Analysing Near-Miss Incidents in Construction: A Systematic Literature Review

Abstract

The construction sector is notorious for its high rate of fatalities globally. Previous research has established that near-miss incidents act as precursors to accidents. This study aims to identify research gaps in the literature on near-miss events in construction and to define potential directions for future research. The Scopus database serves as the knowledge source for this study. To identify publications on near-miss events, the search field “Article Title, Abstract, Keywords” was utilized with the keywords “construction” and “near miss”. The main research themes were defined based on keyword mapping performed using VOSviewer. Selected publications were assessed for their alignment with the defined research theme. A statistical analysis of the publications and the co-occurrence of keywords was conducted. The authors of the identified publications primarily used statistical analyses, artificial intelligence, employee monitoring, tracking systems, and building information modelling in their research. The conclusions from the literature review indicate a need for further research focused on developing effective predictive models for workplace accidents based on knowledge of near-miss events. This will contribute to a better understanding of the mechanisms leading to accidents and their prevention, ultimately resulting in a significant reduction in accidents in the construction sector.

1. Introduction

It is widely acknowledged that the construction industry one of the most hazardous industries in many countries around the world [1,2,3]. Construction sites are inherently hazardous locations [4,5] due to the inherent pressures of working under the constraints of delayed project completion, which are often further compounded by the adverse effects of inclement weather conditions. In comparison to other industries, construction presents a significant risk to the lives and health of workers, with a high incidence of accidents. Construction has the highest proportion of fatal accidents as a percentage of all industries. The statistical data on accident rates in construction worldwide serve to highlight a significant global problem. In Poland, for instance, preliminary data from the Central Statistical Office indicate that 3597 individuals sustained injuries in construction-related incidents in 2023, including 39 fatalities [6]. As a consequence of these accidents, the construction industry was obliged to take 167,486 days of sick leave. In the United States, the estimated financial impact of construction accidents in 2017 was USD 5 billion, comprising lost production, lost family income and pain, and reduced quality of life each year [7]. Global statistics indicate that the rate of fatalities and injuries in the construction industry is three times higher than the average for other industries, with the rate of injuries being twice as high [8].

The construction industry is characterised by a high incidence of accidents, underscoring the importance of a comprehensive understanding of the underlying causes. A detailed examination of the various factors contributing to accidents can inform the development of strategies to enhance safety in this sector. Near misses are regarded as potential precursors to accidents, and an understanding of them can facilitate a more comprehensive grasp of the underlying accident phenomenon. It is of particular importance to identify the topics and results of existing research in order to ascertain new avenues for investigation that have not yet been explored, with a view to reducing accidents on construction sites.

Recent research, such as the study by Amirah et al. [9], has demonstrated the considerable influence of safety behaviours on the development of a safety culture in sectors such as manufacturing. This is consistent with the seminal work of Herbert William Heinrich, who conducted important studies on industrial accidents, particularly his investigation into the causes of industrial accidents in the 1930s [10]. This study constituted the foundation for the theory known as the Heinrich pyramid, which posits that each accident is preceded by a substantial number of hazardous events in which workers do not sustain injuries, classified as “near misses”. A near miss is defined as a hazardous event related to the work being carried out that could have resulted in injury to a worker if the circumstances had been different [11]. Such incidents are far more prevalent than accidents, and under slightly altered circumstances could potentially culminate in an accident [12]. The research conducted by Tan and Li [13] provides further insight into the system of administrative accountability in China, emphasising the significance of governance and legal frameworks in enhancing safety outcomes. In order to reduce the number of accidents and thus improve the state of occupational safety in the construction industry, it is necessary to undertake a detailed analysis of near misses [14,15] in order to identify the underlying causes and to inform future prevention strategies. Based on the identified near misses and their causes, an assessment of the potential risk of future accidents can be conducted [16].

Near-miss events represent a significant source of knowledge, and the learning derived from such events constitutes an essential component of the maintenance of safe work systems. A deeper comprehension of the role of near-miss events in the occurrence of occupational accidents in construction can enhance the precision of predictions and the evaluation of occupational risks [17,18,19]. Nevertheless, this is only feasible if these incidents are meticulously documented and scrutinised, and the insights gleaned are disseminated to the workforce [20].

In view of the expansion of the construction industry, the elevated accident rate in this sector, and the acknowledgement by researchers of the influence of near misses on safety, there was a requirement to identify additional research avenues and thus present a synthesis of the current state of research on near misses. The authors’ proposed literature review represents the most recent summary of knowledge in this area.

The objective of the research presented in this article is to conduct a comprehensive literature review on near misses in the construction industry. This will entail identifying areas of research conducted by researchers to date and identifying development trends in this area of knowledge. Furthermore, the objective is to identify research gaps in this field of knowledge in order to suggest avenues for further research. The following research questions were formulated:

• Q₁—Are near-miss events in construction industry the subject of scientific research?
• Q₂—What methods have been employed thus far to obtain information on near misses and systems for recording incidents in construction companies?
• Q₃—What methods have been used to analyse the information and figures obtained?
• Q₄—What are the key aspects of near misses in the construction industry that have been of interest to the researchers?

The principal source of knowledge employed in the course of this research is the Scopus database. A total of more than two hundred references from the literature were identified that contained the keywords “construction” and “near-miss”. The publications identified through the search were evaluated for quality and relevance to the topic, and then classified into narrower research topics. The innovation of the research lies in its comprehensive and multifaceted summary of the current state of the art in the field of near-miss investigation in construction. The authors are unaware of any prior research in the construction industry that addresses this topic in a similar manner. The article identifies areas of knowledge regarding near misses in construction that have yet to be explored.

2. Definition of Near-Miss Events

The term “near miss” is defined in various ways in the literature. Such definitions can be found in scientific publications, in the standards set by companies with established occupational safety systems, in legislation, and in documents issued by occupational safety organisations.

Table 1. Definitions of near misses by institution.

No.	Name of Institution/Organization	Definition
1	Occupational Safety and Health Administration (OSHA) [21]	“A near-miss is a potential hazard or incident in which no property was damaged and no personal injury was sustained, but where, given a slight shift in time or position, damage or injury easily could have occurred. Near misses also may be referred to as close calls, near accidents, or injury-free events.”
2	International Labour Organization (ILO) [22]	“An event, not necessarily defined under national laws and regulations, that could have caused harm to persons at work or to the public, e.g., a brick that falls off scaffolding but does not hit anyone”
3	American National Safety Council (NSC) [23]	“A Near Miss is an unplanned event that did not result in injury, illness, or damage—but had the potential to do so”
4	PN-ISO 45001:2018-06 [24]	A near-miss incident is described as an event that does not result in injury or health issues.
5	PN-N-18001:2004 [25]	A near-miss incident is an accident event without injury.
6	World Health Organization (WHO) [26]	Near misses have been defined as a serious error that has the potential to cause harm but are not due to chance or interception.
7	International Atomic Energy Agency (IAEA) [27]	Near misses have been defined as potentially significant events that could have consequences but did not due to the conditions at the time.

presents a summary of the definitions of near misses provided by occupational safety and health institutions.

A common feature of the aforementioned definitions is the absence of any injury as resulting from the incident. This finding is also consistent with the definitions set forth by researchers in the field. Marks et al. [28] proposed a definition of a near miss as an unplanned event that could potentially result in illness, injury, or other forms of harm. Gnoni et al. [29] distinguished between distinct approaches to defining near misses. One category encompasses incidents that do not result in injury, whereas the other incorporates situations that could potentially lead to harm, including unsafe working conditions and unsafe behaviour. Furthermore, Thoroman et al. [30] posit that, in certain instances, such an incident may result in minor property damage. The question of which types of event can be classified as a near miss is still under discussion by researchers [31].

In accordance with Heinrich’s theory, there is a correlation between near misses and workplace accidents, which is presented as the so-called “Heinrich pyramid model” or “safety pyramid.” Heinrich posited that a decline in the occurrence of near misses would lead to a concomitant reduction in the incidence of occupational accidents and, consequently, in the prevalence of serious injuries [10]. In accordance with this theory, a fatal accident represents merely the overt aspect of the underlying problem, situated at the pinnacle of the pyramid. At the lower levels of the pyramid are incidents resulting in minor injuries and those without any injuries. The concept of occupational safety, as postulated by Heinrich, is predicated on the notion of risk management through the identification and elimination of incidents at the lower levels of the pyramid, with the objective of reducing accidents at the upper levels.

A comparable study was conducted by Bird [32]. He conducted an analysis of over 1,753,498 accidents documented in 297 U.S. companies across 21 distinct industrial sectors. For one fatal injury there were 10 minor injuries, 29 property damage incidents, and 600 non-injury incidents.

Zimmerman and Bauer [33] constructed a pyramid-shaped model based on the analysis of hazardous events recorded in various industrial sectors within the European Union. In the accident pyramid developed in 2006, for one fatal accident, there were 15 severe accidents resulting in disability, 30 incidents requiring hospitalisation of the injured, and 160 requiring medical treatment compared to 220 total recorded injuries. Figure 1 illustrates the safety pyramids proposed by Heinrich, Bird, Zimmerman, and Bauer.

Gnoni and Saleh [31], Zhou et al. [36], and also Chen et al. [37] emphasise the feature of near misses as a kind of precursor to accidents. Wright and Van der Schaaf base their research on the so-called common cause hypothesis, which posits that near misses and accidents exhibit similar relative causal patterns [38]. It can thus be concluded that the sequence of a near-miss event is very similar to that of an accident, except for a few missing elements [39]. The elimination of causes of near-miss events has the potential to positively impact the prevention of occupational accidents [29]. Nevertheless, there are those who oppose this theory. Mauele [40] posits that a dearth of scientific evidence exists to substantiate the assertion that near misses and accidents are causally linked. This is because unsafe activities can be performed on multiple occasions before an accident occurs, and this is not attributable to the occurrence of more incidents. However, Love and Tenekedjiev [41] posit that the emphasis should not be on enumerating near misses, but rather on elucidating the contextual factors that precipitated incidents.

3. Research Methodology

This study employs an exploratory research methodology, with the objective of conducting a comprehensive review of the literature on near-miss incidents within the construction industry. The study’s scope encompasses the identification of publication trends, an examination of methodologies for incident data collection, an assessment of research tools utilised, and the delineation of prior research areas. The research was conducted in seven stages, which are outlined in Figure 2.

Step 1: The initial step is to create, based on keywords, a database of scientific publications on near misses in construction. To this end, the online database Scopus, which is widely used for scientific analysis [42], was employed. A list of publications was created using the following keyword sequence in the “Article title, Abstract, Keywords” search field: near misses or near misses, construction or construction industry. A search conducted in accordance with the specified criteria yielded 222 publications, published between 1985 and 2023. Subsequently, the search was refined to include only articles written in the English language. The following categories of publications were included in the analysis: articles, conference papers, books, and book chapters. The database was then subjected to further filtration, with reviews, conference reviews, errata, letters, short surveys, and retracted items removed. A total of 195 publications were subjected to analysis.

Step 2: The second stage is to conduct a preliminary evaluation of the selected publications in order to ascertain their quality and compliance with the research topic. A selection of publications was made on the basis of an analysis of titles and abstracts, which enabled the exclusion of articles that were not pertinent to the research topic. Subsequently, publications that were responses to review were excluded, as were those in which the keyword near miss was only mentioned, without being the subject of the research. In accordance with the aforementioned criteria, 75 publications that address near misses in construction were retained, which are shown in Appendix A,

Table A1. Database of 75 publications including year of publication, journal name, and doi number.

Year	Source Title	DOI/ISBN/ISSN	Reference
1999	Construction Management and Economics	10.1080/014461999371691	[98]
2002	Structural Engineer	14665123	[99]
2009	Building a Sustainable Future—Proceedings of the 2009 Construction Research Congress	10.1061/41020(339)4	[100]
2010	Safety Science	10.1016/j.ssci.2010.04.009	[14]
2010	Automation in Construction	10.1016/j.autcon.2009.11.017	[57]
2010	Safety Science	10.1016/j.ssci.2009.06.006	[12]
2012	Journal of Construction Engineering and Management	10.1061/(ASCE)CO.1943-7862.0000518	[90]
2013	ISARC 2013—30th International Symposium on Automation and Robotics in Construction and Mining, Held in Conjunction with the 23rd World Mining Congress	10.22260/isarc2013/0113	[101]
2014	Proceedings of the Institution of Civil Engineers: Civil Engineering	10.1680/cien.14.00010	[103]
2014	Safety Science	10.1016/j.ssci.2013.12.012	[55]
2014	Journal of Construction Engineering and Management	10.1061/(ASCE)CO.1943-7862.0000795	[102]
2014	31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014—Proceedings	10.22260/isarc2014/0115	[58]
2014	Construction Research Congress 2014: Construction in a Global Network—Proceedings of the 2014 Construction Research Congress	10.1061/9780784413517.0181	[54]
2014	Construction Research Congress 2014: Construction in a Global Network—Proceedings of the 2014 Construction Research Congress	10.1061/9780784413517.0235	[28]
2014	Construction Research Congress 2014: Construction in a Global Network—Proceedings of the 2014 Construction Research Congress	10.1061/9780784413517.0096	[17]
2015	Automation in Construction	10.1016/j.autcon.2015.09.003	[45]
2015	32nd International Symposium on Automation and Robotics in Construction and Mining: Connected to the Future, Proceedings	10.22260/isarc2015/0062	[60]
2015	ASSE Professional Development Conference and Exposition 2015	-	[104]
2015	Congress on Computing in Civil Engineering, Proceedings	10.1061/9780784479247.019	[66]
2016	Automation in Construction	10.1016/j.autcon.2016.03.008	[47]
2016	Automation in Construction	10.1016/j.autcon.2016.04.007	[67]
2016	IEEE IAS Electrical Safety Workshop	10.1109/ESW.2016.7499701	[105]
2016	Journal of Construction Engineering and Management	10.1061/(ASCE)CO.1943-7862.0001100	[87]
2016	Safety Science	10.1016/j.ssci.2015.11.025	[16]
2016	Journal of Construction Engineering and Management	10.1061/(ASCE)CO.1943-7862.0001049	[65]
2016	IEEE Transactions on Industry Applications	10.1109/TIA.2015.2461180	[109]
2017	Safety Science	10.1016/j.ssci.2017.06.012	[93]
2017	ENR (Engineering News-Record)	8919526	[108]
2017	6th CSCE-CRC International Construction Specialty Conference 2017—Held as Part of the Canadian Society for Civil Engineering Annual Conference and General Meeting 2017	978-151087841-9	[89]
2017	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	10.1007/978-3-319-72323-5_12	[106]
2017	Journal of Construction Engineering and Management	10.1061/(ASCE)CO.1943-7862.0001209	[107]
2017	Safety Science	10.1016/j.ssci.2016.08.027	[68]
2017	Safety Science	10.1016/j.ssci.2016.08.022	[18]
2018	Safety Science	10.1016/j.ssci.2018.04.004	[110]
2018	International Journal of Construction Management	10.1080/15623599.2017.1382067	[111]
2018	Journal of Construction Engineering and Management	10.1061/(ASCE)CO.1943-7862.0001420	[86]
2018	Proceedings of SPIE—The International Society for Optical Engineering	10.1117/12.2296548	[19]
2019	Automation in Construction	10.1016/j.autcon.2019.102854	[34]
2019	Physica A: Statistical Mechanics and its Applications	10.1016/j.physa.2019.121495	[36]
2019	Sustainability (Switzerland)	10.3390/su11051264	[73]
2019	Computing in Civil Engineering 2019: Data, Sensing, and Analytics—Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019	978-078448243-8	[64]
2019	Journal of Health, Safety and Environment	18379362	[112]
2019	Computing in Civil Engineering 2019: Data, Sensing, and Analytics—Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019	978-078448243-8	[63]
2019	Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience—Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019	10.1061/9780784482445.026	[85]
2019	Journal of Construction Engineering and Management	10.1061/(ASCE)CO.1943-7862.0001582	[75]
2019	Advances in Intelligent Systems and Computing	10.1007/978-3-030-02053-8_107	[74]
2020	Accident Analysis and Prevention	10.1016/j.aap.2020.105496	[113]
2020	Advanced Engineering Informatics	10.1016/j.aei.2020.101062	[72]
2020	Advanced Engineering Informatics	10.1016/j.aei.2020.101060	[53]
2020	ARCOM 2020—Association of Researchers in Construction Management, 36th Annual Conference 2020—Proceedings	978-099554633-2	[71]
2020	International Journal of Building Pathology and Adaptation	10.1108/IJBPA-03-2020-0018	[114]
2020	Communications in Computer and Information Science	10.1007/978-3-030-42852-5_8	[88]
2021	Journal of Architectural Engineering	10.1061/(ASCE)AE.1943-5568.0000501	[115]
2021	Safety Science	10.1016/j.ssci.2021.105368	[97]
2021	ACM International Conference Proceeding Series	10.1145/3482632.3487473	[116]
2021	Reliability Engineering and System Safety	10.1016/j.ress.2021.107687	[82]
2021	Proceedings of the 37th Annual ARCOM Conference, ARCOM 2021	-	[70]
2022	Buildings	10.3390/buildings12111855	[117]
2022	Safety Science	10.1016/j.ssci.2022.105704	[31]
2022	Sensors	10.3390/s22093482	[81]
2022	Proceedings of International Structural Engineering and Construction	10.14455/ISEC.2022.9(2).CSA-03	[56]
2022	Journal of Information Technology in Construction	10.36680/j.itcon.2022.045	[83]
2022	Forensic Engineering 2022: Elevating Forensic Engineering—Selected Papers from the 9th Congress on Forensic Engineering	10.1061/9780784484555.005	[118]
2022	Computational Intelligence and Neuroscience	10.1155/2022/4851615	[84]
2022	International Journal of Construction Management	10.1080/15623599.2020.1839704	[61]
2023	Journal of Construction Engineering and Management	10.1061/JCEMD4.COENG-13979	[78]
2023	Heliyon	10.1016/j.heliyon.2023.e21607	[119]
2023	Accident Analysis and Prevention	10.1016/j.aap.2023.107224	[77]
2023	Safety	10.3390/safety9030047	[120]
2023	Engineering, Construction and Architectural Management	10.1108/ECAM-09-2021-0797	[121]
2023	Advanced Engineering Informatics	10.1016/j.aei.2023.101929	[76]
2023	Engineering, Construction and Architectural Management	10.1108/ECAM-05-2023-0458	[80]
2023	Intelligent Automation and Soft Computing	10.32604/iasc.2023.031359	[62]
2023	International Journal of Construction Management	10.1080/15623599.2020.1847405	[79]
2024	Heliyon	10.1016/j.heliyon.2024.e26410	[3]

.

Step 3: Conduct a statistical analysis of the publications contained in the database, with a view to determining the number of papers published in successive years of the time interval studied, the country in which the topic was studied, and the journals in which the papers were published.

Step 4: The fourth stage of the process involves the categorisation of the publications in accordance with the specified keywords. A thematic breakdown of the publications was carried out using VOSviewer. This tool is employed for the mapping and analysis of trends and patterns in the scientific literature [12]. VOSviewer employs network analysis techniques to generate visual representations of the relationships between keywords. The map depicts nodes, which represent keywords, and lines indicate their co-occurrence. The proximity and connections between nodes signify thematic relationships. The analysis was conducted on both indexed and authored keywords.

Step 5: A comprehensive examination of individual publications, encompassing the methodologies employed for the acquisition of information on near misses, the research instruments utilized, the techniques of data analysis, and the extent of research conducted. A review of the literature on near misses in the construction sector, based on an analysis of publications listed in the Scopus database, has identified key aspects of past research in this field. The following aspects of past research on near misses in the construction sector have been defined:

-

methods of obtaining information on incidents, including reporting and recording incidents;

-

real-time monitoring of workers;

-

methods of data analysis;

-

thematic areas of research.

Step 6: A discussion of the findings and an identification of research gaps.

Step 7: A summary and discussion prospective avenues for future research directions.

4. Results

4.1. A Statistical Analysis of Publications

Figure 3 illustrates the distribution of articles retrieved from the Scopus database between the years 1999 and 2023. From 1999 to 2013, the total number of publications did not exceed eight. However, since 2014, there has been a notable increase in interest in the topic of near misses in construction. The greatest number of papers, amounting to nine, were published in 2019.

Figure 4 demonstrates the global interest among researchers in near misses and their implications for construction safety.

The United States of America demonstrates the most significant interest in this subject, followed by China, the United Kingdom, Australia, Hong Kong, and Germany.

Table 2. Journals of publication.

No.	Journal	Number of Publications
1	Safety Science	10
2	Journal of Construction Engineering and Management	8
3	Automation in Construction	5
4	Advanced Engineering Informatics	3
5	Construction Research Congress 2014 Construction in a Global Network Proceedings of the 2014 Construction Research Congress	3
6	International Journal of Construction Management	3
7	Accident Analysis and Prevention	2
8	Computing in Civil Engineering 2019 Data Sensing and Analytics Selected Papers From The ASCE International Conference	2
9	Engineering Construction and Architectural Management	2
10	Heliyon	2

provides a summary of articles on near misses published in the following journals. The table includes only those journals that have published at least two articles on this topic.

The highest number of articles was published in Safety Science (10), followed by The Journal of Construction Engineering and Management (8), and Automation in Construction (5). Three publications each were made in Advanced Engineering Informatics, Construction Research Congress 2014, and The International Journal of Construction Management.

The term “index terms” is used to describe the phrases that are assigned to 80% of titles in the Scopus database [43]. The terms are drawn from thesauri that are either owned or licensed by Elsevier. The objective of their utilisation is to enhance the efficacy of search results. A team of professional indexers, utilising controlled vocabularies, is responsible for the assignment of these terms to publications. In contrast, author keywords are terms or phrases selected by the authors of scientific publications themselves to describe the main research problems, methodologies, or key issues addressed in the article. Figure 5 illustrates the mapped indexed and author keywords. A minimum of five occurrences of a given keyword is required.

The analysis of the keyword maps has revealed a number of leading themes and connections, which point to a number of potential areas for further research. Four thematic clusters were identified and are presented in

Table 3. Keywords in the clusters.

Cluster Number	Colour	Basic Keywords
1	blue	construction, construction sites, decision making, machine learning, near misses, neural networks, project management, safety, workers
2	green	building industry, construction industry, construction projects, construction work, human, near miss, near misses, occupational accident, occupational safety, safety, management, safety performance
3	red	accident prevention, construction equipment, construction, safety, construction workers, hazards, human resource management, leading indicators, machinery, occupational risks, risk management, safety engineering
4	yellow	accidents, risk assessment, civil engineering, near miss, surveys

, with each cluster colour-coded in blue, green, red, and yellow.

4.2. Methods Used to Obtain Information about Near Misses

The techniques employed to ascertain data regarding near-miss incidents are categorised into two distinct categories: traditional methods and real-time monitoring systems. This is illustrated in Figure 6.

4.2.1. Traditional Methods

The documentation of near-miss incidents is of paramount importance for the enhancement of safety within an organisational context [44]. The content of near-miss incidents is a topic of considerable debate among researchers and company representatives [45]. In particular, such reporting systems should be based on the principles of voluntariness and confidentiality, as this is the only way to collect valuable and reliable information on occupational safety [46]. Raviv et al. [18] identified a deficiency in the methodology employed for the technical investigation of the content of reported near-miss incidents. It is of great importance that accurate incident reports are compiled in order to draw valid conclusions regarding safety. Such reports should include comprehensive details pertaining to the incident in question, including, if feasible, an identification of the underlying causes [47]. As a consequence of technological advances, near-miss recording systems have evolved from paper forms to computerised records and various forms of real-time employee tracking. Such systems are designed to accommodate evolving organisational requirements and capabilities.

• Traditional registration forms

The utilisation of paper-based forms for the documentation of near misses represents a conventional approach to the reporting of such incidents. Upon completion, the forms are scanned and stored in directories [48]. However, this system presents several disadvantages, including the unavailability of real-time safety information. The forms are not automatically archived, and the process of recording the event and placing it in the appropriate directory is a laborious one. The level of safety within the organisation is subject to interpretation and therefore open to subjectivity when assessed on the basis of such reports. Furthermore, the interpretation of the supervisor may vary, thus affecting the overall assessment of the level of safety within the organisation [49].

• Computerized systems for the recording of events

The utilisation of contemporary information technologies facilitates the identification and documentation of near misses [14,45]. A considerable number of companies have opted to utilise mobile applications for the purpose of event reporting. An exemplar of a near-miss form developed as iAuditor by SafetyCulture [50] includes the date of occurrence and report, location, description of the event, description of the property damage, a photograph confirming the occurrence of the event, observations and comments of the reporter, witness statements, and details of the person involved in the event, such as job type, years of experience, hours worked that day, shift pattern, and relevant training or qualifications.

The Safety Reporting System [51] provides employees with the ability to report incidents, generate reports, and track corrective actions. The application enables the generation of reports based on all submitted incidents. The system is capable of analysing a range of data, including the time and location of the event, a description of the incident, any relevant photographs, the consequences of the incident and the factors that may have contributed to it. The system has been developed by Align Technologies. The recording of incidents may be conducted via a smartphone, tablet, or computer.

Zou et al. [48] have developed a cloud-based system for the purpose of recording safety information. The system employs a geographic information system (GIS) and mobile technologies to develop the Safety Information Management System (MapSafe). MapSafe facilitates the aggregation, archiving, and visualisation of safety-related data within the context of construction projects. The data are entered into the system via a variety of forms. The forms utilized for this purpose are the Pre-Start Meeting Record (PSM) form, Permit to Enter (PTO) form, Job Safety Analysis (JSA) form, and Safety Incident Report (SIR) form. The information stored in the forms can be displayed on a map or through Building Information Modelling (BIM) projects uploaded to the system. To ensure the maintenance of chronological order, the system automatically assigns a form number and provides a hyperlink to the form. The user is required to input data regarding the location and time of the incident, as well as details pertaining to the circumstances surrounding its occurrence. Upon submission of the form to the system, it is automatically generated as a PDF and transmitted via email to the designated supervisor and security department personnel.

Wu et al. [52] developed a cloud-based application that employs sensors and Internet of Things (IoT) devices. The system was designed with the objective monitoring the health status of workers and the environmental parameters of the workplace in real time. The sensors collect data pertaining to various environmental and physiological parameters, including ambient temperature, relative humidity, UV index, carbon dioxide levels, body temperature, and heart rate. These sensors, known as the safety node and the health node, are attached to the worker’s helmet and body, respectively. The system is capable of distinguishing between indoor and outdoor locations. In the event of a hazardous situation, the system triggers an alarm. The Internet of Things (IoT) gateway functions as a standalone local server, facilitating the interconnection of the local sensor network with the cloud infrastructure. Additionally, it serves as a local server for users and performs edge computing.

In a study based on safety report data from several construction sites for the extension of metro and railway networks in China, Fang et al. [53] investigated the potential risks associated with such projects. The data were entered into an online system by engineers with extensive experience in documenting safety-related events. Information pertaining to near-miss incidents was duly recorded, encompassing the following details: location, time, name, description, safety level, categories, and photographs. A total of three instances of potential danger were classified into 170 distinct categories. Examples of these categories include steel processing and fabrication, the quality of the main concrete structure, personal protective equipment, and the working environment in the foundation excavation.

• Surveys and interviews

Furthermore, surveys and interviews can serve as a valuable source of information regarding near misses. For example, Gatti et al. [54] employed surveys on construction sites in Seattle to examine the impact of sleep deprivation on the incidence of hazardous incidents associated with the operation of heavy machinery and the utilisation of personal protective equipment. Similarly, Hon et al. [55] conducted surveys on construction sites in Hong Kong. The analysis of the results showed a relationship between the safety climate and the frequency with which workers reported near-miss incidents. Consequently, the interviews conducted by Martin et al. [56] with construction workers in Trinidad helped to understand how adequate knowledge and use of personal protective equipment affected the reduction in near misses. A survey of construction experts was also conducted in Malaysia with the objective of determining the causes of near misses and accidents [56].

4.2.2. Real-Time Monitoring Systems

The utilisation of real-time monitoring of workers is now one of the most prevalent strategies employed to enhance safety in construction, entailing the direct observation of the construction site. The objective is to promptly alert personnel to potential hazards, thereby preventing numerous dangerous incidents. Real-time monitoring systems employ a range of technologies, including ultrasonic technology, radio-frequency identification (RFID), inertial measurement units (IMUs), real-time location systems (RTLSs), industrial cameras, wearable technology, motion sensors, advanced information technology, and numerous others.

• Employee-tracking systems

Wu et al. [57] developed an advanced worker-tracking system based on ultrasonic and RFID technology, which monitors near misses on construction sites in real time. The system integrates environmental monitoring, access control, and worker safety, enabling a proactive response to near misses and thus enhancing overall safety. Aria et al. employed data from an inertial measurement unit (IMU) sensor and a Laplacian support vector machine algorithm to monitor the movements of steelworkers. Hasanzadeh and Garza [58] concentrated on the utilisation of worker motion-tracking systems to examine alterations in risk-taking behaviours and near misses. Teizer and Cheng [45] and Golovina et al. [47] developed a real-time worker location-tracking system for construction sites, which enables the automatic collection and analysis of data on potential hazards. The identification of spatiotemporal conflicts between pedestrian workers and potential hazards enables the implementation of an expedient response to near-miss scenarios.

Teizer [59] developed a magnetic field proximity detection technology with the objective of monitoring and warning workers about dangerous proximity to equipment. The implementation of this technology has led to a notable enhancement in construction site safety, as it is capable of generating alerts in response to the detection of magnetic signals.

In contrast, Li et al. [16] utilised real-time location systems (RTLSs) to gather data on the movement patterns of pedestrian workers, with the objective of developing algorithms that could anticipate potential hazards and identify high-risk areas.

• Video surveillance systems

Video surveillance can be employed for the purpose of recording and subsequent analysis of employee behaviours that may potentially result in near misses. The essential elements of a video surveillance system are cameras positioned in different locations on the construction site. The progressive advancement of information technologies, including those based on artificial intelligence, provides enhanced capabilities for the analysis of recorded events.

Mohajeri et al. [60] employed visual technologies to record employee behaviours, subsequently analysing the data using Association Rules and Risk Matrices. The results enabled the identification of key risk factors, such as habits, motivation, and perceived control, which were found to significantly impact safety.

In contrast, Kisaezehra et al. [61] devised a surveillance system based on the You Only Look Once (YOLOv5) architectural framework to ascertain whether employees were utilising protective headgear. It was highlighted that this is of particular significance in developing countries where safety standards may be less rigorous. The real-time analysis of images facilitated by this system represents an effective contribution to the prevention of occupational accidents and near misses.

In a notable contribution to the field, Li and Ding [62] devised a system that is capable of automatically detecting near misses, such as falling objects, and subsequently creating a database that can be leveraged to enhance the efficacy of safety training methodologies. By means of continuous monitoring and analysis, this system serves to enhance preventive practices on construction sites. Jeelani et al. [63] employed the use of head-mounted cameras to capture images from a first-person perspective (FPV) for the purpose of monitoring workers. The utilisation of three-dimensional point clouds enables the identification of potential hazard locations in real time, the documentation of worker behaviours, and the monitoring of near misses. The acquisition of this information has enabled the implementation of more effective training programmes and an enhancement of workplace safety.

• Wearable technology

The term “wearable technology” is used to describe clothing and personal accessories that incorporate advanced computer and electronic technologies. Wearable technology is employed in a variety of applications, including wearable cameras, smart clothing, activity trackers, smartwatches, and others. Furthermore, the technology offers numerous advantages in the domain of occupational safety.

Arslan et al. [34] implemented the Worker Trajectory Analysis System (WoTAS), which employs Bluetooth Low Energy (BLE) beacons to track worker trajectories, thereby enabling the real-time collection and analysis of data on worker movements. The Semantic Trajectories for Dynamic Environments (STriDE) ontology model, in conjunction with a Hidden Markov Model (HMM), facilitated the identification of potentially hazardous areas and events.

Similarly, Zhao et al. [19] employed the use of smartphones with integrated sensors to record the kinetic changes associated with the movement of workers. The data were subjected to analysis using an artificial neural network (ANN) in order to identify situations that were conducive to falls. Lim et al. [64] employed the use of smart wearable devices, such as smartphones equipped with a three-axis accelerometer, to monitor the movements of workers on a simulated construction site. The data collected by the devices were processed by an ANN, which enabled the identification of near misses and the exact time of their occurrence.

Furthermore, Kanghyeok Yang et al. [17] employed IMUs to gather data on workers’ movements and attitudes, thereby enabling the precise monitoring of typical activities and the identification of near misses.

• Motion sensors

Aria et al. [65] employed the use of motion sensors in the form of IMUs to facilitate the automatic detection of near-miss events in metallurgical movements. This was achieved through the training of a semi-supervised algorithm based on a Laplacian support vector machine. The algorithm demonstrated a classification accuracy of over 98%.

Similarly, K. Yang et al. [66] employed analogous IMU sensors attached to the sacrum of workers to gather kinematic data while traversing a steel structure. The determination of threshold values was achieved through the utilisation of features such as the sum vector magnitude and the normalised signal magnitude area (SMA), as evidenced by the experimental findings.

Kanghyeok Yang et al. [67] also employed wearable inertial measurement units (WI-MUs) to collect kinematic data, which was then analysed using a single-class support vector machine. This method allowed for the automatic detection of potential accident events such as slips and trips. This approach enabled the identification of at-risk workers without disrupting their work activities.

4.3. Methods Used to Analyse the Information and Figures That Have Been Obtained

The most prevalent analytical techniques employed in the examination of near-miss incidents are quantitative statistical methods, which utilise statistical inference procedures, and qualitative techniques, which draw upon existing near-miss incident reports as a foundation for a range of analytical approaches. In recent years, research into the potential applications of artificial intelligence (AI) in occupational safety has grown significantly. Furthermore, Building Information Modelling (BIM) technology has transformed safety management on construction sites, providing enhanced tools for the identification and management of hazardous events. Figure 7 illustrates the analytical techniques for near-miss data, as outlined in the article.

4.3.1. Quantitative and Qualitative Statistical Methods

The analysis of accumulated near-miss data typically entails a combination of quantitative and qualitative statistical techniques. The primary objective of quantitative analysis is to utilise statistical inference techniques in order to obtain results that can be generalised to the entire study population.

A qualitative analysis [18] and structural analysis [68] are often conducted in parallel. An example of the use of qualitative and quantitative analysis is the study conducted by Saurin et al. [69]. The study employed incident reporting systems (IRSs) based on semi-structured interviews, participant observation, direct observation at construction sites, and document analysis. Additionally, a quantitative analysis of 946 reported near misses and occupational accidents at 16 construction sites was conducted, classifying them by type, severity, duration, and number of reports. The results of the study have the potential to yield benefits in three key areas regarding the enhancement of construction safety: (1) The analysis of incident reporting systems (IRSs) revealed insights that can inform further investigation of existing data; (2) A set of IRS design guidelines was developed based on these insights; and (3) The quantitative analysis of incident reports constituted a significant contribution in its own right, given the dearth of empirical research on near misses. It is anticipated that these findings will prove beneficial to those engaged in the design and management of incident reporting systems within the construction industry. Additionally, Williams et al. [70] conducted a qualitative analysis of a substantial data set (3519 reports). The objective of the study was to enhance the near-miss reporting system and identify deficiencies within the system.

Temporal and spatial data are also analysed [34], making it possible to identify locations characterized by the risk of a hazardous event [47]. The data are subjected to automated analysis in order to ascertain the degree of danger, which is subsequently presented in the form of a heat map.

Wu et al. [14] used accident precursor analysis in their study. They used historical accident records compiled from reported incidents to identify precursors and immediate factors (PaIFs) at construction sites and obtain information on mitigation measures. The findings of the research led to the development of a systematic mechanism for the disruption and prevention of precursors and immediate factors at construction sites.

An analysis of system failures was conducted by Baker et al. [71]. The study employed a data-driven approach to analyse texts describing the failure events. The data were processed using natural language processing (NLP) and machine learning.

Based on historical data and expert knowledge, Jin et al. [72] conducted an event probability analysis, including an evaluation of potential consequences. The study employed stochastic methodologies to evaluate the probability of accidents and their potential consequences. This enabled the modelling and prediction of hazardous events in construction projects.

Mohajeri et al. [60] employed a causal analysis to examine the underlying causes of hazardous behaviours that contribute to fall hazards on construction sites. In order to achieve this, video footage was employed to record and subsequently analyse the unsafe behaviours exhibited by workers. To verify the identified causal patterns, the association rules method and a risk matrix were employed. The findings revealed that the primary factors influencing unsafe worker behaviours are habits, motivation, perceived behavioural control, and subjective norms.

Zhou et al. [73] developed an eight-stage process for the management of near-miss events, comprising the following stages: discovery, reporting, identification, prioritisation, causal analysis, resolution, dissemination, and evaluation. The research revealed that occupational accidents and near-miss events in construction projects display analogous causal patterns. Given the high frequency of near-miss events and the analogous causal factors involved, learning from these situations represents an effective proactive measure for accident prevention and enhancing overall safety on construction sites.

4.3.2. Analysis Using Artificial Intelligence (AI)

In recent years, research involving the application of artificial intelligence (AI) for the prevention of near-miss events on construction sites has made significant advances.

Baker et al. [71] employed machine learning algorithms to analyse safety data, thereby facilitating the identification of patterns and the prediction of potential hazards. A natural language processing (NLP) model was developed for the automatic identification of key event attributes.

Boateng et al. [74] concentrated their efforts on the construction of artificial neural networks capable of forecasting the safety levels observed on construction sites, based on the data collected from operational sources.

Zhang et al. [75] explored the potential of smartphones with integrated sensors as a means of data collection. They demonstrated that, when coupled with an ANN, these devices can effectively detect and identify hazardous situations involving falls. By analysing data from accelerometers, the system is able to identify precise motion patterns that could result in an accident, which is crucial for enabling early intervention and enhancing safety on construction sites.

A comparable methodology was proposed by Lim et al. [64], who devised a system for categorising near misses, such as slips and trips, through the utilisation of an artificial neural network (ANN). The system incorporates data from smart sensors and an SSN system. The system has been trained to recognise near misses on construction sites. The study concentrated on the collection of data utilising a tri-axial accelerometer embedded in a smartphone, thereby enabling precise real-time monitoring and energy analysis of worker movements.

Gadekar and Bugalia [76] developed a technology for the automatic classification of safety reports, which allows for faster responses to reports of hazardous situations. Zhu et al. [77] and Fang et al. [53] concentrated on text mining as a means of analysing safety reports, thus facilitating a more profound comprehension of the context and causes of potential hazards. To enhance the analysis of reports on near misses on construction sites, an advanced approach based on deep learning utilising Bidirectional Encoder Representations from Transformers (BERT) was developed. This approach enables the efficient and accurate automatic text classification.

Jin et al. [72] used advanced stochastic analysis techniques and time series modelling. The researchers employed Markov copula methods and Poisson modelling to assess risk, thereby enabling the dynamic updating of accident probabilities based on historical data accumulation and expert knowledge. The Markov copula model represents a methodology for time series modelling that employs copulas to flexibly and accurately model dependencies between variables. The copula model permits the separation of the modelling of marginal distribution functions from the dependencies between variables, with parameters determined analytically from autocorrelation functions. This approach facilitates the inference of probability and enables a natural extension to higher-order Markov models. Hypothesis testing based on the Probability Integral Transformation Theorem (PITT) is employed to assess the adequacy of the model for near-miss events.

Li et al. [78] introduced an innovative blockchain-based system for creating an automatic monitoring and early warning system at construction sites. This system enables more efficient management of near misses and accident prevention. Blockchain is a digital system of data records. The utilisation of blockchain’s intrinsic advantages, namely decentralisation and the impossibility of manipulation, has resulted in a reduction in the time required to detect accident-prone events and an enhancement in the reliability and practicality of safety monitoring.

Chung et al. [79] integrated artificial intelligence with Internet of Things (IoT) technology, creating a real-time monitoring system that identifies potential hazards.

Kisaezehra et al. [61] employed the YOLOv5 deep learning model to monitor the utilisation of protective helmets by construction workers, thereby enhancing safety through the automation of personal protective equipment checks.

Liu et al. [80] employed sophisticated image processing techniques, including three-dimensional pose estimation, for the contactless detection of worker falls. The proposed solution enables the early identification of near misses on construction sites.

Abbasi et al. [81] applied Convolutional Neural Networks (CNNs) in conjunction with sound recognition techniques to identify hazards related to impacts from construction equipment.

Raviv et al. [18] and Wang et al. [82] employed machine learning techniques to analyse data pertaining to near misses associated with tower cranes, as well as to develop advanced time series modelling.

In a recent study, Li and Ding [62] designed a video surveillance system that utilizes deep learning technology to automatically detect and analyse the causes of near misses. Bugalia et al. [83] applied machine learning (ML) to analyse safety reports, demonstrating how n-gram modelling techniques and sensitivity analysis can enhance decision-making processes on construction sites. Chen et al. [84] utilized Convolutional Neural Networks (CNNs) for the classification of safety records, supporting the identification and management of potential hazards.

Jeelani et al. [63] employed image processing algorithms and machine learning in the Bag of Words (BoW) localisation method for the analysis and classification of visual data. The AI system was able to assist in the accurate estimation of distances from potential hazards and the identification and recording of unsafe behaviours among workers.

Kanghyeok Yang et al. [67] developed a method using one-class support vector machines (OCSVM) to analyse data from Wireless Inertial Measurement Units (WIMUs). Their AI algorithm enables the identification of near misses, such as falls.

Zhao et al. [19] applied an ANN to analyse data from motion sensors in smartphones, which monitor changes in energy during worker movement. Their system enables real-time detection of various types of potentially hazardous falls and allows for proactive warnings to workers about dangers, thereby minimizing the risk of actual accidents on construction sites.

Tang et al. [85] utilized artificial intelligence in the form of Long Short-Term Memory (LSTM) networks to forecast the movements of workers and construction equipment, analysing previously observed trajectories. Their model incorporated contextual cues such as object placements and type attributes, enabling effective short-term forecasting.

Rashid and Behzadan [86] utilized polynomial regression and Hidden Markov Models (HMMs) to forecast the trajectories of worker movements. These advanced artificial intelligence techniques enabled early detection of near misses, thereby enhancing safety on construction sites.

4.3.3. Building Information Modelling

The advent of building information modelling has brought about a revolutionary change in the way safety is managed on construction sites. The utilisation of BIM has equipped construction professionals with enhanced tools to more effectively identify and manage near misses. Shen and Marks [87] described how BIM enables the collection, analysis, and visualization of data on potential hazards, allowing construction personnel to more easily report and visualize dangerous situations. The technology is also used to create personalized safety reports. Golovina et al. [47] have used BIM to automatically generate personalized safety reports that integrate computationally derived hazard values with actual building information models.

In addition, Erusta and Sertyesilisik [88] showed how BIM can improve efficiency and safety on construction sites by identifying and eliminating the causes of accidents and near misses, treating them as “waste” in light of lean management principles. Arslan et al. [34] used BIM to visualize and analyse data on worker movements collected by the WOTAS system. This system monitors the trajectories of workers on construction sites using BLE signals. By using this solution, it was possible to identify high-risk regions by visualizing the most likely locations to experience rapid employee movement and turnover. In a study conducted by Skanska USA Building Inc. [89], BIM was used as a tool to centralize and compare safety data collected after each near-miss event, enabling deeper analysis of the causes of accidents and near misses.

In a study conducted by Liu et al. [80], an advanced video surveillance system integrated with BIM was employed to analyse and identify unsafe worker behaviour that could potentially result in near misses. The technology, which is based on a fusion of machine learning methods, enabled the precise classification of workers’ postures, thereby facilitating more effective risk management and enhancing safety on the construction site.

4.4. Key Aspects of Near-Miss Investigations in the Construction Industry

A review of the literature on near misses in the construction industry has been undertaken, focusing on areas of interest to researchers. A substantial proportion of the articles concentrated on the investigation of quantitative patterns with a view to evaluating occupational risks and qualitative patterns with a view to identifying precursors to accidents, particularly the causes of risks. Furthermore, time series studies were conducted with the objective of detecting the distinctive features of near-miss events. Furthermore, studies that focused on the activities of workers at the time of the incident, the impact of the safety climate on the generation of near misses, and the material elements present in construction processes were identified. Figure 8 illustrates the principal elements of near-miss searches in the construction industry.

4.4.1. Occupational Risk Assessment

Cambraia et al. [12] propose that the analysis of near misses should be conducted in four areas: (a) determining the course of the incident; (b) characterizing each incident in terms of physical characteristics (e.g., falling objects); (c) evaluating the feedback provided on the safety management system, whether positive or negative; and (d) conducting a risk analysis based on an assessment of the probability and consequences of each incident.

Zhou et al. [90] created a database containing information on subway construction incidents to improve occupational safety management and expand the capabilities of quantitative risk assessment (QRA). The study analysed the differences between an incident, an accident, an accident-prone situation, and unsafe worker behaviour. A subway construction incident database (SCID) was created using the Microsoft Access 2007 software platform, with consideration given to three key aspects: supervision, investigation, and implementation. Furthermore, an exemplar of the deployment of the SCID in the domain of safety management during the construction of subways is provided. The findings of the study suggest that the SCID has the potential to address the shortcomings in safety management during subway construction. It can be employed as a qualitative instrument for the identification of preliminary scenarios and as a quantitative instrument for the estimation of risks.

A quantitative risk assessment was conducted using real-time worker location-tracking technology [45]. The technology facilitated the automatic aggregation of data pertaining to the geographical coordinates of workers and construction equipment, which was subsequently subjected to an analytical process aimed at identifying locations and scenarios characterised by an elevated risk of collisions or other hazardous interactions.

In contrast, Raviv et al. [18] conducted a quantitative risk assessment using the Analytic Hierarchy Process (AHP) method, applying it to evaluate the risk of near misses associated with tower cranes. The study identified the risk potential by assigning weights to different levels of event impacts, which were then used to calculate the total risk potential for each reported event. The results showed how technical and human factors affect risk in the field of tower crane operation. Cluster analysis (k-means clustering) was used to group incidents into homogeneous groups based on their similarity. Clusters were evaluated by comparing specific incident severity levels within each cluster to the overall database. This analysis determined which clusters had a higher or lower risk of specific incident severity levels. This method facilitated a comprehensive examination of potential risks and the discernment of clusters of events exhibiting analogous risk profiles.

4.4.2. Causes of Hazards in Construction

One effective method for preventing accidents is to identify instances of near misses and their underlying causes, and to integrate the insights gained from these incidents into construction practice [62]. Wu et al. [91] claim that the use of preventive measures and the real-time reporting of a near miss would avoid 66% of accidents caused by falling objects.

Zhou et al. [73] developed a near-miss incident management process comprising eight stages: discovery, reporting, identification, prioritisation, cause analysis, resolution, dissemination, and evaluation. The study revealed that the causal patterns underlying both workplace accidents and near-miss incidents in construction projects are strikingly similar. As a result of this characteristic, it is possible to infer potential hazards from observed situations, thus providing an effective approach to the proactive prevention of accidents and the enhancement of overall site safety. A Precursors and Immediate Contributory Factors (PaICF) model was constructed based on the analysis of reports of hazardous incidents. The causal factors were classified into two categories. The initial category pertained to the potential release of hazardous materials or energy within the system. The second category pertains to contributory factors that could potentially result in the failure of protective measures. The causal factors of hazards in the second category can be analysed according to three hierarchies: direct, managerial, and foundational.

4.4.3. Time Series of Near Misses

Unsafe events at construction sites occur at discrete points in time and form a stochastic process [92]. They represent a measurable representation of safety system variability over time [93]. To effectively manage accident risk, the analysis of time series of near misses has become a significant tool for better understanding the dynamics and evolution of safety situations over time. However, in most studies on the temporal nature of near misses, stochastic statistical models have been utilized, assuming random event frequencies [16].

Zhou et al. [93] demonstrated the existence of temporal characteristics of near-miss events using complex network theory and visibility graph algorithms. This feature was discovered using data from events occurring during the construction of the metro in Wuhan, China, between 2011 and 2015. It was found that the time series of near misses exhibit scale-free properties, clustered in hierarchical structures, and demonstrate small-world characteristics.

The trend line of the time series of hazardous events is influenced by various factors associated with construction processes. These include variables such as the scale of construction production, fluctuating workforce numbers, the level of safety culture and climate within the company, and, primarily, human factors—specifically, workers’ attitudes towards reporting near misses and their understanding of the issue’s importance [94].

The method of detecting anomalies in time series was also applied in a study aimed at developing a method for detecting fall-related hazardous events [17]. Inertial Measurement Units (IMUs) were used to collect data on typical movements, postures, and near-miss accidents caused by workers’ falls. This enabled the quantification and real-time detection of near misses during classified worker postures/movements. On the construction site, workers’ postures and movements were recognized using supervised classification algorithms.

4.4.4. Material Factors of Construction Processes

In construction processes, material factors include machinery and equipment, construction materials, and construction workers performing various tasks. Raviv et al. [18] investigated near misses involving tower cranes, while Abbasi et al. [81] applied Convolutional Neural Networks (CNNs) in conjunction with sound recognition techniques to identify hazards related to collisions between construction equipment and workers.

In work processes, the human factor is paramount. In accidents, humans play a triple role: as decision-makers, as accident perpetrators, and as victims. For this reason, the so-called human factor is often the subject of various aspects of accident research. One of the most common accidents involves falls to ground level [3,95]. Near misses related to falls have been the subject of research by Kanghyeok Yang et al. [17].

The safety culture and climate in the company have a significant impact on the number of accidents in construction [55,96]. Hon et al. [55] surveyed Hong Kong, demonstrating a correlation between the safety climate and the frequency of reporting near misses by workers.

It has also been found that factors such as seasonality [93], worker fatigue, lack of sleep, and stress [97] influence the frequency of near misses. Unsafe behaviours and conditions constitute sources of hazard and are referred to as safety hazards. A hazard is defined as a specific space, area, location, equipment element, or situation containing stored energy or material that poses a risk of potential injuries, death, property loss, and environmental damage. Unsafe behaviours of workers are primarily influenced by factors such as poor habits, worker motivation, perceived behavioural control, and subjective norms [60].

4.5. A Comprehensive Overview of the Research Questions and References on Near Misses in the Construction Industry

Table 4. Overview of research questions and references on near misses in the construction industry.

Number of Question	Question	References
Q1	Are near misses in the construction industry studied scientifically?	[3,12,14,16,17,18,19,28,31,34,36,45,47,53,54,55,56,57,58,60,61,62,63,64,65,66,67,68,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,93,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121]
Q2	What methods have been used to obtain information on near misses and systems for recording incidents in construction companies?	[14,16,17,18,19,34,45,47,53,54,55,56,57,58,60,61,62,63,65,66,67]
Q3	What methods have been used to analyse the information and figures that have been obtained?	[14,18,19,34,47,53,60,61,62,63,67,68,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89]
Q4	What are the key aspects of near misses in the construction industry that have been of interest to the researchers?	[3,12,17,18,45,55,60,62,73,81,90,93,97]

presents the selected literature pertinent to the research questions posed.

5. Discussion

A review of 75 articles from the existing literature on NMs in the construction sector was conducted to ascertain the current state of knowledge on this topic and to address the defined research questions.

5.1. Interest of Researchers in Near Misses in Construction (Question 1)

In examining the first research question, the quantitative analysis revealed a notable increase in interest in this subject area, particularly over the past decade.

A second observation is the concentration of the research analysed in a few specific countries. The subject matter is especially prevalent in the United States, China, the United Kingdom, Australia, and Hong Kong. In other countries, articles on NMs are scarce. The results of the research are published in a number of journals specialising in the fields of occupational safety and construction. These include Safety Science, Journal of Construction Engineering and Management, Automation in Construction, Advanced Engineering Informatics, International Journal of Construction Management, Accident Analysis and Prevention, Engineering Construction and Architectural Management, Heliyon, and others.

This topic could be the subject of further research in international bodies set up by different countries. In order to foster greater interest among scientists from different countries in the subject of near misses, it would be prudent to undertake joint research in international projects with the objective of establishing a uniform approach to these incidents.

While quantitative research offers insights into the trajectory of knowledge advancement in the domain of near misses, the qualitative research conducted, in addition to its scientific value, is of significant relevance for engineering practice.

5.2. Methods Used to Obtain Near-Miss Information (Question 2)

The analysis of the articles revealed a number of issues related to the collection of NM information that could be addressed in the future.

The construction industry is typified by markedly variable working conditions, which are linked to elevated risks to the lives and health of workers and a high prevalence of accidents. The documentation of all hazardous occurrences, encompassing both workplace accidents and near misses, represents a significant knowledge asset, providing invaluable support to management in the construction industry. Nevertheless, the implementation of an event recording system in construction companies may prove to be a challenging undertaking. In many countries, the recording of near misses is not a mandatory requirement [3,29]. The traditional methods of event recording have a number of disadvantages. The most significant of these is the absence of immediate access to essential information [48], as manually prepared reports necessitate precise analysis and the interpretation of results can be subjective [49]. Conversely, the selection of an appropriate IT system for reporting near misses is contingent upon the specific requirements of the company, the existing IT infrastructure, the availability of internet connectivity at the site, and other factors. Moreover, the implementation of such a system entails additional costs that may prove challenging for smaller companies to accommodate. The utilisation of information technology and artificial intelligence in the investigation of near-miss incidents enables the analysis and extraction of valuable information, thereby facilitating the transformation of these data into actionable knowledge. The insights gained will be of significant value to management in terms of reporting and training employees. Furthermore, it serves to reinforce the organisation’s overarching safety culture [55].

The results of the conducted analyses indicate that automated tools based on intelligent technologies are already being employed in the reporting phase within the construction industry [56,62]. Nevertheless, a significant challenge remains in ensuring the reliability of reporting, which hinges on workers accurately reporting near misses (NMs). The research presented in [3] suggests that, over time, employees become less enthusiastic about reporting incidents.

These findings are corroborated by the findings of the research presented in the paper by Zong and Fu [46]. This is a significant issue, as the absence of reliable and credible data can impact the interpretation and quality of results. Consequently, systems have been developed that focus on the automated identification of near misses through visual monitoring and the utilisation of diverse sensor technologies, in conjunction with the examination of received signals for recurrent patterns [31].

5.3. Methods Used to Analyse the Information and Data Sets (Question 3)

The most common techniques used for the analysis of numerical data in near-miss investigations are quantitative statistical methods, which employ statistical inference techniques. Qualitative methods are based on the analysis of existing near-miss reports. The increasing volume of near-miss data presents a challenge for conducting precise analyses using conventional techniques. In recent years, there has been a notable emergence of artificial intelligence (AI) methods designed to facilitate ongoing research and analysis.

The principal aim of utilising artificial intelligence methodologies is to derive valuable insights from near-miss data and transform them into knowledge that can be leveraged to enhance the overall safety standards within the organisation. A review of the methods employed to analyse near-miss data has revealed that there is a tendency to prioritise the identification and analysis of events, with less emphasis placed on the dissemination of knowledge about near-miss occurrences. It may therefore be proposed that a potential avenue for future research would be the development of procedures to optimise the efficacy of near-miss event management systems within an organisational context, whilst simultaneously enhancing their dissemination beyond the organisation.

5.4. Key Aspects of Near-Miss Investigations in the Construction Industry (Question 4)

A review of near-miss incidents in the construction industry indicates a notable shift in the methods employed to address safety and risk management. A review of the literature reveals a tendency towards the adoption of more integrated and dynamic research methods. These combine quantitative risk assessment with causal analysis of hazards, with the objective of identifying the principal factors affecting worker safety.

The initial area that requires attention is the development of risk assessment systems that are based on data obtained from actual workplaces. The advent of modern technology, such as real-time tracking of worker locations, has rendered it a pivotal instrument in the identification and prediction of potential collisions and other hazardous interactions. This approach not only enhances the precision of risk assessments, but also facilitates expeditious response to evolving site circumstances.

In the context of near-accident root cause analysis, the development of management processes such as Zhou et al.’s eight-step process illustrates the importance of a systematic approach to each incident. The proactive utilisation of near-accident data to enhance design practices can markedly diminish the probability of accidents. The PaICF model elucidates how profound causal analysis can facilitate more efficacious risk factor management at disparate organisational levels.

A pivotal instrument for comprehending the dynamics and evolution of safety scenarios over time is the examination of time series of near misses. The insights gained can serve as the foundation for anticipating the timing of subsequent occurrences that may, under specific circumstances, culminate in workplace accidents.

It is becoming increasingly evident from research into the physical factors involved in construction processes that both the people involved and the tools and machinery they utilise are integral components of the safety ecosystem. It is crucial to comprehend the interactions between humans and machines, as well as the influence of safety culture on worker conduct, for the effective management of risks.

In conclusion, these four pivotal research areas illuminate novel strategies for enhancing safety in the construction industry. They propose a paradigm shift from a reactive to a proactive approach to safety management, wherein nearly every incident is regarded as an opportunity for learning and improvement.

6. Conclusions

A review of the literature reveals a growing interest in near misses in the construction industry in recent years. The objective of these studies is to enhance the safety standards on construction sites by reducing the incidence of workplace accidents. This is also associated with the minimisation of material and moral losses associated with this negative phenomenon. A comprehensive review of 75 publications led to the following conclusions being drawn:

• A quantitative analysis of the Q₁ question has revealed a positive trend, namely that there is a growing interest among researchers in studying near misses in construction. The greatest interest in NM topics is observed in the United States of America, China, the United Kingdom, Australia, Hong Kong, and Germany. Additionally, there has been a recent emergence of interest in Poland. The majority of articles are mainly published in journals such as Safety Science (10), Journal of Construction Engineering and Management (8), and Automation in Construction (5);
• The analysis of question Q₂ illustrates that traditional paper-based event registration systems are currently being superseded by advanced IT systems. However, both traditional and advanced systems are subject to the disadvantage of relying on employee-reported data, which introduces a significant degree of uncertainty regarding in the quality of the information provided. A substantial proportion of the data and findings presented in the studies was obtained through surveys and interviews. The implementation of real-time monitoring systems is becoming increasingly prevalent in construction sites. The objective of such systems is to provide immediate alerts in the event of potential hazards, thereby preventing a significant number of near misses. Real-time monitoring systems employ a range of technologies, including ultrasonic technology, radio frequency identification (RFID), inertial measurement units (IMUs), real-time location systems (RTLSs), industrial cameras, wearable technology, motion sensors, and advanced IT technologies, among others;
• The analysis of acquired near-miss data is primarily conducted through the utilisation of quantitative and qualitative statistical methods, as evidenced by the examination of the Q₃ question. In recent years, research utilising artificial intelligence (AI) has made significant advances. The most commonly employed artificial intelligence techniques include text mining, machine learning, and artificial neural networks. The growing deployment of Building Information Modelling (BIM) technology has precipitated a profound transformation in the safety management of construction sites, with the advent of sophisticated tools for the identification and management of hazardous occurrences;
• In response to question Q₄, the study of near misses in the construction industry has identified several key aspects that have attracted the attention of researchers. These include the utilisation of both quantitative and qualitative methodologies for risk assessment, the analysis of the causes of hazards, the identification of accident precursors through the creation of time series, and the examination of material factors pertaining to construction processes. Researchers are focusing on the utilisation of both databases and advanced technologies, such as real-time location tracking, for the assessment and analysis of occupational risks. Techniques such as Analytic Hierarchy Process (AHP) and clustering facilitate a comprehensive assessment and categorisation of incidents, thereby enabling the identification of patterns and susceptibility to specific types of accidents. Moreover, the impact of a company’s safety climate and organisational culture on the frequency and characteristics of near misses represents a pivotal area of investigation. The findings of this research indicate that effective safety management requires a holistic approach that integrates technology, risk management and safety culture, with the objective of reducing accidents and enhancing overall working conditions on construction sites.

7. Gaps and Future Research Directions, Limitations

A review of the literature in the area of NMs revealed research gaps that the authors believe need to be filled in order to enhance occupational safety management in the construction industry. The following areas of research were identified:

• Given the diversity and variability of construction sites and the changing conditions and circumstances of work, it is essential to create homogeneous clusters of near misses and to analyse the phenomena within these clusters. The formation of such clusters may be contingent upon the direct causes of the events in question;
• Given the inherently dynamic nature of construction, it is essential to analyse time series of events that indicate trends in development and safety levels. The numerical characteristics of these trends may be used to construct predictive models for future accidents and near misses;
• The authors have identified potential avenues for future research, which could involve the development of mathematical models using techniques such as linear regression, artificial intelligence, and machine learning. The objective of these models is to predict the probable timing of occupational accidents within defined incident categories, utilising data from near misses. Moreover, efforts are being made to gain access to the hazardous incident recording systems of different construction companies, with a view to facilitating comparison of the resulting data;
• One significant limitation of near-miss research is the lack of an integrated database that encompasses a diverse range of construction sites and construction work. A data resource of this nature would be of immense value for the purpose of conducting comprehensive analyses and formulating effective risk management strategies. This issue can be attributed to two factors: firstly, the reluctance of company managers to share their databases with researchers specialising in risk assessment, and secondly, the reluctance of employees to report near-miss incidents. Such actions may result in adverse consequences for employees, including disciplinary action or negative perceptions from managers. This consequently results in the recording of only a subset of incidents, thereby distorting the true picture of safety on the site.