scholarly journals Developing an Ensemble Predictive Safety Risk Assessment Model: Case of Malaysian Construction Projects

2020 ◽  
Vol 17 (22) ◽  
pp. 8395 ◽  
Author(s):  
Haleh Sadeghi ◽  
Saeed Reza Mohandes ◽  
M. Reza Hosseini ◽  
Saeed Banihashemi ◽  
Amir Mahdiyar ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Developing Countries ◽  
Construction Projects ◽  
Well Being ◽  
Working Environment ◽  
Assessment Model ◽  
Machine Learning Techniques ◽  
Risk Assessment Model ◽  
Safety Risk ◽  
Safety Risks

Occupational Health and Safety (OHS)-related injuries are vexing problems for construction projects in developing countries, mostly due to poor managerial-, governmental-, and technical safety-related issues. Though some studies have been conducted on OHS-associated issues in developing countries, research on this topic remains scarce. A review of the literature shows that presenting a predictive assessment framework through machine learning techniques can add much to the field. As for Malaysia, despite the ongoing growth of the construction sector, there has not been any study focused on OHS assessment of workers involved in construction activities. To fill these gaps, an Ensemble Predictive Safety Risk Assessment Model (EPSRAM) is developed in this paper as an effective tool to assess the OHS risks related to workers on construction sites. The developed EPSRAM is based on the integration of neural networks with fuzzy inference systems. To show the effectiveness of the EPSRAM developed, it is applied to several Malaysian construction case projects. This paper contributes to the field in several ways, through: (1) identifying major potential safety risks, (2) determining crucial factors that affect the safety assessment for construction workers, (3) predicting the magnitude of identified safety risks accurately, and (4) predicting the evaluation strategies applicable to the identified risks. It is demonstrated how EPSRAM can provide safety professionals and inspectors concerned with well-being of workers with valuable information, leading to improving the working environment of construction crew members.

Developing a fuzzy risk assessment model for guaranteed maximum price and target cost contracts in South Australia

Facilities ◽  
2014 ◽  
Vol 32 (11/12) ◽  
pp. 624-646 ◽  
Author(s):  
Daniel W.M. Chan ◽  
Joseph H.L. Chan ◽  
Tony Ma
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Construction Projects ◽  
South Australia ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Content Type ◽  
Risk Levels ◽  
Target Cost ◽  
Fuzzy Risk Assessment

Purpose – This paper aims to develop a fuzzy risk assessment model for construction projects procured with target cost contracts and guaranteed maximum price contracts (TCC/GMP) using the fuzzy synthetic evaluation method, based on an empirical questionnaire survey with relevant industrial practitioners in South Australia. Design/methodology/approach – A total of 34 major risk factors inherent with TCC/GMP contracts were identified through an extensive literature review and a series of structured interviews. A questionnaire survey was then launched to solicit the opinions of industrial practitioners on risk assessment of such risk factors. Findings – The most important 14 key risk factors after the computation of normalised values were selected for undertaking fuzzy evaluation analysis. Five key risk groups (KRGs) were then generated in descending order of importance as: physical risks, lack of experience of contracting parties throughout TCC/GMP procurement process, design risks, contractual risks and delayed payment on contracts. These survey findings also revealed that physical risks may be the major hurdle to the success of TCC/GMP projects in South Australia. Practical implications – Although the fuzzy risk assessment model was developed for those new-build construction projects procured by TCC/GMP contracts in this paper, the same research methodology may be applied to other contracts within the wide spectrum of facilities management or building maintenance services under the target cost-based model. Therefore, the contribution from this paper could be extended to the discipline of facilities management as well. Originality/value – An overall risk index associated with TCC/GMP construction projects and the risk indices of individual KRGs can be generated from the model for reference. An objective and a holistic assessment can be achieved. The model has provided a solid platform to measure, evaluate and reduce the risk levels of TCC/GMP projects based on objective evidence instead of subjective judgements. The research methodology could be replicated in other countries or regions to produce similar models for international comparisons, and the assessment of risk levels for different types of TCC/GMP projects (including new-build or maintenance) worldwide.

Workplace Safety Risk Assessment Model Based on Fuzzy Regression

2018 ◽  
Vol 24 (3) ◽  
pp. 1656-1659 ◽  
Author(s):  
Nureize Arbaiy ◽  
Hamijah Ab Rahman ◽  
Mohd Zaki Mohd Salikon ◽  
Pei Chun Lin
Keyword(s):  
Risk Assessment ◽  
Workplace Safety ◽  
Assessment Model ◽  
Fuzzy Regression ◽  
Risk Assessment Model ◽  
Safety Risk ◽  
Model Based

Expert elicitation to estimate the feed safety impact of criteria included in the Canadian Food Inspection Agency risk assessment model for feed mills

2020 ◽  
Author(s):  
Virginie Lachapelle ◽  
Manon Racicot ◽  
Geneviève Comeau ◽  
Mohamed Rhouma ◽  
Alexandre Leroux ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Expert Elicitation ◽  
Assessment Model ◽  
Assessment Criteria ◽  
Risk Assessment Model ◽  
Safety Risk ◽  
Inspection Agency ◽  
Canadian Food Inspection Agency ◽  
Feed Safety ◽  
Feed Mill

The Canadian Food Inspection Agency (CFIA) is developing an Establishment-based Risk Assessment model for commercial and on-farm mills involved in the manufacture, storage, packaging, labelling or distribution of livestock feed (ERA-Feed Mill model). This model will help inform the allocation of inspection resources based on feed safety risk, including animal health and food safety risk. In a previous study, 34 risk factors, grouped into inherent, mitigation and compliance clusters, along with their assessment criteria were selected. The objective of this current study was to estimate the relative risk (RR) of the 203 assessment criteria based on their impact on feed safety to design an ERA-Feed Mill model algorithm. Furthermore, the intent of this study was to assess the maximum increase or decrease of risk obtained when multiple criteria belonging to a same cluster were identified in a specific feed mill. To do so, a two-round face-to-face expert elicitation was conducted with 28 Canadian feed experts. Results showed no significant association between respondent profiles (years of experience, work sector) and estimated RR. Uniformity of answers between experts improved between rounds. Criteria having the highest increase in risk (median RR≥4) included the presence of materials prohibited to be fed to ruminants in a facility that produces ruminant feed, the presence of multiple livestock species on site and historical non-compliances related to the inspection of the feed mill’s process control and end-product control programs. Risk mitigation criteria having the highest impact on decreasing the risk were the implementation of feed safety certifications, the use of dedicated manufacturing lines (prohibited materials, medications) and having a hazard sampling plan in place for finished feed. The median RR assigned to each criterion and cluster will be used to build an algorithm of the CFIA’s ERA-Feed Mill model.

scholarly journals DESIGN AND PILOT RUN OF FUZZY SYNTHETIC MODEL (FSM) FOR RISK EVALUATION IN CIVIL ENGINEERING

2013 ◽  
Vol 19 (2) ◽  
pp. 217-238 ◽  
Author(s):  
Hamzah Abdul-Rahman ◽  
Chen Wang ◽  
Yee Lin Lee
Keyword(s):  
Risk Assessment ◽  
Construction Projects ◽  
Optimal Allocation ◽  
Early Stage ◽  
Model Development ◽  
Assessment Tools ◽  
Assessment Model ◽  
Quality Data ◽  
Risk Assessment Model ◽  
Synthetic Model

Most of the current construction risk assessment tools deliver unsatisfactory results because the prerequisite for their effective applications rely on the availability of high quality data especially during the early stage of a project. Unfortunately, such data are limited, ambiguous or even not exist due to the great uncertainty inherent in construction projects. Based on Fuzzy Synthetic Analysis (FSA), a model development team was formed among construction engineers, IT professionals, and Mathematicians in developing a holistic risk assessment model to estimate the construction risks especially for the situations with incomplete data and vague environments. Through qualitative scales defined by triangular fuzzy numbers used in pairwise comparisons to capture the vagueness in the linguistic variables, a risk assessment model using Analytic Hierarchy Process (AHP) was developed. The Pilot Run revealed the developed Fuzzy Synthetic Model (FSM) could accelerate the decision-making process and provide optimal allocation of project resources to mitigate possible risks detrimental to the success of a project in terms of time, cost, and quality.

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