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Purpose: The aim of this study is to establish key performance indicators (KPIs) that can be deployed in the evaluation and promotion of safety performance of building developer`s in Nigeria. Design/methodology/approach: A thorough review of the literature was performed to generally identify sets of KPIs used to evaluate the safety performances of building developer`s during construction. Interviews and focus group discussions were conducted with eleven (11) subject matter experts and professionals to ensure inclusion, validation and clarity of the indicators and to further provide agreement/disagreement, and importance ratings for the identified KPIs. The KPIs was grouped into appropriate categories, keeping out redundant KPIs, and ensuring KPIs are clear and measurable. The Relative Importance Index (RII) and Mean values were computed. Findings: Results from the study consist of 137 KPIs, grouped into 9 categories including: Planning, design and procurement, communication on & maintenance of effective safety behaviour, construction safety policy, construction safety personnel, management effort and support, safety training and enlightenment, administration of safety processes, investigation and reporting of accidents, and rewards and sanctions for project stakeholders. These KPIs were observed to be at different levels of importance from the respondents. The following KPIs were considered based on their extreme importance judging from the respective RII values: communicating safety requirements to designer (98.18%), safety performance set as part of contractor selection criterion (96.36%), availability and accessibility of the relevant insurance policies (98.18%) and appropriate issuance of motivational directives by the top management (100%). Originality/value: Many studies have been done in the past where KPIs as it relates to construction safety were identified. However, the specificity of these KPIs to countries other than Nigeria requires similar research be conducted to identify building developer safety performance KPIs for the Nigerian construction industry.

This study aimed to investigate the effects of the splitter-island on pedestrian safety at roundabouts, considering the interaction between pedestrians and vehicles. Traffic accident statistics have traditionally been used to assess traffic safety. This method has severe limitations when used to investigate the impact of a particular feature of traffic facilities on safety at a microscopic level. Most previous research on surrogate safety measures (SSMs) had, on the other hand, focused on studying the safety of traffic operation conditions. The impact of a particular geometric feature of a roundabout on safety has so far received little attention in the literature. Therefore, we evaluated pedestrian safety in the presence and absence of physical splitter-islands by taking advantage of comparative statistical analysis (SA) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) methods. The Towa-Cho roundabout in Nagano prefecture in Japan was chosen for this study because it allows for five approaches, three with a splitter-island and two without. From 33-hr video data, a total of 343 and 183 potential pedestrian-vehicle conflicts were detected in three crosswalks with splitter-islands and two crosswalks without splitter-islands, respectively. The minimum time-to-collision (TTCmin), post-encroach-time (PET), maximum speed (MaxS), and maximum deceleration-to-safety (DTS) measures were monitored using particle tracking velocimetry (PTV) software for potential pedestrian-vehicle conflicts. It was found that geometric differences were significantly reflected by SSMs, and TTCmin was the most efficient among all. It was concluded that the splitter-island had a better safety performance than the roundabout approach.

Retrofitting works have become increasingly important in the construction industry as they play an effective role in providing solutions to maintain, upgrade or change the functions of existing or aged buildings. Very often, safety issues of retrofitting works are underestimated as such works are normally considered small projects/works, in which the accidents might not be reported in the short term. Therefore, qualitative indicators (i.e. safety climate and safety behaviour) have become significant contributors in evaluating the organisational safety performance. This article aimed to examine the relationship between the safety climate and safety performance in the retrofitting works context. The safety climate of retrofitting works was measured by adopting the NOSACQ-50 questionnaire, while the safety performance was examined by three indicators comprising safety compliance, safety participation, and occupational injuries. A total of 264 valid questionnaire responses were collected from the local retrofits work sites in Australia. PLS-SEM technique was used to examine the relationship and estimate the parameters of the structural model. The results indicate that there is a significant positive relationship (0.60) between safety climate and safety performance in retrofitting works.

PurposeThis study proposes a self-regulatory framework to enhance safety performance at the construction stage among building developers.Design/methodology/approachExtant literature identified 137 potential factors that influence the construction safety performances of building developers. Focus group discussions and interviews were conducted with 11 panels of experts and professionals. The Relative Importance Index (RII) was used to analyse the response feedback described in a similar paper. In this study, the survey tool used was set up with 40 variables grouped into eight latent variables in the framework, which were agreed and certified as “extremely important” by the panel. Based on random sampling, data were collected from 229 valid respondents. Structural equation modelling (SEM) technique using Smart PLS software was then used to analyse the respondent's feedback.FindingsThe results show that safety administration and processes, effective communication of safety behaviour, significantly influenced safety performance on a construction site with β values of 0.330 and 0.431 along with t values of 3.005 and 2.547 at p < 0.1, respectively. These factors, among others, provide a distinct approach to understanding and improving on-site construction safety. The study findings will potentially benefit building professionals and other stakeholders by improving awareness of safety practices.Research limitations/implicationsThe study may not have covered all possible factors that influence the construction safety performance of building developers. Also, the generalizability and transferability of the research outcome to the construction industry wide use is also limited when reference is made to the characteristics of the research respondents and/or participants. In addition, validation of the framework by five professionals is rather small.Practical implicationsTheoretically, the framework through the identified factors provide a distinct approach to understanding and improving on-site construction safety through voluntary adherence to self-regulatory standard where there are no enforceable laws and regulations to promote safety. The study findings will potentially benefit building professionals and other stakeholders by improving awareness of the health and safety practices of the construction industry.Originality/valueMany research efforts have developed frameworks and models for construction safety. However, the particularity of these frameworks to countries other than Nigeria requires similar research to be conducted to enhance the safety performance of building developers.

This study has two goals: First, to fill a gap in the use of the predictive approach for evaluating road safety performance in Ethiopia, the most recent analytical methods of the HSM predictive approach in IHSDM software were used to evaluate the safety and operational effects of the existing roadway geometric design. Second, to assure safety and a sustainable transportation system, the relative safety effects of design changes made to hazardous road segments were quantified. Based on the Crash Prediction Module (CPM) evaluation of IHSDM software, the study identified fifteen hazardous road segments on the existing rural two-lane roads of Addis Ababa to Chacha and Addis Ababa to Dillela. The design changes made to the hazardous road segments have resulted in a remarkable reduction in crash rate, especially on the first top five hazardous segments, where incredible improvements have been observed. The total safety benefits acquired by applying engineering mitigations to the fifteen identified hazardous segments are described as follows: 17.18% reduction in total crash frequency (crashes/year), 58.94% reduction in crash rate (crashes/km/year), and 58.86% reduction in travel crash rate (crashes/million veh-km). In general, the study’s findings underlined the effectiveness of performance-based road safety evaluation and design in providing safe, efficient, and economically-feasible roadway infrastructure. The IHSDM reports and graphical outputs assist decision-making in the roadway design process by providing a quantitative evaluation of the safety impact of various design features and identifying roadway segments with safety concerns. Additionally, IHSDM is a tool capable of saving time for Road Safety Audit (RSA) teams. The paper also outlined the need for a computerized crash database recording system in Ethiopia to develop jurisdiction-specific Safety Performance Functions (SPFs).

Exposure to risk is a theoretically important correlate of crash risk, but many safety performance functions (SPFs) for pedestrian and bicycle traffic have yet to include the mode-specific measures of exposure. When SPFs are used in the systematic approach to assess network-wide crash risk, the omission of the exposure potentially could affect the identification of high-risk locations. Using crash data from Minneapolis, this study constructs and compares two sets of SPFs, one with pedestrian and bicycle exposure variables and the other without, for network-wide intersection and mid-block crash models. Inclusion of mode-specific exposure variables improves model validity and measures of goodness-of-fit and increases accuracy of predictions of pedestrian and bicycle crash risk. Including these exposure variables in the SPFs changes the distribution of high-risk locations, including the proportion of high-risk locations in low-income and racially concentrated areas. These results confirm the importance of incorporating exposure measures within SPFs and the need for pedestrian and bicycle monitoring programs to generate exposure data.

One of the objectives of road infrastructure sustainability is to ensure that users are treated equally and their quality of life is improved by providing better mobility and traffic safety. When designing roads, it is important to evaluate different design criteria alternatives - in this case, we look at traffic safety principles. For this, we used the Safety Performance Functions (SPF) tool to obtain the expected crash frequency. The data used were Medellín’s crash records from 2012 to 2016, as well as the geometric features and traffic conditions at signalized intersections. A negative binomial model was fitted to estimate the SPF. Exposure, geometry, and traffic volume were found to be statistically significant in determining the expected crash frequency for collisions where there was property damage only (PDO) and fatal or injury (FI). It was found that accidents were less likely on T-junctions compared to four-leg junctions, one-way approaches were found to be safer whereas right turns were found to increase collisions causing FI. 

Human errors are widely considered among the major causes of road traffic. Furthermore, it is estimated that more than 90% of vehicles crashes causing fatal and permanent injuring are directly related with mental tiredness, fatigue and drowsiness of the drivers. In particular, the driving drowsiness is recognized as a crucial aspect in the context of road safety, since drowsy drivers can suddenly lose control of the car. Moreover, the driving drowsiness episodes mostly appear suddenly without any prior behavioral evidence. The present study aimed at characterizing the insurgence of drowsiness in car drivers by means of a multimodal neurophysiological approach to develop a synthetic Electroencephalographic (EEG)-based index able to detect drowsy events. The study involved 19 participants in a simulated scenario structured in a sequence of driving tasks under different situations and traffic conditions. The experimental conditions were designed to induce prominent mental drowsiness in the final part. The EEG-based index, so called “MDrow index”, was developed and validated to detect the driving drowsiness of the participants. The MDrow index was derived from the Global Field Power calculated in the Alpha EEG frequency band over the parietal brain sites. The results demonstrated the reliability of the proposed MDrow index in detecting the driving drowsiness experienced by the participants, resulting also more sensitive and timely sensible with respect to more conventional autonomic parameters, such as the Eye Blinks Rate and the Heart Rate Variability, and to subjective measurements (self - reports).

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

Learning from incidents (LFI) is a process to seek, analyse, and disseminate the severity and causes of incidents, and take corrective measures to prevent the recurrence of similar events. However, the effects of LFI on the learner’s safety performance remain unexplored. This study aimed to identify the effects of the major LFI factors on the safety performance of workers. A questionnaire survey was administered among 210 construction workers in China. A factor analysis was conducted to reveal the underlying LFI factors. A stepwise multiple linear regression was performed to analyse the relationship between the underlying LFI factors and safety performance. A Bayesian Network (BN) was further modelled to identify the probabilistic relational network between the underlying LFI factors and safety performance. The results of BN modelling showed that all the underlying factors were important to improve the safety performance of construction workers. Additionally, sensitivity analysis revealed that the two underlying factors—information sharing and utilization and management commitment—had the largest effects on improving workers’ safety performance. The proposed BN also helped find out the most efficient strategy to improve workers’ safety performance. This research may serve as a useful guide for better implementation of LFI practices in the construction sector.