Digitization and Automation Applied to Construction Safety Management

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 373

Special Issue Editors

School of Economics and Management, Chang'an University, Xi'an, China
Interests: smart construction; BIM; safety management; knowledge management
Special Issues, Collections and Topics in MDPI journals
Department of Civil and Natural Resources Engineering, University of Canterbury, 69 Creyke Road, Christchurch 8140, New Zealand
Interests: construction safety; construction technologies; sustainable design and construction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the potential of the cutting-edge digital and automation technologies in improving the effectiveness of construction safety management. With the rapid evolution of information technologies such as the Internet of Things (IoT), Building Information Modeling (BIM), artificial intelligence (AI), and Large Language Models (LLMs), the construction industry is witnessing unprecedented opportunities to enhance safety protocols, risk assessment, real-time decision making, and labor upskilling. This Special Issue focuses on innovative applications of these technologies in managing the construction processes of buildings, roads, tunnels, and structures to avoid accidents and casualties, emphasizing their role in optimizing workflows, improving hazard prediction, fostering proactive risk mitigation strategies, and teaming human workers and AI.

By bridging technological advancements with managerial innovation, this Special Issue aims to provide actionable insights for researchers and practitioners to build safer, smarter, and more resilient construction ecosystems. Therefore, submitted  papers should focus on state-of-the-art research on various aspects of digital and automation technology adoption, from both academic and industry perspectives. A key emphasis lies in the integration of these tools into management frameworks—such as dynamic resource allocation, data-driven safety training programs, and collaborative platforms for stakeholder communication. The Special Issue also welcomes studies addressing challenges in technology adoption, including workforce adaptation, interoperability of systems, and ethical implications of automated decision making. Within this, topics of interest include, but are not limited to, the following:

  • Safety informatics and data analytics;
  • AI-based accident analysis and prevention;
  • Cognitive aspects of technology-enabled safety practices;
  • AI-driven safety monitoring and alert systems;
  • Automated safety compliance checks;
  • Digital twins for simulating hazardous scenarios and human cognition;
  • Human–robot teaming/interactions/trust in construction safety management;
  • IoT-enabled wearable devices for worker/machine/material safety tracking;
  • Integration of safety science with emerging technologies.

Dr. Sheng Xu
Dr. Ke Chen
Dr. Brian Guo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • safety management
  • digital technology
  • artificial intelligent
  • human-robot interaction
  • automation and robotics
  • human cognition, ergonomics
  • digital twins

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Published Papers (1 paper)

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Research

20 pages, 3084 KB  
Article
Decoding Construction Accident Causality: A Decade of Textual Reports Analyzed
by Yuelin Wang and Patrick X. W. Zou
Buildings 2025, 15(21), 3859; https://doi.org/10.3390/buildings15213859 (registering DOI) - 25 Oct 2025
Abstract
Analyzing accident reports to absorb past experiences is crucial for construction site safety. Current methods of processing textual accident reports are time-consuming and labor-intensive. This research applied the LDA topic model to analyze construction accident reports, successfully identifying five main types of accidents: [...] Read more.
Analyzing accident reports to absorb past experiences is crucial for construction site safety. Current methods of processing textual accident reports are time-consuming and labor-intensive. This research applied the LDA topic model to analyze construction accident reports, successfully identifying five main types of accidents: Falls from Height (23.5%), Struck-by and Contact Injuries (22.4%), Slips, Trips, and Falls (21.8%), Hot Work & Vehicle Hazards (18.1%), and Lifting and Machinery Accidents (14.2%). By mining the rich contextual details within unstructured textual descriptions, this research revealed that environmental factors constituted the most prevalent category of contributing causes, followed by human factors. Further analysis traced the root causes to deficiencies in management systems, particularly poor task planning and inadequate training. The LDA model demonstrated superior effectiveness in extracting interpretable topics directly mappable to engineering knowledge and uncovering these latent factors from large-scale, decade-spanning textual data at low computational cost. The findings offer transformative perspectives for improving construction site safety by prioritizing environmental control and management system enhancement. The main theoretical contributions of this research are threefold. First, it demonstrates the efficacy of LDA topic modeling as a powerful tool for extracting interpretable and actionable knowledge from large-scale, unstructured textual safety data, aligning with the growing interest in data-driven safety management in the construction sector. Second, it provides large-scale, empirical evidence that challenges the traditional dogma of “human factor dominance” by systematically quantifying the critical role of environmental and managerial root causes. Third, it presents a transparent, data-driven protocol for transitioning from topic identification to causal analysis, moving from assertion to evidence. Future work should focus on integrating multi-dimensional data for comprehensive accident analysis. Full article
(This article belongs to the Special Issue Digitization and Automation Applied to Construction Safety Management)
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