance with respect to the combination of SCMs. This integrated approach has managed to reduce CO₂ emissions by 20% at an increase in cost of less than 10%, while maintaining the target strength above 40 MPa levels. The overall AI-driven methodology would not only deepen the understanding of SCM interactions in concrete but would also provide a pragmatic framework for developing sustainable and cost-effective construction materials, hence making huge contributions to the area of sustainable engineering processes.

Self-Compacting Concrete (SCC) is a highly flowable concrete that can spread into place, fill formwork, and encapsulate reinforcement without mechanical consolidation. This study investigates the use of steel slag as a partial replacement for fine aggregate in SCC, with replacement levels ranging from 0 to 70%. Eight different mixes were prepared and tested for their fresh, mechanical, durability, and microstructural properties. Materials used include Ordinary Portland Cement (OPC) conforming to IS 269:2015, natural river sand, crushed granite, steel slag, potable water, and a polycarboxylate ether superplasticizer. The concrete mix design was based on IS 10262:2019 and EFNARC guidelines for SCC. Fresh properties were assessed using slump flow, T50 time, V-funnel, and L-box tests following EFNARC specifications. Mechanical properties were evaluated through compressive strength, splitting tensile strength, and flexural strength tests. Durability properties were assessed by water absorption, sulfate attack resistance, and freeze-thaw cycle tests. Microstructural properties were analyzed using Scanning Electron Microscopy (SEM), Thermogravimetric Analysis (TGA), and X-Ray Diffraction (XRD). The results indicate that a 50% replacement level of steel slag optimizes the properties of SCC, leading to enhanced flowability, higher compressive strength (up to 59.3 MPa at 28 days), and improved durability against sulfate attack and freeze-thaw cycles. The microstructural analysis confirmed a denser matrix with increased formation of calcium silicate hydrate (CSH) at this optimal replacement level. These findings suggest that incorporating steel slag into SCC not only enhances its performance but also contributes to sustainable construction by reducing the need for natural aggregates and utilizing industrial byproducts.

Effective classification and detection of equipment on construction sites is critical for efficient equipment management. Despite substantial research efforts in this field, most previous studies have focused on classifying a limited number of equipment categories. Furthermore, there is a scarcity of research dedicated to heavy construction equipment. Hence, this study develops a robust Convolutional Neural Network (CNN) model to classify heavy construction machinery into 12 different types. The study utilizes a comprehensive dataset of equipment images, which was divided into three distinct subsets: 60% for training the model, 30% for validating its performance, and 10% for testing its accuracy. The model’s robustness was ensured by monitoring accuracy and loss measures during the training and validation phases. The CNN model achieved approximately 85% training accuracy with a minimum loss of 0.40. The testing phase revealed a high overall precision of 80%. The CNN model accurately classifies concrete mixer machines and telescopic handlers with an Area Under the Curve (AUC) of 0.92, however pile driving machines have a lower accuracy with an AUC of 0.83. These findings demonstrate the model’s high ability to distinguish between several types of heavy construction equipment. This paper contributes to the relatively unexplored area of classifying heavy construction equipment by providing a practical tool for automating equipment classification, leading to enhanced efficiency, safety, and maintenance protocols in construction management.