Delays in the installation process of precast concrete (PC) components significantly impact the project execution. However, traditional scheduling and risk assessment methods fail to consider this process complexity and uncertainty adequately. With a systematic approach, this study analyzed complex delay mechanisms in the PC installation of three component types (columns, beams, and slabs) using 1881 observations across five work steps. Specifically, this study used k-means clustering to divide the observations into groups with certain characteristics. These groups were assessed quantitatively using the delay intensity metric. Based on the assessment, this study revealed six severe delay paths for different component types, which may combine to generate severe combinations of delay factors, considering factors such as the component size, wind conditions, worker availability, and installation location. This research contributed to PC construction management by presenting a systematic analysis of delay factors and by proposing specific severe delay paths during PC installation, offering project managers a basis for schedule optimization and risk management.

This research examines the application of periodic materials in wall–slab structures to mitigate impact noise and vibration propagation, a prevalent issue in multifamily housing. Traditional methods, such as floating floors, have proven insufficient in addressing low-frequency impact noises and in facilitating the identification of noise origins, leading to increased resident annoyance. Periodic materials, known for their effectiveness in controlling plane waves in civil engineering, were applied to the intermediate slab of a wall–slab experimental setup. The research involved assessing the attenuation of noise and vibration over distance before and after the application of periodic materials by measuring indoor sound pressure levels and the natural vibration amplitude of the structure’s members upon impact. The results showed that periodic materials not only facilitated distance attenuation but also significantly diminished noise and vibration throughout the structure, without the side effects of vibration amplification seen in prior civil engineering applications. This indicates a practical advancement in using these materials, offering a novel approach to sound insulation and enabling more precise impact source localization. Ultimately, this study contributes to improving urban living by suggesting a method to enhance acoustic comfort in multifamily housing, underlining the importance of further exploration in architectural applications of periodic materials.

This study investigated impact source ambiguity in box-frame multifamily buildings, prevalent in densely populated areas, by examining the transmission of heavy-impact noise and its relationship with junction attenuations across five floors. Contrary to the decline in noise levels with increased distance from the impact source, our findings uncover an intriguing deviation in the behaviour of vibration response levels. Surprisingly, vibrations are often intensified, rather than diminished, with distance. Notably, upon examining each room, instances where non-structural or thin concrete walls exhibited the highest vibration responses were frequently observed. Such instances were particularly prevalent in rooms not directly adjacent to the impact source. This trend markedly impedes residents’ ability to pinpoint the origin location of impact noises. This study reveals an unexpected weakening and reversal of distance attenuation in vibration, linked to the increasing number of valid flanking paths as the junction distance grows. The findings suggest that increasing slab thickness for noise insulation may unintentionally complicate source identification due to diminished junction attenuation in thicker horizontal components compared to vertical ones. To enhance acoustic comfort in such housing, this study recommends minimizing valid flanking paths through improved junction design, addressing an often-overlooked issue of source ambiguity of floor noises.

Ensuring workers’ safety is a critical component of social sustainability in the construction industry. Accidents that occur while workers are walking on construction sites constitute a significant portion of overall accidents, yet they are often overlooked in conventional task-oriented safety risk assessments. This study proposes novel Accident-During-Walk (ADW) risk indices, hierarchical and data-driven metrics designed to quantify workers’ accident risk during walks. The indices are built on Association Rule Mining and utilize structured accident data, accounting for both environmental and work-related attributes. By integrating these indices with project-specific work schedules and worker allocation plans, this study establishes an automated method for daily and weekly look-ahead ADW risk monitoring aligned with construction progress. Case studies on two construction projects validate the discriminative power of the proposed method. The results demonstrate that the indices effectively capture risk fluctuations driven by concurrent multi-trade operations and environmental severity. Notably, the analysis reveals counterintuitive patterns where adverse weather conditions paradoxically reduce risk values by constraining worker mobility, a nuance often missed by static assessments. Ultimately, this framework serves as a data-driven decision-support tool, enabling safety managers to transition from uniform inspections to targeted interventions during high-risk periods, thereby fostering a safer and more socially sustainable construction environment.

Purpose: This study performed a clustering analysis using a human pose clustering (HPC) framework in conjunction with two public datasets of personal protective equipment (PPE) usage to categorize trainees’ poses into several clusters and visualize the average pose for each identified cluster. Design/methodology/approach: The HPC framework was developed, which applies machine learning to video data to identify trainees’ reference action segments during PPE usage. To improve the clustering results, a skinned multi-person linear model was adopted to extract the three-dimensional (3D) coordinates of trainees’ body joints from a video frame and then create a 3D skeleton using the extracted joints. Also, various kinematic features and clustering algorithms were used to determine the best clustering method. Findings: The significant findings were threefold. First, more similar poses were categorized into identical clusters as human poses were represented based on the distance between spine and other joints. Second, individuals’ representative action segments identified during PPE usage were different by the direction of motion as well as the location of key body joints. Third, the identified segments corresponded to significant behavioral steps for the correct PPE use. Originality/value: This study presented a first look at how the type of kinematic features affects the performance of clustering PPE-related poses. In addition, this study provides theoretical contributions in that it clarified the difference in representative action segments in conjunction with their relevance to the correct PPE usage. Further, this study developed the HPC framework, which can identify reference segments of PPE-related actions.