Advancing Construction Safety and Health: Innovations and Strategies

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Buildings buildings	3.1	4.4	2011	15.1 Days	CHF 2600	Submit
CivilEng civileng	2.0	4.0	2020	21.7 Days	CHF 1400	Submit
Infrastructures infrastructures	2.9	6.0	2016	18.3 Days	CHF 1800	Submit
Safety safety	1.7	3.7	2015	34 Days	CHF 1800	Submit
Systems systems	3.1	4.1	2013	20.1 Days	CHF 2400	Submit

26 pages, 1035 KB

Open AccessArticle

Time-Aware Construction Site Risk Prediction Based on Sentence-BERT and 7-Day Window Aggregation with Unlabeled Data

by Shu Liu, Weidong Yan, Guoqi Liu and Rui Zhang

Buildings 2026, 16(6), 1243; https://doi.org/10.3390/buildings16061243 - 21 Mar 2026

Viewed by 176

Construction safety texts are commonly used only for descriptive statistical analysis, and systematic approaches for uncovering latent semantic risk correlations remain limited. In particular, risk identification and prioritization under unlabeled conditions remain challenging. To address this issue, this study proposes a semantic risk [...] Read more.

Construction safety texts are commonly used only for descriptive statistical analysis, and systematic approaches for uncovering latent semantic risk correlations remain limited. In particular, risk identification and prioritization under unlabeled conditions remain challenging. To address this issue, this study proposes a semantic risk association and ranking framework based on Sentence-BERT (SBERT). First, a domain-specific keyword library is constructed, and representative risk terms are extracted through tokenization, stop-word removal, and TF-IDF weighting. A fine-tuned SBERT model is then employed to generate sentence embeddings. FAISS-based similarity search is applied to match safety inspection records with historical accident reports, enabling automatic identification and ranking of the most relevant accident types. In addition, a seven-day inspection window is introduced to capture the temporal accumulation effect of hazards and support risk assessment without explicit labels. Experiments conducted on 1368 accident reports and 484 inspection records show that the proposed framework achieves an accuracy of 0.75, a recall of 1.00, and an F1-score of 0.8571. Cross-project validation yields an F1-score of 0.5607, and the performance remains stable under 10% noise interference. The results demonstrate that the proposed semantic risk association and ranking framework is effective and robust for practical construction safety management. Full article

(This article belongs to the Topic Advancing Construction Safety and Health: Innovations and Strategies)

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17 pages, 1676 KB

Open AccessArticle

Construction Accident Prediction via Generative AI and AutoML Approaches

by Sungchul Seo, Dahyun Oh, Kyubyung Kang, HyunJung Park and JungHo Jeon

Appl. Sci. 2026, 16(5), 2412; https://doi.org/10.3390/app16052412 - 2 Mar 2026

Viewed by 324

Abstract

The construction industry remains one of the most hazardous sectors, with a high incidence of injuries and fatalities, making accurate accident prediction essential for improving safety performance. Although machine learning and deep learning approaches have been widely applied to construction accident prediction, most [...] Read more.

The construction industry remains one of the most hazardous sectors, with a high incidence of injuries and fatalities, making accurate accident prediction essential for improving safety performance. Although machine learning and deep learning approaches have been widely applied to construction accident prediction, most prior studies have primarily focused on optimizing predictive accuracy within structured modeling pipelines under internal validation settings. In contrast, the application of Generative Artificial Intelligence (Generative AI) for accident prediction remains relatively underexplored, and systematic comparisons between Generative AI and Automated Machine Learning (AutoML), particularly under standardized and external validation conditions, are limited. To address this research gap, this study provides a structured comparative evaluation of AutoML and a fine-tuned Generative Pre-trained Transformer (GPT) model in terms of predictive performance, training efficiency, robustness under external validation, and operational usability. A dataset comprising construction accident cases obtained from Korea’s Construction Safety Management Integrated Information (CSI) was used. AutoML was employed to evaluate multiple machine learning classifiers, while a GPT-based model was fine-tuned to classify accident severity. Model performance was assessed using accuracy, precision, recall, and F₁-score metrics. The results indicate that AutoML achieved higher predictive accuracy (97.48%) under controlled training conditions, whereas the Generative AI model achieved 75.6%. However, AutoML required substantial preprocessing and optimization efforts. In contrast, the GPT-based model demonstrated greater deployment flexibility with minimal data preparation. External validation using newly observed imbalanced data revealed that AutoML experienced performance degradation, whereas the Generative AI model maintained relatively stable performance. These findings suggest that Generative AI may serve as a complementary and deployment-friendly alternative in construction accident prediction contexts where adaptability, external validation robustness, and usability are prioritized. Full article

(This article belongs to the Topic Advancing Construction Safety and Health: Innovations and Strategies)

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28 pages, 5515 KB

Open AccessArticle

Automated Guided Vehicle (AGV) Transport System for Hospital Logistics: Analysis and Optimization of Routes Through BIM and IFC Models

by Beatrice Maria Toldo, Giulia De Cet and Carlo Zanchetta

Buildings 2026, 16(5), 900; https://doi.org/10.3390/buildings16050900 - 25 Feb 2026

Viewed by 436

Abstract

Internal hospital logistics are inherently complex, characterized by the critical need to move essential materials with high efficiency, precision, and safety. The adoption of automated guided vehicles (AGVs) is essential for automating these flows, but designing and optimizing their routes represents a significant [...] Read more.

Internal hospital logistics are inherently complex, characterized by the critical need to move essential materials with high efficiency, precision, and safety. The adoption of automated guided vehicles (AGVs) is essential for automating these flows, but designing and optimizing their routes represents a significant challenge. This study presents a methodology for analyzing and optimizing AGV paths within healthcare facilities, effectively managing three-dimensional spatial complexity. The methodology leverages BIM and the open IFC standard to obtain an accurate geometric and semantic representation of the building. These data are then converted into a graph model using graph theory. Pathfinding algorithms, such as A*, are applied to this graph to calculate and optimize AGV trajectories, considering operational and collision constraints. The approach provides distance-optimized AGV paths. The integration of BIM, IFC, and graph theory proves to be an effective tool for logistical planning, simulation, and proactive management of AGVs in multi-level environments. This research contributes to the digital transformation of the construction sector by demonstrating how the integration of open standards and advanced algorithms can optimize the operational performance of complex buildings. By bridging the gap between architectural modeling and robotic logistics, the proposed approach supports the development of “smart buildings” and promotes more sustainable and technologically advanced management of healthcare facilities. Full article

(This article belongs to the Topic Advancing Construction Safety and Health: Innovations and Strategies)

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11 pages, 2509 KB

Open AccessArticle

Design of a Combined Support System for Constructing a New Type of Conical Shell Silo Roof

by Guanchao Xu, Jianhua Yu, Junran Zhang, Yimin Liang and Beifang Gu

Appl. Sci. 2026, 16(5), 2205; https://doi.org/10.3390/app16052205 - 25 Feb 2026

Viewed by 270

Abstract

Reinforced concrete conical shell silo roofs continue to present construction challenges, despite the widespread adoption of slip-form technology for silo walls. This study introduces a novel combined temporary support system for cast-in-place conical shell silo roofs, validated through an engineering case in Suiping. [...] Read more.

Reinforced concrete conical shell silo roofs continue to present construction challenges, despite the widespread adoption of slip-form technology for silo walls. This study introduces a novel combined temporary support system for cast-in-place conical shell silo roofs, validated through an engineering case in Suiping. The proposed system consists of (i) an umbrella-type conical shell combined support structure and (ii) a cross-type vertical core-tube support. Focusing on the umbrella subsystem, a shell–truss framework is developed based on the geometry of cylindrical–conical shell roofs. Special structural components, along with prestressed reinforcement techniques, are introduced following the principles of structural and elastic mechanics. The traditional inclined-beam shoring concept is incorporated into an arched load path: inclined members are circumferentially connected at nodes to form a shell–arch support mechanism, thereby improving force transfer efficiency and reducing flexural demands. Finite element analyses of representative construction stages are conducted to evaluate displacement and stress responses. The results show that the proposed combined support system meets strength and stiffness requirements during roof construction and provides an efficient and practical solution for large-span conical shell silo roofs. Full article

(This article belongs to the Topic Advancing Construction Safety and Health: Innovations and Strategies)

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15 pages, 240 KB

Open AccessArticle

Productivity Pressure and Risk Perception Among a Multinational Construction Workforce in Saudi Arabia

by Wael M. Alruqi

Buildings 2026, 16(4), 774; https://doi.org/10.3390/buildings16040774 - 13 Feb 2026

Viewed by 298

Abstract

The Saudi construction industry relies heavily on a multinational workforce, raising safety concerns under high productivity demands. Although productivity pressure is widely assumed to influence workers’ risk perception and unsafe behavior, empirical evidence directly testing this assumption in culturally diverse construction settings remains [...] Read more.

The Saudi construction industry relies heavily on a multinational workforce, raising safety concerns under high productivity demands. Although productivity pressure is widely assumed to influence workers’ risk perception and unsafe behavior, empirical evidence directly testing this assumption in culturally diverse construction settings remains limited. This study examines whether perceived productivity pressure predicts risk perception among construction workers from different national backgrounds working on the same project. Survey data were collected from 247 construction workers representing five nationalities on a university construction site in Saudi Arabia. Correlation analysis, regression modeling, and linear mixed-effects models were used to assess the relationship between productivity pressure and risk perception while controlling for age and nationality. The results show that perceived productivity pressure does not significantly influence workers’ risk perception, and this relationship is not moderated by age or nationality. Although cultural adjustment differed significantly across national groups, nationality did not alter the pressure risk perception relationship. These findings challenge prevailing assumptions in construction safety research and suggest that productivity pressure may affect unsafe behavior through mechanisms other than cognitive risk appraisal. The study contributes empirical evidence from a controlled multicultural setting and highlights the need for safety interventions that extend beyond productivity pressure management to address decision-making processes, communication, and risk assessment competencies within multinational construction workforces. Full article

(This article belongs to the Topic Advancing Construction Safety and Health: Innovations and Strategies)

19 pages, 554 KB

Open AccessArticle

A Study on Unsafe Behaviors of Construction Workers Based on Personality Trait Theory

by Junwen Mo, Xiu Jia, Guizhang Li and Libing Cui

Appl. Sci. 2026, 16(1), 336; https://doi.org/10.3390/app16010336 - 29 Dec 2025

Viewed by 660

Abstract

The construction industry faces severe safety challenges with over 80% of accidents stemming from unsafe behaviors, yet traditional management overlooks the role of individual differences, and existing research fails to address the specific psychological mechanisms operative in this high-risk, dynamic environment. To effectively [...] Read more.

The construction industry faces severe safety challenges with over 80% of accidents stemming from unsafe behaviors, yet traditional management overlooks the role of individual differences, and existing research fails to address the specific psychological mechanisms operative in this high-risk, dynamic environment. To effectively curtail unsafe behaviors in such high-risk environments, this study aims to reveal the underlying mechanisms through which personality traits influence unsafe behaviors. Grounded in causal chain theory, the theory of planned behavior, and trait activation theory, this study constructs a hypothetical model of personality traits and unsafe behaviors, with fluke mentality serving as a mediating variable and safety climate as a moderating variable. A comprehensive approach combining questionnaire surveys, confirmatory factor analysis, correlation tests, and linear regression was employed to test the hypotheses. The results indicate that neuroticism, openness, and extraversion have significant positive effects on unsafe behaviors, while conscientiousness has a significant negative effect; agreeableness shows no significant influence. Fluke mentality plays a partial mediating role between personality traits and unsafe behaviors, while safety climate plays a negative moderating role. By clarifying the cognitive pathways of individual differences, this study enriches the theoretical framework of unsafe behavior research. The findings provide a theoretical basis for construction enterprises to optimize safety management from the perspective of individual differences, offering practical pathways to promote high-quality development in the construction industry. Full article

(This article belongs to the Topic Advancing Construction Safety and Health: Innovations and Strategies)

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17 pages, 2103 KB

Open AccessArticle

Preparation and Performance Evaluation of a Low-Fume Asphalt Binder

by Hongmei Cai, Rui Li, Yuzhen Zhang and Junrui Xiao

Infrastructures 2025, 10(9), 244; https://doi.org/10.3390/infrastructures10090244 - 16 Sep 2025

Cited by 2 | Viewed by 835

Abstract

Asphalt fume emissions cause significant environmental hazards during the preparation of hot-mix asphalt. In this study, experimental investigations were conducted employing a reactor vessel to simulate asphalt fumes under controlled conditions. Asphalt fumes were obtained through an integrated system comprising glass fiber filter [...] Read more.

Asphalt fume emissions cause significant environmental hazards during the preparation of hot-mix asphalt. In this study, experimental investigations were conducted employing a reactor vessel to simulate asphalt fumes under controlled conditions. Asphalt fumes were obtained through an integrated system comprising glass fiber filter cartridges and an impinger absorption bottle. Quantitative analysis was then conducted using gravimetric analysis and UV-Vis spectrophotometry. Through systematic monitoring of compositional changes in asphalt binder fractions, the fume emission characteristics during in-plant mixing operations were quantitatively correlated with the following processing parameters: temperature, airflow rate, and mixing duration. Comparative evaluation revealed optimal performance from a ternary compound inhibitor containing cuprous chloride, ditert-butylhydroquinone, and ferric chloride in mass proportions of 4:4:2. At a critical dosage of 0.6 wt%, this compound inhibitor demonstrated significant reduction in total particulate matter emissions without compromising asphalt binder properties. In addition, comprehensive performance characterization through rheological testing and thin-film oven aging (TFOT) showed that the modified low-fume asphalt binder maintained equivalent or improved performances compared to a conventional asphalt binder. Full article

(This article belongs to the Topic Advancing Construction Safety and Health: Innovations and Strategies)

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16 pages, 1673 KB

Open AccessArticle

Experimental Analysis of Steel–Concrete Bond Strength Under Varying Material and Geometric Parameters

by Gregor Trtnik, Jakob Šušteršič and Tomaž Hozjan

CivilEng 2025, 6(3), 48; https://doi.org/10.3390/civileng6030048 - 11 Sep 2025

Viewed by 1561

Abstract

This study presents the outcomes of a comprehensive experimental investigation focused on the bond behavior between reinforcing steel bars and tremie concrete, assessed through standardized pull-out tests. The objective was to evaluate the influence of some key parameters: reinforcement bar diameter, concrete age [...] Read more.

This study presents the outcomes of a comprehensive experimental investigation focused on the bond behavior between reinforcing steel bars and tremie concrete, assessed through standardized pull-out tests. The objective was to evaluate the influence of some key parameters: reinforcement bar diameter, concrete age (and associated compressive strength), steel fiber content, and a bentonite coating on rebar surfaces. Experiments were conducted under laboratory conditions according to relevant standards. Slip between the reinforcement and tremie concrete was measured using a sophisticated high-precision optical laser device, enabling accurate assessment of bond characteristics. A large, i.e., a statistically sufficient, number of specimens was tested, allowing the results to be analyzed using the ANOVA technique to determine the statistical significance of each parameter. The results show that, under most test conditions, the influence of the bentonite suspension coating on the bond strength was not statistically significant. Similarly, variations in the bar diameter and fiber content showed no statistically significant impact within the tested ranges. In contrast, concrete age (compressive strength) exhibited a statistically significant influence, confirming that concrete maturity is a dominant factor in bond development. The results contribute to a better understanding of the bond mechanisms in reinforced concrete and can assist in optimizing design strategies where bond performance is critical. Full article

(This article belongs to the Topic Advancing Construction Safety and Health: Innovations and Strategies)

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33 pages, 6933 KB

Open AccessReview

Enhancing Knowledge of Construction Safety: A Semantic Network Analysis Approach

by Yuntao Cao, Shujie Wu, Yuting Chen, Martin Skitmore, Xingguan Ma and Jun Wang

Buildings 2025, 15(17), 3036; https://doi.org/10.3390/buildings15173036 - 26 Aug 2025

Cited by 6 | Viewed by 1388

Abstract

The construction industry is recognized as high-risk due to frequent accidents and injuries, prompting extensive research and bibliometric analysis of construction safety. However, little attention has been given to the evolution and interconnections of key research topics in this field. This study applies [...] Read more.

The construction industry is recognized as high-risk due to frequent accidents and injuries, prompting extensive research and bibliometric analysis of construction safety. However, little attention has been given to the evolution and interconnections of key research topics in this field. This study applies semantic network analysis (SNA) to examine relationships and trends in construction safety research over the past 30 years. SNA enables quantitative exploration of topic interrelationships that is difficult to achieve with other approaches. Chronological network graphs are evaluated using the number of nodes, edges, density, average clustering coefficient, and average path length. Prominent topics are identified through degree, betweenness, and eigenvector centrality measures. The analysis combines a global overview of the main network, a chronological perspective, and local examination of clusters based on five macro keywords: accident, safety management, worker behavior, machine learning, and safety training. Results show a shift from traditional concerns with mortality and injuries to contemporary issues, such as safety climate, worker behavior, and technological innovations, including building information modeling, machine learning, and real-time monitoring. Topics with lower centrality scores are identified as under-researched. Overall, SNA offers a comprehensive view of the construction safety knowledge system, guiding researchers toward emerging topics and helping practitioners prioritize resources and design integrated safety risk strategies. Full article

(This article belongs to the Topic Advancing Construction Safety and Health: Innovations and Strategies)

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