The Influence of Human-Organizational Factors on Falling Accidents From Historical Text Data

This paper firstly proposes a modified human factor classification analysis system (HFACS) framework based on literature analysis and the characteristics of falling accidents in construction. Second, a Bayesian network (BN) topology is constructed based on the dependence between human factors and organizational factors, and the probability distribution of the human-organizational factors in a BN risk assessment model is calculated based on falling accident reports and fuzzy set theory. Finally, the sensitivity of the causal factors is determined. The results show that 1) the most important reason for falling accidents is unsafe on-site supervision. 2) There are significant factors that influence falling accidents at different levels in the proposed model, including operation violations in the unsafe acts layer, factors related to an adverse technological environment for the unsafe acts layer, loopholes in site management in the unsafe on-site supervision layer, lack of safety culture in the adverse organizational influence layer, and lax government regulation in the adverse external environment layer. 3) According to the results of the BN risk assessment model, the most likely causes are loopholes in site management work, lack of safety culture, insufficient safety inspections and acceptance, vulnerable process management and operation violations.

A-site management and oxygen-deficient regulation strategy with perovskite oxide electrocatalyst for oxygen evolution reaction

Perovskite oxides (ABO3) as electrocatalysts applied for oxygen evolution reaction (OER) have been studied for decades due to its high flexibility and adjustability for electronic structures. Herein, a series of...

The application of “deep learning” in construction site management: scientometric, thematic and critical analysis

Purpose The digital construction transformation requires using emerging digital technology such as deep learning to automate implementing tasks. Therefore, this paper aims to evaluate the current state of using deep learning in the construction management tasks to enable researchers to determine the capabilities of current solutions, as well as finding research gaps to carry out more research to bridge revealed knowledge and practice gaps. Design/methodology/approach The scientometric analysis is conducted for 181 articles to assess the density of publications in different topics of deep learning-based construction management applications. After that, a thematic and gap analysis are conducted to analyze contributions and limitations of key published articles in each area of application. Findings The scientometric analysis indicates that there are four main applications of deep learning in construction management, namely, automating progress monitoring, automating safety warning for workers, managing construction equipment, integrating Internet of things with deep learning to automatically collect data from the site. The thematic and gap analysis refers to many successful cases of using deep learning in automating site management tasks; however, more validations are recommended to test developed solutions, as well as additional research is required to consider practitioners and workers perspectives to implement existing applications in their daily tasks. Practical implications This paper enables researchers to directly find the research gaps in the existing solutions and develop more workable applications to bridge revealed gaps. Accordingly, this will be reflected on speeding the digital construction transformation, which is a strategy over the world. Originality/value To the best of the authors’ knowledge, this paper is the first of its kind to adopt a structured technique to assess deep learning-based construction site management applications to enable researcher/practitioners to either adopting these applications in their projects or conducting further research to extend existing solutions and bridging revealed knowledge gaps.

Critical Factors Causing Material Wastes in Building Construction Projects

Construction sites generate a large amount of material wastes and have become a common problem with associated risks in Ethiopia. However, the sources of such wastes are not well recognised. Therefore, the purpose of this research was to analyse the risk factors that contribute to material wastes in building construction projects. To achieve this goal, the factors that cause construction wastes were identified from literature and construction experts via focus group discussions and personal interviews. Following this, the factors were subjected to a questionnaire survey to identify the most critical factors of construction wastes. The questionnaire was distributed purposively to 85 construction experts representing contractors, consultants, and clients and 70 questionnaires were duly received for analysis. The data were analysed with a mean score and ranked to identify the most critical factors generating material wastes at construction sites. According to the results of the study frequent changes made to the design, poor strategies for waste minimisation, improper storage of material, poor site management, poor planning and supervision, and errors of contract document were the most critical factors causing construction wastes.

Automated Vigor Estimation on Vineyards

Estimating the balance or vigor in vines, as the yield to pruning weight relation, is a useful parameter that growers use to better prepare for the harvest season and to establish precision agriculture management of the vineyard, achieving specific site planification like pruning, debriefing or budding. Traditionally growers obtain this parameter by first manually weighting the pruned canes during the vineyard dormant season (no leaves); second during the harvest collect the weight of the fruit for the vines evaluated in the first step and then correlate the two measures. Since this is a very manual and time-consuming task, growers usually obtain this number by just taking a couple of samples and extrapolating this value to the entire vineyard, losing all the variability present in theirs fields, which imply loss in information that can lead to specific site management and consequently grape quality and quantity improvement. In this paper we develop a computer vision-based algorithm that is robust to differences in trellis system, varieties and light conditions; to automatically estimate the pruning weight and consequently the variability of vigor inside the lot. The results will be used to improve the way local growers plan the annual winter pruning, advancing in the transformation to precision agriculture. Our proposed solution doesn\textsc{\char13}t require to weight the shoots (also called canes), creating prescription maps (detail instructions for pruning, harvest and other management decisions specific for the location) based in the estimated vigor automatically. Our solution uses Deep Learning (DL) techniques to get the segmentation of the vine trees directly from the image captured on the field during dormant season

Simulation of Emergency Evacuation from Construction Site of Prefabricated Buildings

To ensure the safe construction of prefabricated buildings and improve the efficiency of the safe evacuation of construction personnel after a fire caused by improper operation during construction, this study used the PyroSim software to numerically simulate a fire situation based on the size and volume of a prefabricated building construction site. The variation rules of smoke visibility, CO concentration, and ambient temperature in the construction site of prefabricated buildings were analyzed and the available safe evacuation time was determined. Moreover, the Pathfinder software was used for simulation in combination with the physical attributes of personnel, evacuation speed, and personnel proportions. The time required for safe evacuation was determined and the factors influencing the evacuation time, such as the quantity and location of stacked prefabricated components, machinery, and appliances, and the number of on-site construction personnel, were analyzed. The results reveal that the original layout of the prefabricated building construction site cannot facilitate the safe evacuation of all construction personnel. The bottleneck area for the evacuation of construction personnel is the indoor corridor and evacuation stairway. The quantity and location of stacked items at the construction site greatly influence the evacuation time. When the number of construction personnel on each floor reaches a certain value, restrictions should be imposed. The results obtained by this study can provide the theoretical basis for the rational planning of evacuation routes and construction site management.