A Fuzzy Risk Assessment Model for Small Scale Construction Work

Abstract

Occupational risk assessment is important for providing employees with a safe and healthy work environment. When compared with other industries, the construction industry poses a higher risk for accidents due to the variety and complexity of skills required for different types of work in the sector. Small-sized construction sites have a higher risk of occupational injury. Countries without occupational safety and health (OSH) enforcement pose an additional risk increasing the need of an effective and easy-to-apply risk assessment approach. This research aims to develop and study an easy to apply risk assessment model for small-scale construction sites. The method includes opinions from experts on safety, checklists to estimate the possibility of occurrence of accidents, the identification of current site-specific safety levels, the severity of risk, and safety barriers. The model uses both historical data and fuzzy approaches to calculate risk level and was applied to four different construction sites in North Cyprus. Results reveal the risk level for each accident type and the aggregate safety level of the construction sites. Falling from height was identified as the most common accident type with the highest risk level. This study contributes to the development of sustainable OSH management systems for construction companies by highlighting the measures that must be taken to reduce occupational accidents.

A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management

“…In risk management field 25 out of 61 papers dealt with data on the "risk management process". Into this class the 54 observations out of 202 concerned the following topics: awkward working postures [7,19]; compliance with Health and Safety standards [2,4,47]; risk assessment [21,51,53,63,66,77,79]; safe climate [44]; slope instability [10,17]; teaching-training tasks [5,6,78]; unsafe behaviours [25,72]; worker fatigue-heat stress [38,69,73]; site image [1,46]. Antwi-Afari et al [7] used deep learning networks to automatically extract relevant features with spatial-temporal dependence acquired by a wearable insole pressure system.…”

“…(32), knn (49), svm (81) and rf (69), to the right of the graph characterised by a strongly positive coordinate on the axis, to individuals such as MCDA C (58), characterised by a strongly negative coordinate on the axis (to the left of the graph). Dimension 2 opposes individuals such as lstm (54), word2vec (88), nlp (63) and BIM ( 16), who at the top of the graph, and characterised by a low positive co-ordinate on the axis, with individuals such as ann (8), adaboost (3), who have low negative coordinate on the axis and are located at the bottom of the graph. The Dim1, group 1 (dt , knn, svm and rf) is sharing high values for the variables "predicting", "supervised", "monitoring", "frequency", "institutional data", "data project-simulation-signal", "classifying", "best method and "interview-literature-text" (variables are sorted from the strongest).…”

A Conceptual Architecture of Fuzzy Risk-Based Decision Support Systems (R+DSS)

“…The study employed a hybrid methodology that integrates historical data and fuzzy logic techniques to assess the level of risk. The model was implemented in four different construction sites located in North Cyprus [14]. The conceptualisation of expanding the classical k-means clustering methodology to incorporate membership degrees instead of final assignments of data objects to clusters has resulted in the development of numerous novel fuzzy clustering algorithms [15].…”

Predictive worker safety assessment through on-site correspondence using multi-layer fuzzy logic in outdoor construction environments

“…Fuzzy logic has also been used in many fields such as constructions [40] , decision-making models [41] , and project management [42] , where it is used to model uncertainty and evaluate performance. To provide a safer and healthier work environment for construction workers, the author in [43] proposed a fuzzy risk assessment model to evaluate the risks connected with various construction tasks in the small-scale construction sector. Also, in the study [44] , fuzzy logic is utilized as an analytical and scientific method to identify and mitigate risks in construction projects.…”