Analytical Hierarchy Process for Construction Safety Management and Resource Allocation

Abstract

The construction industry plays a crucial role in contributing to the economy and developing sustainable infrastructures. However, it is known as one of the most dangerous industrial domains. Over the years, special attention has been paid to developing models for managing and planning construction safety. Many research studies have been carried out to analyze the root causes of fatal accidents in construction sites to develop models for preventing them and mitigating their consequences. Root cause identification and analysis are essential for effective risk mitigation. However, implementing mitigation activities is usually limited to the project’s safety budget. The construction sector suffers from a lack of allocation of appropriate safety resources triggered by a dynamic and complex project environment. This study aims to address the gap in safety resource allocation through a comprehensive root cause analysis of construction work accidents. In this paper, we present a comprehensive review of work accident-related research, categorized according to the 5M model into five root factors: medium, mission, man, management, and machinery. A novel methodology for construction safety resource allocation is proposed to mitigate risks analyzed by the 5M model with the aid of advanced technological solutions. Safety resource allocation alternatives are formulated, and their priorities are established based on an analysis of structured criteria that integrate both risk and cost considerations. The Analytical Hierarchy Process (AHP) is employed to select the optimal alternative for safety resource allocation, with the objective of effective risk mitigation. The proposed model underwent validation through two different case studies. The findings indicate that risk aversion is a critical factor in the optimal allocation of safety resources. Furthermore, the results suggest that regulatory measures should prioritize the stimulation of risk motivation in the safety decision-making processes of construction firms.

1. Introduction

The construction industry plays a pivotal role in the global economy and infrastructure development; however, it is also recognized as one of the most hazardous sectors in industry. Construction sites are characterized by continuous dynamism, rendering daily changes that introduce varying accident risks. Dangerous activities, such as excavations, working at height, exposure to dust and noise, and operation of heavy machinery, pose significant risks to workers’ safety [1].

Addressing safety concerns in the construction industry requires a proactive approach, meticulous planning, and a deep understanding of the complex dynamics inherent in construction site operations. By systematically identifying and addressing hazards, construction companies can create safer working environments for their employees. Research studies have focused on identifying and quantifying risks associated with construction accidents [2,3]. It concluded that while significant improvements have been made, practical tools for hazard analysis and enhancing safety performance in construction are still lacking.

Accidents in the construction industry are often attributed to a combination of factors, necessitating comprehensive risk assessment and management approaches. Collaboration among diverse teams on construction sites adds complexity and underscores the importance of analyzing safety risks by occupation or project phases to ensure comprehensive accident prevention strategies. This study seeks to improve safety outcomes on various construction projects through attribute-based risk analysis and customized safety plans. Acknowledging the complex interplay of various factors, the 5M model, illustrated in Figure 1, is employed to analyze root factors to prevent future accidents [4]. Such a model helps identify the root causes of accidents and attribute them to five main groups: man, machinery, medium, management, and mission [5,6]. This study suggests analyzing these risk factors using the 5M model and categorizing them according to different project phases to enhance their mitigation process.

Allocating appropriate safety resources for promoting a safety culture, enhancing training programs and safety practices, and implementing advanced solutions can improve safety outcomes in the construction industry [7]. To mitigate risks effectively and improve safety outcomes, research studies emphasize the correlation between safety practices and construction quality [8], underlining the critical need for stringent safety measures. These measures ultimately ensure the well-being of workers and the successful completion of projects. Given the fact that few studies have examined safety resource allocation in construction projects [9,10] there is still a lack of modeling tools for safety resource allocation, and construction safety suffers from a scarcity of safety resources. This paper introduces the Analytical Hierarchy Process (AHP) to support decision-makers to prioritize and allocate safety resources effectively. Unlike other studies [10], decisions regarding safety resource allocation are proposed based on the priority-setting of safety risk and cost preferences.

While the 5M model and AHP have been studied and applied individually, there is a dearth of research on the benefits of combining these two approaches to enhance decision-making and resource optimization processes. Due to the limited exploration of their integrated application, the research gap lies in combining the 5M model for root cause analysis and the AHP for decision-making and optimizing resource allocation.

1.1. Research Objective

The research objective is to explore the applicability of the 5M and AHP models in accident prevention, risk analysis, and safety resource allocation within the construction context. Overall, the objective is to contribute to developing proactive and adequate safety measures to ensure the well-being of workers and the successful execution of construction projects.

This paper addresses the intricate challenges associated with safety in the construction industry by thoroughly investigating various factors influencing work accidents. Employing the 5M model—which focuses on medium, mission, man, management, and machinery—this study seeks to dissect these factors and their interplay within construction environments and to enhance safety outcomes by examining management practices, workers’ behavior, machinery, working conditions, and project objectives.

The research also examines resource allocation practices, advocating for optimal safety investments to mitigate the financial and reputational costs of workplace accidents. It aims to uncover insights into safety decision-making processes and inform targeted safety training programs by assessing risk perception among stakeholders, including managers, workers, and safety personnel.

1.2. Research Contribution

By identifying effective strategies for safety management and investment, the research will promote a safety culture and reduce the incidence of work accidents in the construction industry. This study investigates and analyzes various safety aspects in the construction industry, including assessing the effectiveness of safety practices, identifying root causes of accidents, and developing comprehensive risk assessment and management strategies. Emphasizing the need for a comprehensive approach, the research categorizes safety risks across project types, construction stages, professions, and timeframes to formulate strategic safety plans and training protocols tailored to specific risk scenarios. Ultimately, the savings achieved by preventing accidents far outweigh the initial budget allocation. This strategic decision not only safeguards life and health but also contributes to the overall success and sustainability of construction projects.

2. Background

The literature review related to the 5M model and the AHP is presented in the following sections: Section 2.1 (including Section 2.1.1, Section 2.1.2, Section 2.1.3, Section 2.1.4 and Section 2.1.5) and Section 2.2, respectively.

2.1. The 5M Model

Typically, a learning process through investigation follows the occurrence of an accident. Such a process is notably challenging in construction sites due to various risk factors influencing operations. This paper provides a comprehensive review of research related to the investigation of work accidents, categorized into the five groups defined by the 5M model: medium, mission, man, management, and machinery.

2.1.1. Medium

The relationship between unsafe working conditions and workers’ behavior and their impact on the severity of injuries has been thoroughly investigated across various industries. Unsafe actions by workers, including incorrect assessments of situations or improper operations, combined with hazardous working environments like bad weather or unstable surfaces, are significant root causes of workplace accidents. Heinrich [11] noted that 88% of workplace accidents were due to unsafe worker actions within dangerous environments.

The frequency of confined space incidents indicates that working in such environments poses a significant risk of serious harm or fatality. Arifin et al. [12] investigated the root causes of fatal incidents associated with confined spaces in Malaysia. Their findings revealed that physical and atmospheric hazards are the primary factors contributing to fatal incidents in confined spaces, with improper use of Personal Protective Equipment (PPE) and hazardous environmental conditions identified as the main immediate causes. Effective risk management of confined spaces, particularly addressing atmospheric and physical hazards, is essential for preventing accidents, particularly those resulting in fatalities.

By examining the medium component in the root cause analysis, the research delves into the relationship between unsafe working conditions and workers’ behavior, highlighting the critical role of hazard perception in accident prevention.

2.1.2. Mission

Assessing risks in construction work is challenging due to the dynamic nature of the construction environment, which differs significantly from other industries where the work environment is generally static. Construction sites undergo constant changes, including team rotations, high turnover rates, varying ground conditions, diverse technologies and execution methods, unique project-specific environments, and different teams with distinct missions operating in the same area. These characteristics increase the complexity of safety risk assessment [13]. Esmaeili et al. [14] developed an attribute-based model for risk assessment using accident reports from OSHA’s Integrated Management Information System (IMIS) [15]. Their model aimed to identify measurable safety attributes and incorporate them into a risk analysis approach based on historical data. However, this model has limitations, notably the need to expand attribute-based safety risk analysis beyond specific risk types and accommodate variations in risk perception among different professions in construction. Xiao et al. [16] developed a quantitative analysis method for generating a loss index for various accidents on construction sites. This method, based on the Bayesian Network (BN) and AHP solution, calculates the contribution rate of each risk factor to a particular accident.

Additionally, studies have highlighted that designers often lack familiarity with construction risks and may be reluctant to prioritize safety in their design decisions [17]. Collaboration among stakeholders, ongoing training, and integrating safety considerations into design and planning processes are essential for mitigating risks and promoting a safer work environment.

Consequently, the mission aspect underscores the dynamic nature of construction work, necessitating innovative risk assessment models that account for the evolving work environment and project-specific challenges.

2.1.3. Man

Furthermore, psychological factors significantly influence safety behavior, as evidenced by studies on risk perception among construction workers in different roles, professions, and experience levels [18,19]. For example, management personnel perceive certain situations as more dangerous than workers, possibly due to their higher education and safety training. Fan et al. [20] proposed using the AHP to evaluate the impact of aging construction workers on safety risk factors. The profession also plays a role in risk assessment; for instance, experienced workers may assess certain scenarios differently from new workers or those in mid-career, reflecting differences in risk tolerance and perception [18,19].

Existing methods for analyzing construction safety risks often rely on subjective experience and may not accurately reflect the real-time risk levels of construction projects. To address this issue, a knowledge-graph-improved dynamic risk analysis method for Behavior-Based Safety (BBS) management on construction sites has been proposed [21].

For example, a study in Hong Kong explored why construction workers engaged in unsafe behavior and identified factors such as lack of skill or safety training, disregard for safety protocols, failure to use PPE, and difficulty recognizing unsafe conditions [22]. Another study categorized human errors into two main types: cognitive-related failures, such as limited human capability, and deviations from established safe work practices [23]. By exploring factors influencing human behavior on construction sites, such as lack of training, information, or personal protective gear, the study aims to identify key areas for intervention and develop risk mitigation strategies.

2.1.4. Machinery

Construction equipment is typically categorized into four main families: concrete creation and handling equipment, transport equipment, earthworks equipment, and formworks and scaffolding [24]. The choice of equipment for construction sites depends on various factors such as site organization, execution methods, technological constraints, costs, and safety considerations. Tower cranes, a major component of construction sites, pose significant safety risks due to their widespread use and complex operations [25]. A significant challenge encountered by crane operators is the restricted field of vision, which can compromise safe operation. Limited visibility, whether due to inadequate lighting or physical obstructions, hampers the operator’s ability to carry out tasks and inspect cargo safely. To mitigate these risks, measures such as employing signalmen and installing auxiliary vision systems, like cameras on cranes, have been adopted [26,27]. These technologies enhance operator visibility and improve overall safety on construction sites.

The use of cranes on construction sites is frequently linked to high accident rates. Wu et al. [28] introduced a method for quantitatively assessing various risk factors in crane accidents using BN and the N-K model. It examines the causes of crane accidents, identifies types of risk, and calculates failure probabilities. Their findings underscored the significant impact of human and management factors on crane accidents, highlighting the need for increased attention to these areas. However, visibility issues, particularly those related to construction machinery, remain a significant contributing factor to accidents. Understanding the interconnectedness of medium, man, mission, and machinery is essential for optimizing safety management.

2.1.5. Management

Management plays a crucial role in ensuring safety on construction sites, mainly through the supervision and control actions of construction managers and supervisors. Management personnel typically undergo more systematic safety training than frontline workers, leading to a heightened focus on accident prevention.

However, studies have highlighted challenges in effectively identifying and assessing risks by construction supervisors. Research conducted in 2006 [29] revealed that only 6.7% of construction supervisors could identify all relevant safety risks presented to them. Additionally, a lack of acquaintance with a project’s mission may lead to underestimating associated risks [30]. The dynamic nature of construction work often requires on-the-spot solutions to various interferences, underscoring the critical role of foremen and construction supervisors in managing physical site conditions [31,32]. However, studies have shown variations in risk assessment abilities among construction supervisors. For instance, a study conducted at the Technion [33] found that experienced supervisors and safety managers demonstrated better risk assessment abilities than students with minimal working experience and formal safety training. Their study evaluated risks within a virtual construction site, revealing that experienced professionals typically rated risks as more severe and identified more risks than students. Regarding resource allocation, organizations often underestimate the importance of preventing work accidents, frequently due to a lack of credible data. Research conducted in 2018 [34] aimed to estimate the optimal budget for construction safety investment. The findings suggested that an optimal investment of 1% of the project scope could significantly reduce accident rates and overall safety costs.

She et al. [35] introduced an innovative “scenario-response” analysis framework for managing construction safety accidents. Leveraging BN technology, their framework systematically analyzes accident risks and developmental paths. Their study evaluates emergency decision-making plans and establishes a comprehensive accident emergency response evaluation indicator system.

The intersection of factors such as man, machine, medium, mission, and management underscores the importance of fostering an organizational culture prioritizing safety. This includes enhancing workers’ awareness of risk identification. Familiarity with the construction site, creating a safe work environment, and providing general and specific guidance—combined with knowledge and technology—empower project managers, safety managers, constructors, and foremen to receive scenario-based information tailored to different construction stages. While advanced technology can enhance safety practices, the human factor remains crucial. Effective safety management begins with organizational leadership promoting safe behavior and allocating sufficient budgetary resources.

Promoting collaboration among all stakeholders to enhance worker safety through additional legislation and updates can significantly improve safety practices. Integrating proactive safety approaches into the education and training of all construction professions, including foremen, safety helpers, crane operators, engineers, and practical engineers, is beneficial.

2.2. The AHP Model

The AHP method was originally developed by Saaty L. Thomas, in the 1970s [27], and ref. [36] has been widely used for decision-making across various industries such as automotive, chemistry, energy, machinery, and medicine. This method is suitable for determining priorities, allocating funds based on performance indicators, designing systems, ensuring system stability, and optimization purposes. The AHP is a multi-criteria decision-making tool that quantifies and prioritizes criteria and sub-criteria through pairwise comparisons using proportional scales [37]. A review of the application of AHP in construction was presented by Darko et al. [38]. Subramanian and Ramanathan [39] identified five key application areas of AHP in operational research: operational strategy, process optimization, product design, resource planning, project management, and supply chain management. Wang and Yang [40] utilized AHP to enhance decision-making for outsourcing information systems, evaluating criteria like cost, resources, strategy, risk, management, and quality. Their study primarily contributed to a decision-making framework for selecting service providers and determining criteria weights.

In construction, Skibniewski and Chao [41] highlighted that using AHP in a tower crane case study can lead to more effective technical and economic evaluations in decision-making. As in construction projects, various risks can impact cost, schedule, and quality, and the AHP helped identify and prioritize high-impact risks [42]. The study pinpointed the lack of clear delegation of professional responsibilities as the most critical risk factor. Therefore, effective risk management strategies should target this specific risk to prevent adverse effects on project performance.

A recent study [43] has applied the AHP to prioritize criteria for material selection for prefabricated wooden buildings in Canada and the United States. It revealed that the safety and security of building occupants, location, shape, and height of the building; comfort, satisfaction, and well-being of the building; occupant health; and material availability were the top five sub-criteria for structural materials.

Another study [44] has focused on enhancing civil aviation safety by reducing human error incidents. Aminbakhsh et al. [45] introduced a safety risk assessment framework that prioritizes safety risks, aiding in budgeting and goal setting without compromising safety. Their framework serves as a decision-making tool for stakeholders, helping them determine accident and injury prevention investments within the funding limits.

Considering the high accident rate in China, Xiao et al. [16] developed a new quantitative analysis strategy using a BN and AHP solution. Their approach calculates the contribution rate of every risk factor to specific accidents and generates a loss index for various construction site incidents. It helps clarify the safety situation and analyze risk priority based on the dynamic condition. Khodabocus and Seyis [46] developed a decision-making model for efficient risk management approaches in modular construction projects. The Delphi method and the AHP were used to rank and quantify risks and approaches. Critical risk categories and top-rated risk management approaches were identified.

As improving safety communication in the construction industry is essential for reducing workplace injuries, Kim et al. [47] investigated the factors that positively impact safety communication, particularly among foreign construction field workers. They proposed a quantitative assessment through AHP to highlight the significant influence of extrinsic motivation, management communication style, and visible safety information on communication effectiveness. They also recommend including foreign construction field workers in future research to enhance the diversity of perspectives and experiences.

Moon et al. [48] proposed a research study to fill a gap in current guidelines by examining the risks of nuclear decommissioning. Using the fuzzy-AHP technique, tasks for dismantling radioactive concrete structures are prioritized, revealing structural and human-related risks. Their study underscores the significance of thorough risk assessment to improve safety during decommissioning, contributing to standardized safety protocols for global nuclear decommissioning.

Anjamrooz et al. [49] delved into the critical aspect of selecting sustainable construction projects and programs, emphasizing the need for sustainability-specific criteria in the portfolio selection process. They discuss categorizing these criteria into environmental, social, and economic pillars and highlight the results of a survey conducted in the United Arab Emirates, underscoring the importance of environmental criteria in sustainability selection. Mega Construction Projects (MCPs) encompass lengthy durations, intricate environments, diverse equipment, and numerous safety risk factors, posing significant challenges for project managers. Xiang et al. [50] utilized advanced models to identify 31 secondary safety risk factors within MCPs, including the key safety risk factors and their interconnections. Their findings provide valuable insights for enhancing safety risk identification and management in MCPs, contributing to the advancement of complex system risk management research.

The construction industry grapples with the challenge of managing information effectively. Saah et al. [51] explored the potential of blockchain technology to store construction workers’ data securely, enhancing data security and privacy. Their study showcases the promise of blockchain in reshaping workforce management and optimizing safety measures. Practical validation confirms the feasibility and effectiveness of the model, heralding a new era of data management for the industry.

Ran et al. [52] proposed a unique safety risk analysis technique for raised urban bridges. Their model used dynamic scores and analytical hierarchy to calculate static and dynamic weights.

Vahedi Nikbakht et al. [53] suggested using AHP and VIKOR (multi-criteria decision-making method designated to compromise problem-solving) methodologies to identify and rate factors causing high-rise project delays.

The abovementioned literature review shows the wide use of AHP in decision-making in construction. However, it points to a research gap in its application for safety resource allocation. This study proposes implementing this tool to support construction managers’ decision-making in allocating optimal safety resources.

3. Research Methodology

Investing in safety management typically reduces the probability of accidents and minimizes financial losses post-accident. While direct costs of accidents, such as medical care, compensation for injuries, and damage to buildings or machinery, are the main financial losses, indirect costs, including time dedicated to accident investigation, legal expenses, loss of clients, damage to the firm’s reputation and closure of the construction site, can be even more substantial. Finding the optimal investment level for each construction project ensures that safety investments are financially viable without compromising safety standards. Striking this balance helps mitigate accidents while controlling overall safety expenses.

The proposed framework (Figure 2) suggests using the AHP to select the best safety resource allocation that allows mitigation of the risks identified in the project using advanced technological solutions. This is achieved based on priority setting by the contractor, regarding safety risk and safety cost.

The research methodology commences with an analysis of the fundamental risk factors contributing to fatal accidents at construction sites. Unlike similar research studies that analyze root factors of construction accidents [54,55,56], the proposed root cause analysis is carried out based on the 5M method (man, medium, mission, machinery, and management) and is further deepened by categorizing these factors according to various project phases, encompassing earthworks, structural works, finishing works, and site layout organization (Figure 3). The proposed model is designed to gain insights into the occurrence of fatal accidents across different project stages to enhance risk management practices.

In response to the imperative to reduce the frequency and severity of construction-related accidents, the recommended approach involves implementing risk mitigation measures. Risk mitigation activities can be implemented according to the safety budget allocated in the project. This study examines the following three alternatives for safety resource allocation:

• The first alternative (A) suggests optimizing the safety budget to an optimal ratio of 1% of the project scope, as advocated by Shohet et al. [34]. It should be emphasized that this ratio is suitable for large construction projects and that in other types of projects (e.g., Small and Medium Enterprises’ (SMEs) projects), the optimal ratio may be up to four times higher (3.8%) [7].
• The second alternative (B) suggests allocating a modest safety budget of 0.5% and using technological mitigation activities to enhance project safety with the extra 0.5%, eventually allowing an exception of d% from the optimal safety budget of 1% suggested in A. That means a total safety budget of (1 + d)%.
• The third alternative (C) suggests allocating a modest safety budget of 0.5% and sticking to it without any additional safety resource allocation.

Additionally, the research estimates the costs of mitigation activities in two project phases: the structural and finishing phases. It underscores the importance of utilizing advanced technological solutions such as simulations, drones, body cameras, crane-installed cameras, and sunscreen shedding and fall-avoidance nets to enhance safety risk mitigation measures (Figure 4).

A wide variety of advanced mitigation solutions are discussed in the literature. As falls from heights are frequent work accidents that cause injuries and deaths on construction sites, Tanvi Newaz et al. [57] evaluated recent safety technologies for controlling the risk of falls from heights, especially in urban building projects. Advanced technologies were identified as contributing to predicting, preventing, and mitigating risks of falls from heights. These include safety risk assessment, real-time monitoring, automated prevention, ontology modeling, virtual reality training, personal fall arrest systems, and collective fall protection systems. Shafei et al. [58] aimed to assess the implementation of Construction 4.0 technologies in a national strategy plan focusing on improving health and safety. Rasouli et al. [59] reviewed high-tech PPE in the construction industry, discussing their benefits, challenges, and potential for data collection to reduce risks and hazards.

The use of such advanced technologies for enhancing risk mitigation is allowable if safety resource allocation in the project is sufficient. Figure 5 suggests risk mitigation activities in each project phase that can be implemented according to alternative A and additional advanced risk mitigation methods for upgrading safety measures according to alternative B. AHP enables the selection of the most suitable alternative, ensuring optimal results while satisfying project budget constraints and meeting the needs of entrepreneurs, management, and execution teams on-site.

Contractors are consistently confronted with the challenge of allocating resources to safety activities, necessitating a careful assessment of the interplay between reducing safety risks and managing the safety budget. The use of the AHP to evaluate the impact of prioritizing safety costs and risks on decision-making processes in the context of safety budget allocation is central to this study. To implement such an evaluation with the AHP, the three suggested alternatives were tested for the safety investment ratio using different cost and risk weights of safety criteria.

The following three AHP tests were held:

• AHP Test 1: High priority to safety risk—20% for cost and 80% for risk.
• AHP Test 2: High priority to safety cost—80% for cost and 20% for risk.
• AHP Test 3: No criteria are prioritized—50% for cost and 50% for risk.

Finally, by applying the AHP decision model described in Figure 6, the alternative with the minimum final ranking score, presenting minimum cost and minimum risk, is selected for each one of the three AHP tests examined. This way, the proposed approach contributes to contractors’ decision-making in allocating safety resources to prevent work accidents based on their preferences regarding safety costs and risks.

For the exploitation of the safety budget allocated in the project, alternative suggestions are discussed, and their costs are analyzed. The risk assessment, focused on the probability of materializing, was analyzed based on fatal accidents in Israel. Therefore, there is no reference for light accidents or accidents in other countries. The model focuses on structural and finishing work phases, and it would be worthwhile considering future works to check its feasibility in other work stages as well.

4. Results and Discussion

To assess the model’s feasibility, it was implemented in two case studies from different domains with different scopes, complexity, and climates. The first is a construction project in central Israel, and the second is an infrastructure project in Eilat, located in the south of Israel.

4.1. Case Study 1—Cost Analysis

The first case study is a construction project in central Israel, comprising ten floors (nine residential and one basement), covering a built area of 5968 m² with an estimated construction duration of 24 months. The project’s scope, including fees, is estimated at USD 9.019M. Additionally, a safety budget of 0.5% of the total project cost was allocated for apartment delivery, contingency, liability period, and safety measures.

The safety budget for the project can be calculated using the following formula:

$$TPB={\displaystyle \frac{TPBWSB}{1-p_{safety}}}={\displaystyle \frac{TPBWSB}{1-0.005}}=\mathrm{9,018,987}\$$$

(1)

$$SB=TPB-TPBWSB=TPB-TPB\ast \left(1-p_{safety}\right)=TPB\ast \left( p_{safety}\right)=\mathrm{45,095}\$$$

(2)

where

• $TPB=total project budget$
• $TPBWSB=total project budget without safety budget$
• $p_{safety}=safety budget in percentage of total project budget$
• $SB=safety budget$

The Total Project Budget (TPB) is

$$TPB=\mathrm{9,018,987}\$$$

(3)

The previously suggested alternatives (A and B) for mitigation activities are analyzed and compared to the allocated safety budget of the test project (referred to as alternative C) in terms of total cost to the owner. A cost analysis is conducted for each alternative as follows.

4.1.1. Case Study 1—Alternative A

Alternative A considers allocating an optimal safety investment ratio that is 1% of the total project cost, according to Shohet et al. [34]:

$$P_{safety}=0.01$$

(4)

$$S{B}_{1\%}=TPB\ast \left(P_{safety}\right)=\mathrm{9,018,987}\ast 0.01=\mathrm{90,190}\$$$

(5)

The cost to the owner, including safety investment according to alternative A, is therefore

$$TP{B}_{1\% safety improvement-included}=\mathrm{9,109,177}\$$$

(6)

4.1.2. Case Study 1—Alternative B

Alternative B considers allocating an initial safety investment ratio that is 0.5% of the total project cost:

$$P_{safety}=0.005$$

(7)

$$S{B}_{0.5\%}=TPB\ast P_{safety}=\mathrm{45,095}\$$$

(8)

The cost to the owner, including initial safety investment according to alternative B, is therefore

$$TP{B}_{0.5\% safety improvement-included}=\mathrm{9,064,082}\$$$

(9)

Considering the optimal safety investment ratio of 1%, an upgraded safety investment is possible with the extra 0.5% available safety budget:

$${\mathsf{\Delta }}_{for safety budget}=\mathrm{45,095}\$$$

(10)

Subsequently, we recommend the following technical solutions for safety upgrades and utilization of the remaining safety budget in two project phases, the structural work phase and the finishing phase, as follows:

• The estimated duration of the structural works is 6 months.

  ◦

  The cost of a safety inspector in a 50% position scope in addition to the existing professional team on site—USD 15,142;

  ◦

  An extra worker for directing concrete trucks for an estimated duration of 20 min in casting—USD 3114;

  ◦

  Three body cameras (one for the workers’ team, one for the foreman, and one for the pump operator)—USD 870;

  ◦

  A drone for two workdays for each (total cost for worker + drone)—USD 5063.

A total of USD 24,189 for a safety upgrade in structural works.

This leaves an extra available safety budget of

$${\mathsf{\Delta }}_{for safety budget}=\mathrm{45,095}-\mathrm{24,189}=\mathrm{20,906}\$$$

(11)

• The estimated duration of the finishing and developing works is 13 months.

  ◦

  The cost of a safety inspector in a 25% position scope in addition to the existing professional team on site—USD 13,880;

  ◦

  Three body cameras (one for the workers’ team, one for the foreman, and one for the lifting machine)—USD 1344;

  ◦

  The cost of an upgrade to a European scaffold—after a cost reduction of the scaffold in a total front area of 4662 m²—USD 12,392.

A total of USD 27,616 or a safety upgrade in finishing works.

This results in a supplement to the safety budget:

$${\mathsf{\Delta }}_{for safety budget}=\mathrm{20,906}-\mathrm{27,616}=-6710\mathrm{\$}$$

(12)

Allocating a supplement of USD 6710 to the safety budget leads to a total safety budget of 1.07%.

4.1.3. Case Study 1—Alternative C

Alternative C considers allocating a modest safety investment ratio that is 0.5% of the total project cost:

$$P_{safety}=0.005$$

(13)

$$S{B}_{0.5\%}=TPB\ast P_{safety}=\mathrm{45,095}\$$$

(14)

The cost to the owner, including initial safety investment according to alternative B, is therefore

$$TP{B}_{0.5\%safety improvement-included}=\mathrm{9,064,082}\mathrm{\$}$$

(15)

4.1.4. Case Study 1—AHP for Decision-Making

The three alternatives suggest allocating different Safety Investment Ratios (SIR) of the total project scope:

Alternative A: Optimize the safety budget allocated in the project.

SIR(A) = 1%

(16)

Alternative B: Upgrade the safety budget allocated in the project.

SIR(B) = 1.07%

(17)

Alternative C: Stick to the safety budget allocated in the project.

SIR(C) = 0.5%

(18)

According to Shohet et al. [34], the Total Safety Cost Ratio (TSCR) for each alternative can be calculated as a function of the SIR as follows:

$$y=164.95x^2-3.2435x+0.0348$$

(19)

Consequently, and based on the following:

TSCR = SIR + TCAR

(20)

The Total Cost of Accidents Ratio (TCAR) can be calculated for each alternative:

$$\mathrm{Alternative} \mathrm{A} TSCR\left(A\right)=y\left(1.0\%\right)=1.89\%\to \mathit{TCAR}\mathrm{(A)}=1.89\%-1.0\%=0.89\%$$

(21)

$$\mathrm{Alternative} \mathrm{B} TSCR\left(B\right)=y\left(1.07\%\right)=1.90\%\to \mathit{TCAR}\mathrm{(B)}=1.90\%-1.07\%=0.83\%$$

(22)

$$\mathrm{Alternative} \mathrm{C} TSCR\left(C\right)=y\left(0.5\%\right)=2.27\%\to \mathit{TCAR}\mathrm{(C)}=2.27\%-0.5\%=1.77\%$$

(23)

Using an AHP decision model (delineated in Figure 6), the best alternative is selected based on two criteria: safety cost and safety risk. The SIR values represent the safety resource allocation criterion, and the TCAR values represent the safety risk criterion. The alternative with the minimum cost and minimum risk is selected.

The relative importance of the three alternatives based on both criteria, cost and risk, is shown through the following comparison matrices:

$$A_C=\left[\begin{array}{ccc}1& 0.93& 2\\ 1.07& 1& 2.14\\ 0.5& 0.47& 1\end{array}\right]\hspace{1em}\hspace{1em}\hspace{1em}{A}_R=\left[\begin{array}{ccc}1& 1.07& 0.5\\ 0.93& 1& 0.47\\ 1.99& 2.13& 1\end{array}\right]$$

(24)

Its column’s sum normalizes each matrix:

$$\mathrm{c}\mathrm{o}\mathrm{l}\mathrm{u}\mathrm{m}\mathrm{n} \mathrm{s}\mathrm{u}\mathrm{m} AC=\left(\begin{array}{ccc}2.57& 2.40& 5.14\end{array}\right)\hspace{1em}\hspace{1em}\hspace{1em}\mathrm{c}\mathrm{o}\mathrm{l}\mathrm{u}\mathrm{m}\mathrm{n} \mathrm{s}\mathrm{u}\mathrm{m} AR=\left(\begin{array}{ccc}3.92& 4.20& 1.97\end{array}\right)$$

(25)

As follows:

$$N_C=\left[\begin{array}{ccc}0.39& 0.39& 0.39\\ 0.42& 0.42& 0.42\\ 0.19& 0.19& 0.19\end{array}\right]\hspace{1em}\hspace{1em}\hspace{1em}{N}_R=\left[\begin{array}{ccc}0.26& 0.26& 0.26\\ 0.24& 0.24& 0.24\\ 0.51& 0.51& 0.51\end{array}\right]$$

(26)

The row averages of each matrix are the scores of each alternative according to each criterion:

$$W_C=\left(\begin{array}{c}WCA\\ WCB\\ WCC\end{array}\right)=\left(\begin{array}{c}0.39\\ 0.42\\ 0.19\end{array}\right)\hspace{1em}\hspace{1em}\hspace{1em}{W}_R=\left(\begin{array}{c}WRA\\ WRB\\ WRC\end{array}\right)=\left(\begin{array}{c}0.26\\ 0.24\\ 0.51\end{array}\right)$$

(27)

4.1.5. Case Study 1—Consistency Test

To test the consistency of the $A_C$ and $A_R$ matrices, we compute the following:

$$n_{\mathrm{m}\mathrm{a}\mathrm{x}\_C}=\sum A_C·W_C; n_{\mathrm{m}\mathrm{a}\mathrm{x}\_R}=\sum A_R·W_R$$

(28)

The Consistency Ratio (CR) is calculated for each as follows:

$$CI=Consistency Index={\displaystyle \frac{n_{\mathrm{m}\mathrm{a}\mathrm{x}}-n}{n-1}}; n=3$$

(29)

$$RI=Random Consistency={\displaystyle \frac{1.98\left(n-2\right)}{n}}=0.66$$

(30)

$$CR={\displaystyle \frac{CI}{RI}}\le 0.1$$

(31)

That indicates that the AHP used is consistent.

4.1.6. Case Study 1—AHP Test 1: High Priority to Safety Risk

The first AHP test considers that safety risk importance is four times higher than safety cost:

$$WCost=0.2 WRisk=0.8$$

(32)

The comparison matrix is therefore

$$A=\left[\begin{array}{cc}1& 0.25\\ 4& 1\end{array}\right]$$

(33)

The relative weight matrix is

$$N=\left[\begin{array}{cc}WCost& WCost\\ WRisk& WRisk\end{array}\right]=\left[\begin{array}{cc}0.2& 0.2\\ 0.8& 0.8\end{array}\right]$$

(34)

The columns of N are equal, indicating that the decision-making is consistent.

The final scores for the three alternatives are

$$U_A=WCost\ast WCA+WRisk\ast WRA=0.282$$

(35)

$$U_B=WCost\ast WCB+WRisk\ast WRB=0.274$$

(36)

$$U_C=WCost\ast WCC+WRisk\ast WRC=0.445$$

(37)

Alternative B, with the minimum ranking score (0.274), presenting the minimum cost and minimum risk, is selected when the risk importance is four times higher than the cost. This finding emphasizes the key role of risk assessment and risk weight in the allocation of safety resources.

4.1.7. Case Study 1—AHP Test 2: High Priority to Safety Cost

The second AHP test considers that safety cost importance is four times higher than safety risk:

$$WCost=0.8 WRisk=0.2$$

(38)

The comparison matrix is therefore

$$A=\left[\begin{array}{cc}1& 4\\ 0.25& 1\end{array}\right]$$

(39)

The relative weight matrix is

$$N=\left[\begin{array}{cc}WCost& WCost\\ WRisk& WRisk\end{array}\right]=\left[\begin{array}{cc}0.8& 0.8\\ 0.2& 0.2\end{array}\right]$$

(40)

The columns of N are equal, indicating that the decision-making is consistent.

The final scores for the three alternatives are

$$U_A=WCost\ast WCA+WRisk\ast WRA=0.362$$

(41)

$$U_B=WCost\ast WCB+WRisk\ast WRB=0.381$$

(42)

$$U_C=WCost\ast WCC+WRisk\ast WRC=0.257$$

(43)

Alternative C, with the minimum ranking score (0.257), presenting minimum cost and minimum risk, is selected when the cost is more important than the risk.

4.1.8. Case Study 1—AHP Test 3: No Criteria Are Prioritized

The third AHP test considers that safety cost and safety risk importance are equal:

$$WCost=0.5 WRisk=0.5$$

(44)

The comparison matrix is therefore

$$A=\left[\begin{array}{cc}1& 1\\ 1& 1\end{array}\right]$$

(45)

The relative weight matrix is

$$N=\left[\begin{array}{cc}WCost& WCost\\ WRisk& WRisk\end{array}\right]=\left[\begin{array}{cc}0.5& 0.5\\ 0.5& 0.5\end{array}\right]$$

(46)

The columns of N are equal, indicating that the decision-making is consistent.

The final ranking scores for the three alternatives are

$$U_A=WCost\ast WCA+WRisk\ast WRA=0.322$$

(47)

$$U_B=WCost\ast WCB+WRisk\ast WRB=0.327$$

(48)

$$U_C=WCost\ast WCC+WRisk\ast WRC=0.351$$

(49)

Alternative A, with the minimum ranking score (0.322), presenting minimum cost and minimum risk, is selected when cost and risk criteria are equally important.

4.1.9. Case Study 1—Discussion

The results of the AHP decision model are summarized in Figure 7 and show the following:

• Alternative B will be preferred when the contractor wishes to avoid risk as much as possible (the risk weight is high—risk aversion contractor).
• Alternative C will be preferred when the contractor wishes to save expenses (the cost weight is high).
• Alternative A will be preferred when the weights of risks and costs are equal—what goes along with the optimal investment model.

These findings confirm the optimal model and state further that there is a need to create an environment that prevents contractors from taking safety risks by giving higher weight to the safety issues when selecting contractors and corresponding regulations.

Allocating a supplement of USD 6710 of the safety budget, which leads to a total safety budget of 1.07% (Alternative B), is found to be preferable when the contractor wishes to invest in safety resources to mitigate risks. When comparing this relatively modest expense to the potential costs associated with construction accidents, it becomes clear that prioritizing safety expenditures is essential.

The economic consequences of a severe accident can be substantial, encompassing not only direct costs but also the long-term effects on the individuals involved, their families, and the overall project. By proactively investing in safety measures, construction companies can mitigate risks, prevent accidents, and foster a culture of well-being.

4.1.10. Case Study 1—Worker’s Questionnaire

As part of this research, construction site workers were asked to complete a questionnaire (detailed in

Table 1. Worker’s questionnaire #1.

Question Number	Question Drafting	Mean	Standard Deviation
1	How do you assess safety on a construction site during the swinging of cargo using a crane? For example, how would you evaluate safety during the swinging of a concrete heater for wall casting?	7.23	2.27
2	To what extent do you estimate that the implementation of hazardous situation identification technology would enhance the safety rating in the scenario described in the previous question?	7.77	1.66
3	What safety rating would you assign to the execution of plaster works on the balcony bottom at the construction site where you work?	6.63	1.53
4	To what extent do you estimate that using a body camera for identifying hazardous situations would improve the safety rating for the execution of plaster works on the balcony bottom as mentioned in the previous question?	7.83	1.83
5	What safety rating would you assign to the execution of works on the building’s exterior fronts (e.g., exterior plaster, rigid cover, etc.)?	6.66	1.45
6	To what extent do you estimate that using a European scaffold would improve the safety rating in the previous question?	7.43	1.79
7	What safety rating would you assign to the construction site while a cement mixer is entering it?	6.31	2.2
8	To what extent do you estimate that an extra worker, whose duty is only to direct the entrance and exit to the construction site, would improve the safety rating in the previous question?	8.97	1.03
9	On a scale of 1–10, please rate the level of safety that you feel during daily work on the construction site.	6.77	1.64

) that focused on safety in construction, incorporating technological and applied solutions as presented in the previously introduced methodology. The objective of the questionnaire was to assess the safety climate at the construction sites of the surveyed company and to validate and support the outcomes and results derived from the presented model.

Through analysis of questionnaires, we aim to estimate the relationship between perceived safety levels in specific construction tasks and the subjective safety climate reported by workers and assess the research hypotheses and the model outcomes. The questionnaire sample encompassed 30 workers, and the means and standard deviations are presented in Table 1. Questions 1–3, 5, and 7 indicate insufficient safety regarding crane operation, hazardous situation assessment, plaster works, exterior envelope, and concrete pouring works (means range 6.63–7.77), indicating the need for further allocation of safety resources to these construction safety issues. Question 4 indicates that the use of body cameras contributes to plaster works safety in the workers’ perception (7.83), and Questions 6 and 8 indicate that the allocation of safety resources in crane signaling and concrete mixers maneuvering in the construction sites are perceived as effective for safety by the site workers (7.43–8.97). The questionnaire indicates that the safety issues of crane operation, exterior envelop works, and concrete pouring need to be further addressed and that the assimilation of safety preventive measures of body camera, crane signaling, and concrete mixers maneuvering control were perceived effective measures in the workers’ safety perception.

Question 9 assessed workers’ perception of the safety climate. Comparing the results of questions 8 and 9 reveals that workers believe having a directing worker significantly improves safety practices when working with trucks at the construction site.

Additionally, workers were asked to suggest their solutions to improve the safety climate on the construction site. The questionnaire responses are detailed in

Table 2. Worker’s questionnaire #2.

Worker #	Do You Have Any Suggestions for Improving Safety in Construction Sites?
Worker 1	Enforcing
Worker 2	Order and organization
Worker 3
Worker 4
Worker 5
Worker 6	Guidance days for workers, conducting surprise inspections, and more…
Worker 7	More supervision, confirming regulations implementation, strict punishment
Worker 8
Worker 9
Worker 10
Worker 11	I believe a more effective approach would involve continuous supervision with a wide reach. This means having safety supervisors and project safety agents closely connected to the ongoing work. Regular site tours would help identify potential hazards, including pre-planned risky situations. Implementing safety protocols for specific tasks, along with clear instructions and appropriate consequences, would enhance the overall safety of the construction site.
Worker 12
Worker 13	To make sure that foremen will take care of safety and not execution
Worker 14	From my perspective, safety professionals are increasingly burdened with safety paperwork to shift accountability. However, prioritizing worker advocacy holds greater significance. It is essential to prioritize worker safety and find solutions for safe work practices, even if it increases project costs. In summary, prioritizing safety over cost savings is critical: it’s the cause of disasters.
Worker 15
Worker 16	A safety representative will enter hazardous areas and oversee safety.
Worker 17	Due to the limitations of a foreman, separate manpower is necessary to supervise both work-in-progress and safety domains.
Worker 18
Worker 19
Worker 20
Worker 21	To enhance safety on construction sites, it is essential to provide guidance and strict supervision and utilize technological means for monitoring and improving worker behavior. Lessons learned and recommendations should be implemented.
Worker 22	Consider appointing a safety officer responsible for daily safety inspections, with the authority to suspend workers who violate safety rules, even minor ones, and impose fines. This appointment should be made by the project owner rather than the contractor.
Worker 23	Providing instructions to workers and imposing fines for safety violations
Worker 24
Worker 25
Worker 26
Worker 27
Worker 28
Worker 29
Worker 30
Worker 31
Worker 32
Worker 33	After the main contractor has fulfilled all requirements related to equipment and guidance, the constructor is allowed to impose fines on workers or subcontractors who violate the safety orders they have agreed to
Worker 34	Implement ongoing education programs for management and work teams.
Worker 35

below:

Based on sample results and research conducted at Ben Gurion University in Beer Sheva [8], it is evident that choosing alternative B and implementing the suggested solutions significantly reduced the average risk in construction works, as expected from the model’s recommendations.

4.2. Case Study 2

4.2.1. Case Study 2—Cost Analysis and Alternatives

The second case study is an infrastructure project in southern Israel for building pump shafts for a desalination plant and connecting them to the sea. The estimated project duration is 36 months. The project’s scope, including fees, is estimated at USD 11M. Additionally, a safety budget of about USD 200,000 was allocated for safety measures, including safety measures for water stay, which is 1.8% of the total project scope. The safety measures used in the project did not apply technological solutions. It is worth noting that compared to the first case study, the current case study is riskier and more complex and is of a larger scale. The safety budget allocated at the beginning of the project was much lower, which caused many work accidents at the project site. Due to the allocation of additional safety resources, risk prevention was improved, and the number of work accidents was notably lowered.

The TPB of the project is USD 11,000,000. A cost analysis of safety resources allocated is conducted for each alternative as follows:

• Alternative A considers allocating an optimal safety investment ratio that is 1% of the total project cost. The cost to the owner, including safety investment according to alternative A, is therefore USD 11,110,000.
• Alternative B considers allocating an initial safety investment ratio that is 0.5% of the total project cost. The cost to the owner, including initial safety investment according to alternative B, is therefore USD 11,055,000. Considering the optimal safety investment ratio of 1%, an upgraded safety investment is possible with the extra 0.5% available safety budget. However, due to the large number of accidents that occurred at the site, the safety budget was upgraded to a total safety budget of USD 200,000. This results in a supplement of the safety budget of USD 90,000 but without applying any advanced technological solutions. Allocating a supplement of USD 90,000 to the safety budget leads to a total safety budget of 1.8%.
• Alternative C considers allocating a modest safety investment ratio that is 0.5% of the total project cost. The cost to the owner, including initial safety investment according to alternative B, is therefore USD 11,055,000.

4.2.2. Case Study 2—AHP for Decision-Making

The three alternatives suggest allocating different SIRs of the total project scope:

Alternative A: SIR(A) = 1%

(50)

Alternative B: SIR(B)= 1.8%

(51)

Alternative C: SIR(C) = 0.5%

(52)

According to Equation (19) the TSCR and TCAR values are as follows:

$$\mathrm{Alternative} \mathrm{A} TSCR\left(A\right)=y\left(1.0\%\right)=1.89\% \to \mathit{TCAR}\mathrm{(A)}=1.89\%-1.0\%=0.89\%$$

(53)

$$\mathrm{Alternative} \mathrm{B} TSCR\left(B\right)=y\left(1.8\%\right)=2.99\%\to \mathit{TCAR}\mathrm{(B)}=2.99\%-1.8\%=1.19\%$$

(54)

$$\mathrm{Alternative} \mathrm{C} TSCR\left(C\right)=y\left(0.5\%\right)=2.27\% \to \mathit{TCAR}\mathrm{(C)}=2.27\%-0.5\%=1.77\%$$

(55)

Consequently, the comparison matrices are

$$A_C=\left[\begin{array}{ccc}1& 0.56& 2\\ 1.8& 1& 3.6\\ 0.5& 0.28& 1\end{array}\right] A_R=\left[\begin{array}{ccc}1& 0.75& 0.5\\ 1.34& 1& 0.67\\ 1.99& 1.49& 1\end{array}\right]$$

(56)

Its column’s sum normalizes each matrix:

$$\mathrm{c}\mathrm{o}\mathrm{l}\mathrm{u}\mathrm{m}\mathrm{n} \mathrm{s}\mathrm{u}\mathrm{m} AC=\left(\begin{array}{ccc}3.3& 1.83& 6.6\end{array}\right)\hspace{1em}\hspace{1em}\hspace{1em}\mathrm{c}\mathrm{o}\mathrm{l}\mathrm{u}\mathrm{m}\mathrm{n} \mathrm{s}\mathrm{u}\mathrm{m} AR=\left(\begin{array}{ccc}4.33& 3.24& 2.18\end{array}\right)$$

(57)

As follows:

$$N_C=\left[\begin{array}{ccc}0.30& 0.30& 0.30\\ 0.55& 0.55& 0.55\\ 0.15& 0.15& 0.15\end{array}\right] N_R=\left[\begin{array}{ccc}0.23& 0.23& 0.23\\ 0.31& 0.31& 0.31\\ 0.46& 0.46& 0.46\end{array}\right]$$

(58)

The scores of each alternative according to each criterion:

$$W_C=\left(\begin{array}{c}WCA\\ WCB\\ WCC\end{array}\right)=\left(\begin{array}{c}0.30\\ 0.55\\ 0.15\end{array}\right) W_R=\left(\begin{array}{c}WRA\\ WRB\\ WRC\end{array}\right)=\left(\begin{array}{c}0.23\\ 0.31\\ 0.46\end{array}\right)$$

(59)

The consistency ratio of the $A_C$ and $A_R$ matrices was computed and was found to be less than 0.1. That indicates that the AHP used is consistent.

4.2.3. Case Study 2—AHP Test 1: High Priority to Safety Risk

The first AHP test considers 80% for safety risk and 20% for safety cost.

The final scores for the three alternatives are

$$U_A=WCost\ast WCA+WRisk\ast WRA=0.246$$

(60)

$$U_B=WCost\ast WCB+WRisk\ast WRB=0.356$$

(61)

$$U_C=WCost\ast WCC+WRisk\ast WRC=0.398$$

(62)

Alternative A, with the minimum ranking score (0.246), presenting minimum cost and minimum risk, is selected when risk importance is four times higher than cost. This finding emphasizes that an excessive safety investment is not always preferable, and it is better to invest an optimal budget for risk mitigation of safety measures using advanced technological solutions.

4.2.4. Case Study 2—AHP Test 2: High Priority to Safety Cost

The first AHP test considers 20% for safety risk and 80% for safety cost.

The final scores for the three alternatives are

$$U_A=WCost\ast WCA+WRisk\ast WRA=0.289$$

(63)

$$U_B=WCost\ast WCB+WRisk\ast WRB=0.498$$

(64)

$$U_C=WCost\ast WCC+WRisk\ast WRC=0.213$$

(65)

Alternative C, with the minimum ranking score (0.213), presenting minimum cost and minimum risk, is selected when the cost is more important than the risk.

4.2.5. Case Study 2—AHP Test 3: No Criteria Are Prioritized

The third AHP test considers that safety cost and safety risk importance are equal.

The final ranking scores for the three alternatives are

$$U_A=WCost\ast WCA+WRisk\ast WRA=0.267$$

(66)

$$U_B=WCost\ast WCB+WRisk\ast WRB=0.427$$

(67)

$$U_C=WCost\ast WCC+WRisk\ast WRC=0.306$$

(68)

Alternative A, with the minimum ranking score (0.267), presenting minimum cost and minimum risk, is selected when cost and risk criteria are equally important.

4.2.6. Case Study 2—Discussion

The results of the AHP decision model are summarized in Figure 8 and show the following:

• Alternative B will NOT be preferred when the contractor wishes to avoid risk as much as possible due to the high number of accidents at the project site but without the use of technological solutions. Instead, alternative A is selected.
• Alternative C will be preferred when the contractor wishes to save expenses (the cost weight is high).
• Alternative A will be preferred when the weights of risks and costs are equal—which goes along with the optimal investment model.

These findings confirm the optimal model and state further that although safety investments are critical to safety management and risk prevention, there is a point beyond which additional investment without the use of advanced technologies might cause the return on safety investment to be negative. This goes along with the Cost of Safety (COS) model discussed by [45], which presents a theoretical equilibrium point that reflects an optimum investment in work accident prevention.

Allocating a supplement of USD 90,000 to the safety budget was beyond the optimum investment, so investing a total safety budget of 1.8% (Alternative B) is found to be an ineffective investment for accident prevention in this test project.

Furthermore, the results show that the complexity and scale of projects affect the decisions made on safety resource allocation for risk mitigation.

5. Conclusions

Understanding the specific risks associated with different construction tasks and predicting these risks based on the nature of the work, duration, and worker profiles can significantly enhance safety on construction sites. This study highlights that fostering a corporate culture prioritizing safety and raising worker awareness of risk identification can reduce work-related accidents. To achieve this, a risk assessment that involves analyzing project-specific risk factors to formulate and implement an effective construction risk management strategy should be conducted. By identifying potential risks early, project teams can proactively address them.

Project managers, safety managers, and workers on construction sites all play crucial roles in ensuring safety through informed decision-making and proactive risk management. In this paper, an AHP model was developed and tested for safety resource allocation according to their preferences regarding safety risks and safety costs. It incorporates safety performance and cost criteria for an optimal allocation of safety resources. The model’s outcomes reveal that the safety risk criterion must be significantly higher than the cost criterion to acquire an optimal safety solution.

The proposed methodology was tested on two case studies that differ in complexity and scope. In case study 1, the initial safety budget was 0.5%. Following a recommendation to increase it to 1%, the remaining safety budget was allocated based on the model’s recommendations. Several technological solutions were adopted during implementation, including body cameras, drones, scaffolding adhering to European regulations, an additional safety inspector, and a traffic management worker at the construction site. The impact of these solutions was evident in the subsequent risk survey. After implementing the suggested solutions, the average risk associated with construction work decreased significantly, aligning with the expected outcomes from the model’s recommendations. Although the deviation from the budget was relatively small for a USD 9M construction project, it underscores the importance of optimizing safety investments for each specific project. It is crucial to recognize that the potential costs of severe accidents far outweigh the expenses incurred by safety investments. Therefore, leveraging models like the one developed in this study is essential to ensure optimal safety practices and effectively mitigate risks. Case study 2, carried out in an infrastructure project, reveals that the SIR of 1.8% was an excessive investment found not effective due to a lack of technological measures [45].

The results show that leveraging technology and utilizing advanced tools and software for construction risk management can significantly enhance safety performance. These strategies can help track risks, automate processes, and provide real-time insights for project stakeholders. By integrating these practices, construction companies can create safer work environments, reduce accidents, and improve overall safety performance. In addition, such advanced solutions can monitor risks throughout the project’s duration. Regularly assessing risk factors, adapting strategies as needed, and staying informed about any changes or emerging risks are important steps toward mitigating work accidents [60]. The proposed model can be implemented dynamically in construction sites using safety key leading indicators such as near-miss events and the safety climate to adapt the safety weight in the AHP model and allocate safety resources accordingly.

The limitation of the research is the limited scope of case studies; it is recommended to expand the research to SME construction projects and to assimilate the model with the safety and project management decision-making process [7,9]. It is also worth noting that no investigations of near accidents were conducted before implementing this model.

Future work should outline how to handle identified risks, including preventive measures, contingency plans, and emergency protocols. Future endeavors will focus on refining this model to cater to specific roles during construction to provide a practical and profitable alternative, beginning with selecting appropriate personal protective equipment. The refined model will also offer tailored recommendations based on the work stage and address specific timeframes within the workday. Additionally, it is essential to explore allocating a portion of the safety budget toward stimulating workers to embrace a safety-conscious culture. Integrating them into technological guidance systems, such as augmented reality tools, can achieve this objective. Safety initiatives at the company level can also be pivotal in fostering a safety climate. An advocacy program should be implemented to promote awareness of the investment’s viability. This program would target entrepreneurs and managers, emphasizing the importance of safety and encouraging their active participation in driving positive safety changes. Further research can also be conducted to see how workers’ behavior and training affect the model’s outputs and efficiency.