Interlocked projects in safety competency and safety effectiveness indicators in the construction sector

Safety culture is a concept that has long been accepted in high risk industries such as aviation, nuclear industries and mining, however, considerable research is now also being undertaken within the construction sector. This paper discusses three recent interlocked projects undertaken in the Australian construction industry. The first project examined the development and implementation of a safety competency framework targeted at safety critical positions (SCP's) across first tier construction organisations. Combining qualitative and quantitative methods, the project: developed a matrix of SCP's (n=11) and safety management tasks (SMTs; n=39); mapped the process steps for their acquisition and development; detailed the knowledge, skills and behaviours required for all SMTs; and outlined potential organisational cultural outcomes from a successful implementation of the framework. The second project extended this research to develop behavioural guidelines for leaders to drive safety culture change down to second tier companies and to assist them to customise their own competency framework and implementation guidelines to match their aspirations and resources. The third interlocked project explored the use of safety effectiveness indicators (SEIs) as an industry-relevant assessment tool for reducing risk on construction sites. With direct linkages to safety competencies and SMT's, the SEIs are the next step towards an integrated safety cultural approach to safety and extend the concept of positive performance indicators (PPIs) by providing a valid, reliable, and user friendly measurement platform. Taken together, the results of the interlocked projects suggest that industry engaged collaborative safety culture research has many potential benefits for the construction industry.

Applying Bayesian hierarchical models to examine motorcycle crashes at signalized intersections

Motorcycles are overrepresented in road traffic crashes and particularly vulnerable at signalized intersections. The objective of this study is to identify causal factors affecting the motorcycle crashes at both four-legged and T signalized intersections. Treating the data in time-series cross-section panels, this study explores different Hierarchical Poisson models and found that the model allowing autoregressive lag 1 dependent specification in the error term is the most suitable. Results show that the number of lanes at the four-legged signalized intersections significantly increases motorcycle crashes largely because of the higher exposure resulting from higher motorcycle accumulation at the stop line. Furthermore, the presence of a wide median and an uncontrolled left-turn lane at major roadways of four-legged intersections exacerbate this potential hazard. For T signalized intersections, the presence of exclusive right-turn lane at both major and minor roadways and an uncontrolled left-turn lane at major roadways of T intersections increases motorcycle crashes. Motorcycle crashes increase on high-speed roadways because they are more vulnerable and less likely to react in time during conflicts. The presence of red light cameras reduces motorcycle crashes significantly for both four-legged and T intersections. With the red-light camera, motorcycles are less exposed to conflicts because it is observed that they are more disciplined in queuing at the stop line and less likely to jump start at the start of green.

An estimate of road accident costs in Singapore

An earlier study by the Asian Development Bank (ADB) showed that the annual cost of road traffic accidents in 2001 was S$699.36 million which was 0.5% of the annual GDP. This paper attempts to update of the cost estimates of road traffic accidents. More precise methods of computing the human cost, lost output and property damage are adopted which grew in an annual cost of S$610.3 million or 0.338% of the annual GDP in 2003. A more conservative estimate of S$878,000 for fatal accident is also obtained, compared to the earlier figure of S$1.4 million. This study has shown that it is necessary to update the annual traffic accident costs regularly, as the figures vary with the number of accidents which change with time.

Modeling random effect and excess zeros in road traffic accident prediction

Poisson distribution has often been used for count like accident data. Negative Binomial (NB) distribution has been adopted in the count data to take care of the over-dispersion problem. However, Poisson and NB distributions are incapable of taking into account some unobserved heterogeneities due to spatial and temporal effects of accident data. To overcome this problem, Random Effect models have been developed. Again another challenge with existing traffic accident prediction models is the distribution of excess zero accident observations in some accident data. Although Zero-Inflated Poisson (ZIP) model is capable of handling the dual-state system in accident data with excess zero observations, it does not accommodate the within-location correlation and between-location correlation heterogeneities which are the basic motivations for the need of the Random Effect models. This paper proposes an effective way of fitting ZIP model with location specific random effects and for model calibration and assessment the Bayesian analysis is recommended.

Bayesian hierarchical analysis on crash prediction models

Traditional crash prediction models, such as generalized linear regression models, are incapable of taking into account the multilevel data structure, which extensively exists in crash data. Disregarding the possible within-group correlations can lead to the production of models giving unreliable and biased estimates of unknowns. This study innovatively proposes a -level hierarchy, viz. (Geographic region level – Traffic site level – Traffic crash level – Driver-vehicle unit level – Vehicle-occupant level) Time level, to establish a general form of multilevel data structure in traffic safety analysis. To properly model the potential cross-group heterogeneity due to the multilevel data structure, a framework of Bayesian hierarchical models that explicitly specify multilevel structure and correctly yield parameter estimates is introduced and recommended. The proposed method is illustrated in an individual-severity analysis of intersection crashes using the Singapore crash records. This study proved the importance of accounting for the within-group correlations and demonstrated the flexibilities and effectiveness of the Bayesian hierarchical method in modeling multilevel structure of traffic crash data.

Developing targeted countermeasures to improve motorcycle safety

The fatality and injury rate of motorcyclists per registered vehicle are higher than those of other motor vehicles by 13 and 7 times respectively. The crash involvement rate of motorcyclists as a victim party is 58% at intersections and as an offending party is 67% at expressways. Previous research efforts showed that the motorcycle safety programs are not very effective in improving motorcycle safety. This is perhaps due to inefficient design of safety program as specific causal factors may not be well explored. The objective of this study is to propose more sophisticated countermeasures and awareness programs for improving motorcycle safety after analyzing specific causal factors for motorcycle crashes at intersections and expressways. Methodologically this study applies the binary logistic model to explore the at-fault or not-at-fault crash involvement of motorcyclists at those locations. A number of explanatory variables representing roadway characteristics, environmental factors, motorcycle descriptions, and rider demographics have been evaluated. Results shows that the night time crash occurrence, presence of red light camera, lane position, rider age, licence class, and multivehicle collision significantly affect the fault of motorcyclists involved in crashes at intersections. On the other hand, the night time crash occurrence, lane position, speed limit, rider age, licence class, engine capacity, riding with pillion passenger, foreign registered motorcycles, and multivehicle collision has been found to be significant at expressways. Legislate to wear reflective clothes and using reflective markings on the motorcycles and helmets are suggested as an effective countermeasure for reducing their vulnerability. The red light cameras at intersections reduce the vulnerability of motorcycles and hence motorcycle flow and motorcycle crashes should be considered during installation of red light cameras. At signalized intersections, motorcyclists may be taught to follow correct movement and queuing rather than weaving through the traffic as it leads them to become victims of other motorists. The riding simulators in the training centers can be useful to demonstrate the proper movement and queuing at junctions. Riding with pillion passenger and excess speed at expressways are found to significantly influence the at at-fault crash involvement of the motorcyclists. Hence the motorcyclists should be advised to concentrate more on riding while riding with pillion passenger and encouraged to avoid excess speed at expressways. Very young and very older group of riders are found to be at-fault than middle aged groups. Hence this group of riders should be targeted for safety improvement. This can be done by arranging safety talks and programs in motorcycling clubs in colleges and universities as well as community riding clubs with high proportion of elderly riders. It is recommended that the driving centers may use the findings of this study to include in licensure program to make motorcyclists more aware of the different factors which expose the motorcyclists to crash risks so that more defensive riding may be needed.

Hotspot Identification: A Full Bayesian Hierarchical Modeling Approach

This study proposes a full Bayes (FB) hierarchical modeling approach in traffic crash hotspot identification. The FB approach is able to account for all uncertainties associated with crash risk and various risk factors by estimating a posterior distribution of the site safety on which various ranking criteria could be based. Moreover, by use of hierarchical model specification, FB approach is able to flexibly take into account various heterogeneities of crash occurrence due to spatiotemporal effects on traffic safety. Using Singapore intersection crash data(1997-2006), an empirical evaluate was conducted to compare the proposed FB approach to the state-of-the-art approaches. Results show that the Bayesian hierarchical models with accommodation for site specific effect and serial correlation have better goodness-of-fit than non hierarchical models. Furthermore, all model-based approaches perform significantly better in safety ranking than the naive approach using raw crash count. The FB hierarchical models were found to significantly outperform the standard EB approach in correctly identifying hotspots.

Efficient safety information retrieval on construction sites: a preliminary methodology

IT-supported field data management benefits on-site construction management by improving accessibility to the information and promoting efficient communication between project team members. However, most of on-site safety inspections still heavily rely on subjective judgment and manual reporting processes and thus observers’ experiences often determine the quality of risk identification and control. This study aims to develop a methodology to efficiently retrieve safety-related information so that the safety inspectors can easily access to the relevant site safety information for safer decision making. The proposed methodology consists of three stages: (1) development of a comprehensive safety database which contains information of risk factors, accident types, impact of accidents and safety regulations; (2) identification of relationships among different risk factors based on statistical analysis methods; and (3) user-specified information retrieval using data mining techniques for safety management. This paper presents an overall methodology and preliminary results of the first stage research conducted with 101 accident investigation reports.

Quantity of documentation of maltreatment risk factors in injury-related paediatric hospitalisations

Background While child maltreatment is recognised as a global problem, solid epidemiological data on the prevalence of child maltreatment and risk factors associated with child maltreatment is lacking in Australia and internationally. There have been recent calls for action to improve the evidence-base capturing and describing child abuse, particularly those data captured within the health sector. This paper describes the quantity of documentation of maltreatment risk factors in injury-related paediatric hospitalisations in Queensland, Australia. Methods This study involved a retrospective medical record review, text extraction and coding methodology to assess the quantity of documentation of risk factors and the subsequent utility of data in hospital records for describing child maltreatment and data linkage to Child Protection Service (CPS). Results There were 433 children in the maltreatment group and 462 in the unintentional injury group for whom medical records could be reviewed. Almost 93% of the any maltreatment code sample, but only 11% of the unintentional injury sample had documentation identified indicating the presence of any of 20 risk factors. In the maltreatment group the most commonly documented risk factor was history of abuse (41%). In those with an unintentional injury, the most commonly documented risk factor was alcohol abuse of the child or family (3%). More than 93% of the maltreatment sample also linked to a child protection record. Of concern are the 16% of those children who linked to child protection who did not have documented risk factors in the medical record. Conclusion Given the importance of the medical record as a source of information about children presenting to hospital for treatment and as a potential source of evidence for legal action the lack of documentation is of concern. The details surrounding the injury admission and consideration of any maltreatment related risk factors, both identifying their presence and ruling them out are required for each and every case. This highlights the need for additional training for clinicians to understand the importance of their documentation in child injury cases.

Safety leaders’ perceptions of safety culture in a large Australasian construction organisation

This paper reports safety leaders’ perceptions of safety culture in one of Australasia’s largest construction organisations. A modified Delphi method was used including two rounds of data collection. The first round involved 41 semi-structured interviews with safety leaders within the organisation. The second round involved an online quantitative perception survey, with the same sample, aimed at confirming the key themes identified in the interviews. Participants included Senior Executives, Corporate Managers, Project Managers, Safety Managers and Site Supervisors. Interview data was analysed using qualitative thematic analysis, and the survey data was analysed using descriptive statistics. Leaders’ definitions and descriptions of safety culture were primarily action-oriented and some confusion was evident due to the sometimes implicit nature of culture in organisations. Leadership was identified as a key factor for positive safety culture in the organisation, and there was an emphasis on leaders demonstrating commitment to safety, and being visible to the project-based workforce. Barriers to safety culture improvement were also identified, including the subcontractor management issues, pace of change, and reporting requirements. The survey data provided a quantitative confirmation of the interview themes, with some minor discrepancies. The findings highlight that safety culture is a complex construct, which is difficult to define, even for experts in the organisation. Findings on the key factors indicated consistency with the current literature; however the perceptions of barriers to safety culture offer a new understanding in to how safety culture operates in practice.

Integrated health prediction of bridge systems using dynamic object oriented Bayesian networks (DOOBNS)

The serviceability and safety of bridges are crucial to people’s daily lives and to the national economy. Every effort should be taken to make sure that bridges function safely and properly as any damage or fault during the service life can lead to transport paralysis, catastrophic loss of property or even casualties. Nonetheless, aggressive environmental conditions, ever-increasing and changing traffic loads and aging can all contribute to bridge deterioration. With often constrained budget, it is of significance to identify bridges and bridge elements that should be given higher priority for maintenance, rehabilitation or replacement, and to select optimal strategy. Bridge health prediction is an essential underpinning science to bridge maintenance optimization, since the effectiveness of optimal maintenance decision is largely dependent on the forecasting accuracy of bridge health performance. The current approaches for bridge health prediction can be categorised into two groups: condition ratings based and structural reliability based. A comprehensive literature review has revealed the following limitations of the current modelling approaches: (1) it is not evident in literature to date that any integrated approaches exist for modelling both serviceability and safety aspects so that both performance criteria can be evaluated coherently; (2) complex system modelling approaches have not been successfully applied to bridge deterioration modelling though a bridge is a complex system composed of many inter-related bridge elements; (3) multiple bridge deterioration factors, such as deterioration dependencies among different bridge elements, observed information, maintenance actions and environmental effects have not been considered jointly; (4) the existing approaches are lacking in Bayesian updating ability to incorporate a variety of event information; (5) the assumption of series and/or parallel relationship for bridge level reliability is always held in all structural reliability estimation of bridge systems. To address the deficiencies listed above, this research proposes three novel models based on the Dynamic Object Oriented Bayesian Networks (DOOBNs) approach. Model I aims to address bridge deterioration in serviceability using condition ratings as the health index. The bridge deterioration is represented in a hierarchical relationship, in accordance with the physical structure, so that the contribution of each bridge element to bridge deterioration can be tracked. A discrete-time Markov process is employed to model deterioration of bridge elements over time. In Model II, bridge deterioration in terms of safety is addressed. The structural reliability of bridge systems is estimated from bridge elements to the entire bridge. By means of conditional probability tables (CPTs), not only series-parallel relationship but also complex probabilistic relationship in bridge systems can be effectively modelled. The structural reliability of each bridge element is evaluated from its limit state functions, considering the probability distributions of resistance and applied load. Both Models I and II are designed in three steps: modelling consideration, DOOBN development and parameters estimation. Model III integrates Models I and II to address bridge health performance in both serviceability and safety aspects jointly. The modelling of bridge ratings is modified so that every basic modelling unit denotes one physical bridge element. According to the specific materials used, the integration of condition ratings and structural reliability is implemented through critical failure modes. Three case studies have been conducted to validate the proposed models, respectively. Carefully selected data and knowledge from bridge experts, the National Bridge Inventory (NBI) and existing literature were utilised for model validation. In addition, event information was generated using simulation to demonstrate the Bayesian updating ability of the proposed models. The prediction results of condition ratings and structural reliability were presented and interpreted for basic bridge elements and the whole bridge system. The results obtained from Model II were compared with the ones obtained from traditional structural reliability methods. Overall, the prediction results demonstrate the feasibility of the proposed modelling approach for bridge health prediction and underpin the assertion that the three models can be used separately or integrated and are more effective than the current bridge deterioration modelling approaches. The primary contribution of this work is to enhance the knowledge in the field of bridge health prediction, where more comprehensive health performance in both serviceability and safety aspects are addressed jointly. The proposed models, characterised by probabilistic representation of bridge deterioration in hierarchical ways, demonstrated the effectiveness and pledge of DOOBNs approach to bridge health management. Additionally, the proposed models have significant potential for bridge maintenance optimization. Working together with advanced monitoring and inspection techniques, and a comprehensive bridge inventory, the proposed models can be used by bridge practitioners to achieve increased serviceability and safety as well as maintenance cost effectiveness.

When non-significance may be significant : lessons learned from a study of the development, implementation and evaluation of a fleet risk assessment tool

This paper reports on the development and implementation of a self-report risk assessment tool that was developed in an attempt to increase the efficacy of crash prediction within Australian fleet settings. This study forms a part of a broader program of research into work related road safety and identification of driving risk. The first phase of the study involved a series of focus groups being conducted with 217 professional drivers which revealed that the following factors were proposed to influence driving performance: Fatigue, Knowledge of risk, Mood, Impatience and frustration, Speed limits, Experience, Other road users, Passengers, Health, and Culture. The second phase of the study involved piloting the newly developed 38 item Driving Risk Assessment Scale - Work Version (DRAS-WV) with 546 professional drivers. Factor analytic techniques identified a 9 factor solution that was comprised of speeding, aggression, time pressure, distraction, casualness, awareness, maintenance, fatigue and minor damage. Speeding and aggressive driving manoeuvres were identified to be the most frequent aberrant driving behaviours engaged in by the sample. However, a series of logistic regression analyses undertaken to determine the DRAS-WV scale’s ability to predict self-reported crashes revealed limited predictive efficacy e.g., 10% of crashes. This paper outlines proposed reasons for this limited predictive ability of the DRAS-WV as well as provides suggestions regarding the future of research that aims to develop methods to identify “at risk” drivers.

Is diurnal temperature range a risk factor for childhood diarrhea?

Background Previous studies have found that high and cold temperatures increase the risk of childhood diarrhea. However, little is known about whether the within-day variation of temperature has any effect on childhood diarrhea. Methods A Poisson generalized linear regression model combined with a distributed lag non-linear model was used to examine the relationship between diurnal temperature range and emergency department admissions for diarrhea among children under five years in Brisbane, from 1st January 2003 to 31st December 2009. Results There was a statistically significant relationship between diurnal temperature range and childhood diarrhea. The effect of diurnal temperature range on childhood diarrhea was the greatest at one day lag, with a 3% (95% confidence interval: 2%–5%) increase of emergency department admissions per 1°C increment of diurnal temperature range. Conclusion Within-day variation of temperature appeared to be a risk factor for childhood diarrhea. The incidence of childhood diarrhea may increase if climate variability increases as predicted.

Lifestyle risk factor modification in midlife women with type 2 diabetes : theoretical modelling of perceived barriers

Objective: The aim of this paper is to propose a ‘Perceived barriers and lifestyle risk factor modification model’ that could be incorporated into existing frameworks for diabetes education to enhance lifestyle risk factor education in women. Setting: Diabetes education, community health. Primary argument: ‘Perceived barriers’ is a health promotion concept that has been found to be a significant predictor of health promotion behaviour. There is evidence that women face a range of perceived barriers that prevent them from engaging in healthy lifestyle activities. Despite this, current evidence based models of diabetes education do not explicitly incorporate the concept of perceived barriers. A model of risk factor reduction that incorporates ‘perceived barriers’ is proposed. Conclusion: Although further research is required, current approaches to risk factor reduction in type 2 diabetes could be enhanced by identification and goal setting to reduce an individual’s perceived barriers.