Earthquakes cause liquefaction, which disturbs the design phase during the building construction process. The potential of earthquake-induced liquefaction was estimated initially based on analytical and numerical methods. The conventional methods face problems in providing empirical formulations in the presence of uncertainties. Accordingly, machine learning (ML) algorithms were implemented to predict the liquefaction potential. Although the ML models perform well with the specific liquefaction dataset, they fail to produce accurate results when used on other datasets. This study proposes a stacked generalization model (SGM), constructed by aggregating algorithms with the best performances, such as the multilayer perceptron regressor (MLPR), support vector regression (SVR), and linear regressor, to build an efficient prediction model to estimate the potential of earthquake-induced liquefaction on settlements. The dataset from the Korean Geotechnical Information database system and the standard penetration test conducted on the 2016 Pohang earthquake in South Korea were used. The model performance was evaluated by using the R² score, mean-square error (MSE), standard deviation, covariance, and root-MSE. Model validation was performed to compare the performance of the proposed SGM with SVR and MLPR models. The proposed SGM yielded the best performance compared with those of the other base models.

Construction safety accidents occur due to a combination of factors. Even a minor accident that could have been treated as a simple injury can lead to a serious accident or death, depending on when and where it occurred. Currently, methods for tracking worker behavior to manage such construction safety accidents are being studied. However, applying the methods to the construction site, various additional elements (e.g., sensors, transmitters, wearing equipment, and control systems) that must be additionally installed and managed are required. The cost of installation and management of these factors increases in proportion to the size of the site and the number of targets to be managed. In addition, the application of new equipment and new rules lowers the work efficiency of workers. In this paper, the following contents are described: (1) system overview, (2) image processing-QR code-based safety management target recognition methodology, and (3) object location discrimination technique applying the geometric transformation. Finally, the proposed methodology was tested to confirm the operation in the field, and the experimental results and conclusions were described in the paper.

Construction quality is one of the primary management objectives relating to duration and cost for construction projects. Project managers struggle with minimizing duration and cost while maximizing quality for construction projects. In construction projects, duration and cost have management priorities. On the other hand, quality is considered a matter of achievement only when it reaches a certain level. Although the importance of quality control in construction management has been constantly discussed, it has still been sacrificed under the goal of shortening construction duration and reducing costs. This study presents a method for estimating the quantitative quality performance of construction operations in which the level of detail is breaking into the work task level for intuitive quality performance evaluation. For this purpose, quality weights of resources that have a proportional quality importance weight and quality performance indexes of resources are utilized for estimating the quantitative quality performance of construction operations. Quality performance estimation and the resource allocation optimization system is presented and validated using a construction simulation model.

Pile installation has an environmental impact through its various processes ranging from raw material extraction to construction. In addition, the environmental performance, productivity, and cost of pile installation depend on the construction plan. Therefore, the chain of activities must be considered when analyzing the sustainability of pile installation. A rational construction plan must carefully examine the factors that affect the productivity and sustainability of pile installations. This study presents a method for evaluating eco-economic performance by analyzing the resource utilization and processes of PHC pile installation. First, a process modeling technique, wherein details are broken down to the work task level, based on energy consumption and resource cost, is proposed. Second, a simulation method that calculates the eco-economic performance of the PHC pile process and resources (e.g., equipment) is presented. Third, a quantitative comparison of durations, costs, and emissions resulting from simulation, estimation based on the CSPR (Construction Standard Production Rate) and IUC (Itemized Unit Cost), site contract, and actual construction is presented. The results reveal that the method effectively reflects the prediction of duration, cost, and carbon emissions generated in the real world during the construction planning stage.

In this study, we delve into a novel approach by employing a sensor-based pattern recognition model to address the automation of construction equipment activity analysis. The model integrates time of flight (ToF) sensors with deep convolutional neural networks (DCNNs) to accurately classify the operational activities of construction equipment, focusing on piston movements. The research utilized a one-twelfth-scale excavator model, processing the displacement ratios of its pistons into a unified dataset for analysis. Methodologically, the study outlines the setup of the sensor modules and their integration with a controller, emphasizing the precision in capturing equipment dynamics. The DCNN model, characterized by its four-layered convolutional blocks, was meticulously tuned within the MATLAB environment, demonstrating the model’s learning capabilities through hyperparameter optimization. An analysis of 2070 samples representing six distinct excavator activities yielded an impressive average precision of 95.51% and a recall of 95.31%, with an overall model accuracy of 95.19%. When compared against other vision-based and accelerometer-based methods, the proposed model showcases enhanced performance and reliability under controlled experimental conditions. This substantiates its potential for practical application in real-world construction scenarios, marking a significant advancement in the field of construction equipment monitoring.