A case-based reasoning approach to construction safety risk assessment

Ph.DDOCTOR OF PHILOSOPH

Sustainability analysis of reused industrial buildings in China: an assessment method

The sustainable development of old industrial buildings is in line with the national construction strategy and has an important impact on current urban renewal. Only by achieving a unified balance among economic, social, and environmental factors can reused industrial buildings be considered sustainable. However, there are no relevant sustainability assessment indicators and methods for reused industrial buildings in China. The purpose of this study was to provide a reasonable and effective method for assessing the sustainability of reused industrial buildings. First, this study analysed the factors influencing reused industrial building sustainability through a project investigation. Second, based on the assessment indicator setting procedure, the sustainability assessment indicator system for reused industrial buildings was optimised. Moreover, a multi-level sustainability assessment model based on extenics was established to identify the correlation functions of indicators with different attributes. Finally, a case was considered to verify this assessment method. The results showed that this assessment method in good agreement with the actual state of the case was validated to be more effective and practical. The assessment method could provide a basis for decision-making to improve sustainability and could be adopted by relevant rating agencies to determine the sustainability level of reused industrial buildings

Predicting trucking accidents with truck drivers 'safety climate perception across companies: A transfer learning approach

There is a rising interest in using artificial intelligence (AI)-powered safety analytics to predict accidents in the trucking industry. Companies may face the practical challenge, however, of not having enough data to develop good safety analytics models. Although pretrained models may offer a solution for such companies, existing safety research using transfer learning has mostly focused on computer vision and natural language processing, rather than accident analytics. To fill the above gap, we propose a pretrain-then-fine-tune transfer learning approach to help any company leverage other companies' data to develop AI models for a more accurate prediction of accident risk. We also develop SafeNet, a deep neural network algorithm for classification tasks suitable for accident prediction. Using the safety climate survey data from seven trucking companies with different data sizes, we show that our proposed approach results in better model performance compared to training the model from scratch using only the target company's data. We also show that for the transfer learning model to be effective, the pretrained model should be developed with larger datasets from diverse sources. The trucking industry may, thus, consider pooling safety analytics data from a wide range of companies to develop pretrained models and share them within the industry for better knowledge and resource transfer. The above contributions point to the promise of advanced safety analytics to make the industry safer and more sustainable.Comment: submitted to journal: accident analysis and preventio

An interpretable clustering approach to safety climate analysis: examining driver group distinction in safety climate perceptions

The transportation industry, particularly the trucking sector, is prone to workplace accidents and fatalities. Accidents involving large trucks accounted for a considerable percentage of overall traffic fatalities. Recognizing the crucial role of safety climate in accident prevention, researchers have sought to understand its factors and measure its impact within organizations. While existing data-driven safety climate studies have made remarkable progress, clustering employees based on their safety climate perception is innovative and has not been extensively utilized in research. Identifying clusters of drivers based on their safety climate perception allows the organization to profile its workforce and devise more impactful interventions. The lack of utilizing the clustering approach could be due to difficulties interpreting or explaining the factors influencing employees' cluster membership. Moreover, existing safety-related studies did not compare multiple clustering algorithms, resulting in potential bias. To address these issues, this study introduces an interpretable clustering approach for safety climate analysis. This study compares 5 algorithms for clustering truck drivers based on their safety climate perceptions. It proposes a novel method for quantitatively evaluating partial dependence plots (QPDP). To better interpret the clustering results, this study introduces different interpretable machine learning measures (SHAP, PFI, and QPDP). Drawing on data collected from more than 7,000 American truck drivers, this study significantly contributes to the scientific literature. It highlights the critical role of supervisory care promotion in distinguishing various driver groups. The Python code is available at https://github.com/NUS-DBE/truck-driver-safety-climate.Comment: Submitted to Journal:Accident Analysis and Preventio

Empirical investigation of the average deployment force of personal fall-arrest energy absorbers

The personal energy absorber (PEA) is a critical component of a personal fall arrest system (PFAS), and it is meant to dissipate the energy generated during a fall to prevent injuries to the user. When designing PFASs, engineers need to estimate the fall distance of the user, and one of the parameters needed is the average deployment force (Fa) of a PEA. However, currently there is a lack of empirical information on Fa. The guidance for the estimation of Fa stipulated in the North American standards Z259.16 and Z359.6 did not provide supporting empirical data and appeared to be focused on lower-capacity PEAs (class E4 or Type 1) that are not common in regions like Australia, New Zealand, Europe, and Singapore. Thus, this study aims to provide empirical data for the estimation of Fa of higher-capacity PEAs (class E6 or Type 2) represented by AS/NZS 1891.1:2007 PEAs. Thirty-one force-time charts of drop tests conducted on AS/NZS 1891.1-certified PEAs were evaluated, and it was found that the Fa ranged from 3.2 to 4.7 kN with a mean of 3.9 kN. In contrast to the guidance in Z259.16 and Z359.6, the data does not support estimating Fa based on 80% of maximum arrest force. The study also provided empirical basis for approximating Fa using energy-balance calculation. This paper recommends that in the absence of manufacturer's information on Fa and other test data, a reasonable estimate of Fa of PEA certified to AS/NZS 1891.1:2007 is 3.4 kN (10th percentile) and a conservative estimate is 3.2 kN (minimum). In the absence of publicly available empirical data of PEA certified to other standards, the results in this paper can provide useful guidance for estimation of Fa certified to other similar standards

Empirical models for estimating maximum allowable mass for personal fall arrest energy absorbers

© CCH. Two criteria for determining the capacity of personal fall arrest energy absorbers are maximum extension and maximum arrest force. There are concerns that despite the increasing weight of workers, most energy absorbers of personal fall arrest systems are only tested to 100 kg. In a previous study, a series of dynamic drop tests based on the Australian and New Zealand fall protection equipment standard, AS/NZS 1891.1:2007, were conducted on seven types of energy absorbers (total of 31 samples). Based on the data from the experiments, empirical models for the extension and maximum arrest force are presented in this paper. Using these models, the maximum allowable mass can be calculated

Utilising the Modified Loss Causation Model for the Codification and Analysis of Accident Data

10.4324/9780203493960Construction Safety Management Systems403-42

Investigating the adequacy of horizontal lifeline system design through case studies from Singapore

Horizontal lifeline system (HLLS) is one of the most widely used fall arrest systems for mitigating the risk of work-at-height in the construction and building industry. A HLLS must ensure the maximum arrest load created during a fall arrest does not exceed the capacity of the anchors and other system components. In addition, the maximum arrest force experienced by the user of the HLLS has to be kept below 6 kN (Europe, Australia, and Singapore) or 8 kN (North America). Finally, the height clearance must be sufficient to prevent the user from hitting the ground or obstacles. To achieve these safety criteria, a HLLS should be designed using appropriate calculation methods. This study evaluated 11 HLLS designs in Singapore based on common standards. A comprehensive calculation template was developed based on energy balance method to facilitate the evaluation. It was discovered that all 11 designs were not adequately endorsed or calculated. This is a potentially dangerous situation because HLLSs are usually the last line of defense for falls from height. The study demonstrated the current competency gap in design of HLLSs in Singapore. Engineers in Singapore are not adequately exposed to the concept of fall protection engineering. Thus, many of them tend to underestimate the forces involved in a fall arrest and did not spend sufficient effort to design HLLSs. It is believed that the problem is not unique to Singapore. Four measures were proposed to close this competency gap and promote sharing of best practices internationally. First, specialized training must be provided to engineers designing HLLSs. Second, it was proposed that design guides should be more detailed so as to help engineers understand what is required in a proper design of HLLSs. Third, a case-based reasoning system (CBRS) was proposed to help engineers improve their HLLS design and at the same time accumulate an international repository of HLLS designs. The CBRS will also contain a HLLS design template to facilitate designs of HLLS and other active fall protection systems. Last, it was proposed that manufacturers should make HLLS properties and detailed information on fall arrest equipment more readily available to engineers

Cognitive factors influencing safety behavior at height: A multimethod exploratory study

Despite efforts in recent years, the construction industry remains one of the top contributors for workplace fatalities in many countries. One of the key concerns in the industry is the management of workers' safety behavior. This paper aims to explore the cognitive factors influencing the unsafe behavior of not anchoring a safety harness when working at height. In addition, multiple stepwise linear regression, artificial neural network, and decision tree techniques were applied in the study to assess their usefulness in evaluating survey data of safety cognitive factors. The theory of planned behavior (TPB) was adopted to model the cognitive factors influencing the unsafe behavior of scaffolders. The TPB postulates that attitude, perceived behavioral control, and subjective norms affect the intention of workers, which ultimately affects intentional behavior. The unsafe act of not anchoring harnesses while working on a scaffold was selected as the focal behavior based on observations and interviews with safety supervisors. Supervisors also provided their opinions on the underlying reasons for the unsafe act. A questionnaire was then developed based on the site observations, interviews, and literature review. Subsequently, 40 migrant workers from Bangladesh, India, and China were surveyed. Stepwise multiple linear regression, neural network, and decision tree analyses were implemented. The analyses revealed that subjective norm was the key variable influencing a worker's decision to anchor the safety harness. The significance of subjective norm was probably affected by the national culture of the migrant workers. In addition, the analyses showed that the relationships between the variables were probably nonlinear, thus neural network and decision tree are suitable techniques. The exploratory study provides the basis for design of an in-depth study on the cognitive factors influencing safety behavior and it expands the choice of analyses techniques