Improved safety checklist analysis approach using intelligent video surveillance in the construction industry: a case study

Guo, Shengyu; Li, Jichao; Liang, Kongzheng; Tang, Bing

doi:10.1080/10803548.2019.1685781

Cited by 20 publications

(14 citation statements)

References 31 publications

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“…There is strong evidence provided by previous studies implying that safety monitoring is strongly related to safety performance (Abas et al, 2021;Chellappa et al, 2019;Khalid et al, 2021; ECAM 31,3 Moosa and Oriet, 2022). According to most studies, improving safety inspection and monitoring makes safety technologies more effective and promotes their use (Chellappa et al, 2019;Guo et al, 2017Guo et al, , 2021Okpala et al, 2021;Yap et al, 2021).…”

Section: Safety Inspection and Monitoringmentioning

confidence: 98%

Investigating the impact of emerging technologies on construction safety performance

Dobrucali

Demirkesen

Sadikoglu

et al. 2022

ECAM

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PurposeConstruction safety is heavily affected by using new technologies in this growing trend of technology adoption. Especially, safety performance is enhanced through the utilization of some effective technologies such as artificial intelligence, virtual reality, BIM and wearable devices. Therefore, the main purpose of this study is to investigate the influence of emerging technologies on construction safety performance and quantify the relationship between those. The proposed components of emerging technologies are BIM, GIS, VR, RFID, AI, ML, eye tracking and serious games and wearable devices, whereas the dimensions of construction safety performance are safety planning, safety training, safety inspection and monitoring, safety audits and reviews and safety leadership.Design/methodology/approachA structural model was composed consisting of emerging technologies and safety performance indicators. Then, a questionnaire was designed and administered to construction professionals, and data from 167 projects were analyzed using structural equation modeling. The data were analyzed by using software, called SPSS AMOS.FindingsThe analysis of the structural model proves that there is a positive and significant relationship between emerging technologies and construction safety performance. Moreover, the factor loadings for each factor were found to be high indicating a good representation of the construct by the components developed. Among the technologies, BIM, robotics and automation, AI and wearable devices were detected to be the most significant technologies in terms of impacting safety performance.Originality/valueThe study contributes to the body of knowledge in that it develops a conceptual framework consisting of specific technologies in terms of emerging technologies, reveals the impact of such technologies on safety performance and proposes several tools and strategies for enabling effective safety management along the project lifecycle. Industry practitioners may benefit from the framework developed by adopting such technologies to enhance their safety performance on construction projects.

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Section: Safety Inspection and Monitoringmentioning

confidence: 98%

Investigating the impact of emerging technologies on construction safety performance

Dobrucali

Demirkesen

Sadikoglu

et al. 2022

ECAM

View full text Add to dashboard Cite

show abstract

“…Safety checklists, on the other hand, offer a template that includes a list of pre-defined safety hazards to guide the hazard recognition process [41]. Unfortunately, such pre-defined lists are often not comprehensive of all possible safety hazards in dynamic work environments [42]. Moreover, past research has argued that the use of safety checklists can lead to tunnel vision-where the workers only look for safety hazards that are included as part of the safety checklist templates [43].…”

Section: Construction Hazard Recognition Interventionsmentioning

confidence: 99%

Hazard Recognition Patterns Demonstrated by Construction Workers

Uddin

Albert

Alsharef

et al. 2020

IJERPH

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Construction workers fail to recognize a large number of safety hazards. These unrecognized safety hazards can lead to unintended hazard exposure and tragic safety incidents. Unfortunately, traditional hazard recognition interventions (e.g., job hazard analyses and safety training) have been unable to tackle the industry-wide problem of poor hazard recognition levels. In fact, emerging evidence has demonstrated that traditional hazard recognition interventions have been designed without a proper understanding of the challenges workers experience during hazard recognition efforts. Interventions and industry-wide efforts designed based on a more thorough understanding of these challenges can yield substantial benefits—including superior hazard recognition levels and lower injury rates. Towards achieving this goal, the current investigation focused on identifying hazard categories that workers are more proficient in recognizing and others that they are less proficient in recognizing (i.e., hazard recognition patterns). For the purpose of the current study, hazards were classified on the basis of the energy source per Haddon’s energy release theory (e.g., gravity, motion, electrical, chemical, etc.). As part of the study, 287 workers representing 57 construction workplaces in the United States were engaged in a hazard recognition activity. Apart from confirming previous research findings that workers fail to recognize a disproportionate number of safety hazards, the results demonstrate that the workers are more proficient in recognizing certain hazard types. More specifically, the workers on average recognized roughly 47% of the safety hazards in the gravity, electrical, motion, and temperature hazard categories while only recognizing less than 10% of the hazards in the pressure, chemical, and radiation hazard categories. These findings can inform the development of more robust interventions and industry-wide initiatives to tackle the issue of poor hazard recognition levels in the construction industry.

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“…Compared with other combinations of unsafe behaviors, these four critical groups of unsafe behaviors reflect unsafe behaviors that have a greater impact on the four types of accidents and thus should be collaboratively controlled in building construction. Furthermore, as shown in Table 5, corrective measures are suggested for the unsafe behaviors in these groups, including safety inspection, safety training for workers and safety training for managers/engineers based on the previous work [43,44]. For the unsafe behaviors, such as engaging in a specialized operation without a permit, which will not cause accidents immediately, safety inspection should be strictly executed before and during construction tasks.…”

Section: Backward Reasoning For Identifying Critical Unsafe Behaviorsmentioning

confidence: 99%

Exploring the Impact of Unsafe Behaviors on Building Construction Accidents Using a Bayesian Network

Guo

et al. 2019

IJERPH

Self Cite

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Unsafe behavior is a critical factor leading to construction accidents. Despite numerous studies supporting this viewpoint, the process by which accidents are influenced by construction workers’ unsafe behaviors and the extent to which unsafe behaviors are involved in this process remain poorly discussed. Therefore, this paper selects cases from Chinese building construction accidents to explore the probabilistic transmission paths from unsafe behaviors to accidents using a Bayesian network. First, a list of unsafe behaviors is constructed based on safety standards and operating procedures. Second, several chains of unsafe behaviors are extracted from 287 accident cases within four types (fall, collapse, struck-by and lifting) to form a Bayesian network model. Finally, two accidents are specifically analyzed to verify the rationality of the proposed model through forward reasoning. Additionally, critical groups of unsafe behaviors leading to the four types of accidents are identified through backward reasoning. The results show the following: (i) The time sequence of unsafe behaviors in a chain does not affect the final posterior probability of an accident, but the accident attribute strength of an unsafe behavior, affects the growth rate of the posterior probability of an accident. (ii) The four critical groups of unsafe behaviors leading to fall, collapse, struck-by, and lifting are identified. This study is of theoretical and practical significance for on-site behavioral management and accident prevention.

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Improved safety checklist analysis approach using intelligent video surveillance in the construction industry: a case study

Cited by 20 publications

References 31 publications

Investigating the impact of emerging technologies on construction safety performance

Investigating the impact of emerging technologies on construction safety performance

Hazard Recognition Patterns Demonstrated by Construction Workers

Exploring the Impact of Unsafe Behaviors on Building Construction Accidents Using a Bayesian Network

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