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

Towards safer tower crane operations: An innovative knowledge-based decision support system for automated safety risk assessment

INTRODUCTION

Tower cranes are commonly employed in construction projects, despite presenting significant hazards to the workforce involved.

METHOD

To address these safety concerns, a Knowledge-Based Decision-Support System for Safety Risk Assessment (KBDSS-SRA) has been developed. The system's capacity to thoroughly evaluate associated risks is illustrated through its utilization in various construction endeavors.

RESULTS

The system accomplishes the following goals: (1) compiles essential risk factors specific to tower crane operations, (2) identifies critical safety risks that jeopardize worker well-being, (3) examines and assesses the identified safety risks, and (4) automates the labor-intensive and error-prone processes of safety risk assessment. The KBDSS-SRA assists safety management personnel in formulating well-grounded decisions and implementing effective measures to enhance the safety of tower crane operations.

PRACTICAL APPLICATIONS

This is facilitated by an advanced computerized tool that underscores the paramount significance of safety risks and suggests strategies for their future mitigation.

A hybrid approach based on k-means and SVM algorithms in selection of appropriate risk assessment methods for sectors

Every work environment contains different types of risks and interactions between risks. Therefore, the method to be used when making a risk assessment is very important. When determining which risk assessment method (RAM) to use, there are many factors such as the types of risks in the work environment, the interactions of these risks with each other, and their distance from the employees. Although there are many RAMs available, there is no RAM that will suit all workplaces and which method to choose is the biggest question. There is no internationally accepted scale or trend on this subject. In the study, 26 sectors, 10 different RAMs and 10 criteria were determined. A hybrid approach has been designed to determine the most suitable RAMs for sectors by using k-means clustering and support vector machine (SVM) classification algorithms, which are machine learning (ML) algorithms. First, the data set was divided into subsets with the k-means algorithm. Then, the SVM algorithm was run on all subsets with different characteristics. Finally, the results of all subsets were combined to obtain the result of the entire dataset. Thus, instead of the threshold value determined for a single and large cluster affecting the entire cluster and being made mandatory for all of them, a flexible structure was created by determining separate threshold values for each sub-cluster according to their characteristics. In this way, machine support was provided by selecting the most suitable RAMs for the sectors and eliminating the administrative and software problems in the selection phase from the manpower. The first comparison result of the proposed method was found to be the hybrid method: 96.63%, k-means: 90.63 and SVM: 94.68%. In the second comparison made with five different ML algorithms, the results of the artificial neural networks (ANN): 87.44%, naive bayes (NB): 91.29%, decision trees (DT): 89.25%, random forest (RF): 81.23% and k-nearest neighbours (KNN): 85.43% were found.

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

Increasingly, information technology facilitates the storage and management of data useful for risk analysis and event prediction. Studies on data extraction related to occupational health and safety are increasingly available; however, due to its variability, the construction sector warrants special attention. This review is conducted under the research programs of the National Institute for Occupational Accident Insurance (Inail).

OBJECTIVES

The research question focuses on identifying which data mining (DM) methods, among supervised, unsupervised, and others, are most appropriate for certain investigation objectives, types, and sources of data, as defined by the authors.

METHODS

Scopus and ProQuest were the main sources from which we extracted studies in the field of construction, published between 2014 and 2023. The eligibility criteria applied in the selection of studies were based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). For exploratory purposes, we applied hierarchical clustering, while for in-depth analysis, we used principal component analysis (PCA) and meta-analysis.

RESULTS

The search strategy based on the PRISMA eligibility criteria provided us with 63 out of 2234 potential articles, 206 observations, 89 methodologies, 4 survey purposes, 3 data sources, 7 data types, and 3 resource types. Cluster analysis and PCA organized the information included in the paper dataset into two dimensions and labels: "supervised methods, institutional dataset, and predictive and classificatory purposes" (correlation 0.97-8.18 × 10-1; p-value 7.67 × 10-55-1.28 × 10-22) and the second, Dim2 "not-supervised methods; project, simulation, literature, text data; monitoring, decision-making processes; machinery and environment" (corr. 0.84-0.47; p-value 5.79 × 10-25--3.59 × 10-6). We answered the research question regarding which method, among supervised, unsupervised, or other, is most suitable for application to data in the construction industry.

CONCLUSIONS

The meta-analysis provided an overall estimate of the better effectiveness of supervised methods (Odds Ratio = 0.71, Confidence Interval 0.53-0.96) compared to not-supervised methods.

Safety risk assessment of sustainable construction based on projection pursuit model optimized by multi-intelligent algorithm: a case study of new chemical projects

With the rapid development of urban and social economies, the safety accidents in the construction process of the new chemical plant have caused huge losses to the city. The purpose of this study is to evaluate the risks in the construction process of chemical projects and propose preventive measures. A novel risk assessment model based on multi-intelligence algorithm optimization projection pursuit was developed to assess the construction safety risk and determine the risk level. In this model, the best-worst method and the entropy weight method were used as subjective and objective evaluation methods, respectively. The theory based on the idea of the distance function was applied to the model to calculate the combined weight value. The results showed that the three evaluation objects with the highest risk value were the air compression station plant, regional control room, and hazardous and solid waste temporary repository. The risk values of these three buildings were 2.2557, 2.2160, and 2.1654, respectively, and the corresponding risk level was high. On-site safety managers should take immediate measures in these high-risk buildings to reduce the possibility of accidents. This study is a new attempt to consider the construction safety risk of the new chemical project.

A new hybrid risk assessment method based on Fine-Kinney and ANFIS methods for evaluation spatial risks in nursing homes

Today, as the elderly population in the world increases, the increase in those living in nursing homes causes their problems to be even more important. Spatial hazards cause injury and death most of the time, therefore should be evaluated risks then corrective and preventive actions should be implemented. Fine-Kinney is one of the most widely used risk assessment methods, but it has some shortcomings. One of them is that risk factors such as probability, frequency, and severity are accepted as equally important, but they can have different importance weights in real-life applications. Another is that experts assess the risk magnitudes using their opinions, who usually tend to use linguistic expressions instead of crisp numbers, in incomplete information and uncertain situations. The last is that the experts' experiences are not effectively incorporated into the automation of the risk assessment. The adaptive neuro-fuzzy inference system (ANFIS) method, which is a machine learning method, can overcome all these shortcomings. In this study, a novel hybrid risk assessment method based on Fine-Kinney and ANFIS is developed to predict the class of a new occurring risk. The hybrid method was applied to nursing homes located in Turkey. The risk classes predicted with the hybrid method were compared to ones found in the traditional Fine-Kinney method. It was determined that the prediction accuracy and Fleiss kappa value of the new hybrid method were 95.745% and 0.929 respectively. Thus, the hybrid method can be used instead of the traditional Fine-Kinney method to determine the class of a new risk, because it does not require a large number of experts and provides a faster assessment.

A Comparative Performance Assessment of Optimized Multilevel Ensemble Learning Model with Existing Classifier Models

To predict the class level of any classification problem, predictive models are used and mostly a single predictive model is built to predict the class level of any classification problem; current research considers multiple predictive models to predict the class level. Ensemble modeling means instead of building a single predictive model, it is proposed to build a multilevel predictive model, which generalizes to predict all the class levels with an adequate percent of accuracy, that is, from 70% to 90% by applying and using a different combination of classification algorithms. In this article, a multilevel approach for selecting base classifiers for building an ensemble classification model is proposed. The rudimentary concept behind this approach is to drop lousy performing features and collinearity from the selected data set for ensemble modeling. For the evaluation of the proposed multilevel predictive model, different data sets from the University of California, Irvine, repository have been used and comparisons with the modern classifier's models have been conducted. The implementation analyses demonstrate the potency and excellence of the novel approach when compared with other modern classification models (three-layered artificial neural network, Radial Variant Function Neural Network/Fish Swarm Algorithm). The classification accuracy achieved with selected algorithms lies in the range of 70%-88.3%. Among all the selected classification algorithms, the lowest accuracy is achieved by the naive Bayes algorithm, which is close to 71.9%. However, the proposed algorithm (NB-RF-LR-SEMod), which is a combination of different classifiers, achieved a maximum accuracy of 88.3% on the Photographic and Imaging Manufacturers Association Diabetes data set, which is, by far, the best to any single classifier. Hence, this proposed work is helpful for any health care official to detect the diabetes problem at an early stage and prevent the affected person from future complications of it.

Research Paradigm of Network Approaches in Construction Safety and Occupational Health

Construction safety accidents seriously threaten the lives and health of employees; however, the complexity of construction safety problems continues to increase. Network approaches have been widely applied to address accident mechanics. This study aims to review related studies on construction safety and occupational health (CSOH) and summarize the research paradigm of recent decades. We solicited 119 peer-reviewed journal articles and performed a bibliometric analysis as the foundation of the future directions, application bottlenecks, and research paradigm. (1) Based on the keyword cluster, future directions are divided into four layers: key directions, core themes, key problems, and important methods. (2) The network approaches are not independently applied in the CSOH research. It needs to rely on different theories or be combined with other methods and models. However, in terms of approach applications, there are still some common limitations that restrict its application and development. (3) The research paradigm of network analysis process can be divided into four stages: description, explanation, prediction, and control. When the same network method encounters different research objects, it focuses on different analysis processes and plays different roles.

Factors Affecting Workplace Well-Being: Building Construction Projects

This study explores the factors affecting workplace well-being in building construction projects. The objectives of this study are (1) to investigate the critical factors for workplace well-being in building construction projects, (2) to compare the critical factors between large enterprises (LEs) and small-medium enterprises (SMEs), and (3) to compare the critical factors between high-rise building construction projects and non-high-rise building construction projects. Data from 21 semi-structured interviews with construction industry professionals in Malaysia and a systematic literature review were used to develop a potential list of factors. Then, the factors were used to create a survey that was distributed to industry professionals. Data from 205 valid responses were analyzed using mean score ranking, normalization, the Kruskal–Wallis test, and overlap analysis. Fourteen critical factors were determined, including salary package, working hours, project progress, planning of the project, workers’ welfare, relationship between top management and employees, timeline of salary payment, working environment, employee work monitoring, communication between workers, insurance for construction worker, general safety and health monitoring, collaboration between top management and employee, and project leadership. This study contributes to the body of knowledge by identifying the critical factors for improving workplace well-being. The study findings allow researchers and practitioners to develop strategies to promote workplace well-being in building construction projects.

Advancements in Artificial Intelligence-Based Decision Support Systems for Improving Construction Project Sustainability: A Systematic Literature Review

This paper aims at evaluating the current state of research into artificial intelligence (AI)-based decision support systems (DSS) for improving construction project sustainability. The literature was systematically reviewed to explore the use of AI in the construction project lifecycle together with the consideration of the economic, environmental, and social goals of sustainability. A total of 2688 research papers were reviewed, and 77 papers were further analyzed, and the major tasks of the DSSs were categorized. Our review results suggest that the main research stream is dedicated to early-stage project prediction (50% of all papers), with artificial neural networks (ANNs) and fuzzy logic (FL) being the most popular AI algorithms in use. Hybrid AI models were used in 46% of all studies. The goal for economic sustainability is the most considered in research, with 87% of all papers considering this goal, and there is evidence given of a trend towards the environmental and social goals of sustainability receiving increasing attention throughout the latter half of the decade.

Comparative Analysis of Degree of Risk between the Frequency Aspect and Probability Aspect Using Integrated Uncertainty Method Considering Work Type and Accident Type in Construction Industry

Fatal incidents in the construction business are higher than in other industries. Previous studies concentrated on the frequency of fatal incidents based on safety management, however, the probability of fatal incidents might be more important than the frequency of fatal incidents. For instance, certain work types have low fatal incident cases but show a high probability of fatal incidents, which means they are riskier than others. The purpose of this study is to analyze the level of risk by comparing the frequency of fatal incidents and probability of fatal incidents for 27 types of work and 18 types of accidents using an uncertainty analysis. This study is carried out in five stages from the collection of data to conducting the statistical analysis. The result of the research shows the estimated rank of frequency and probability for work and accident type, respectively. For instance, ‘reinforced concrete construction work’ (66.5 fatal incidents) showed the highest frequency work type, and ‘scaffold and demolition work’ (28.65‱) showed the highest fatality rate. This research addressed the uncertainty problem using an integrated time series and estimation method to compare the degree of risk from the viewpoint of frequency and probability aspects in the construction business.

Investigating the Barriers to Applying the Internet-of-Things-Based Technologies to Construction Site Safety Management

The utilization of Internet-of-Things (IoT)-based technologies in the construction industry has recently grabbed the attention of numerous researchers and practitioners. Despite the improvements made to automate this industry using IoT-based technologies, there are several barriers to the further utilization of these leading-edge technologies. A review of the literature revealed that it lacks research focusing on the obstacles to the application of these technologies in Construction Site Safety Management (CSSM). Accordingly, the aim of this research was to identify and analyze the barriers impeding the use of such technologies in the CSSM context. To this end, initially, the extant literature was reviewed extensively and nine experts were interviewed, which led to the identification of 18 barriers. Then, the fuzzy Delphi method (FDM) was used to calculate the importance weights of the identified barriers and prioritize them through the lenses of competent experts in Hong Kong. Following this, the findings were validated using semi-structured interviews. The findings showed that the barriers related to “productivity reduction due to wearable sensors”, “the need for technical training”, and “the need for continuous monitoring” were the most significant, while “limitations on hardware and software and lack of standardization in efforts,” “the need for proper light for smooth functionality”, and “safety hazards” were the least important barriers. The obtained findings not only give new insight to academics, but also provide practical guidelines for the stakeholders at the forefront by enabling them to focus on the key barriers to the implementation of IoT-based technologies in CSSM.

The role of machine learning in the primary prevention of work-related musculoskeletal disorders: A scoping review

To determine the applications of machine learning (ML) techniques used for the primary prevention of work-related musculoskeletal disorders (WMSDs), a scoping review was conducted using seven literature databases. Of the 4,639 initial results, 130 primary research studies were deemed relevant for inclusion. Studies were reviewed and classified as a contribution to one of six steps within the primary WMSD prevention research framework by van der Beek et al. (2017). ML techniques provided the greatest contributions to the development of interventions (48 studies), followed by risk factor identification (33 studies), underlying mechanisms (29 studies), incidence of WMSDs (14 studies), evaluation of interventions (6 studies), and implementation of effective interventions (0 studies). Nearly a quarter (23.8%) of all included studies were published in 2020. These findings provide insight into the breadth of ML techniques used for primary WMSD prevention and can help identify areas for future research and development.

Predicting Ability of Dynamic Balance in Construction Workers Based on Demographic Information and Anthropometric Dimensions

Background

Difficulties in walking and balance are risk factors for falling. This study aimed to predict dynamic balance based on demographic information and anthropometric dimensions in construction workers.

Methods

This descriptive-analytical study was conducted on 114 construction workers in 2020. First, the construction workers were asked to complete the demographic questionnaire determined in order to be included in the study. Then anthropometric dimensions were measured. The dynamic balance of participants was also assessed using the Y Balance test kit. Dynamic balance prediction was performed based on demographic information and anthropometric dimensions using multiple linear regression with SPSS software version 25.

Results

The highest average normalized reach distances of YBT were in the anterior direction and were 92.23 ± 12.43% and 92.28 ± 9.26% for right and left foot, respectively. Both maximal and average normalized composite reach in the YBT in each leg were negatively correlated with leg length and navicular drop and positively correlated with the ratio of sitting height to leg length. In addition, multiple linear regressions showed that age, navicular drop, leg length, and foot surface could predict 23% of the variance in YBT average normalized composite reach of the right leg, and age, navicular drop, and leg length could predict 21% of that in the left leg among construction workers.

Conclusion

Approximately one-fifth of the variability in the normalized composite reach of dynamic balance reach among construction workers using method YBT can be predicted by variables age, navicular drop, leg length, and foot surface.

Safety Program Elements in the Construction Industry: The Case of Iraq

The construction industries' unsafe conditions require increased efforts to improve safety performance to prevent and reduce accident rates. Safety performance in the Iraqi construction industry is notoriously poor. Despite this condition, safety research has so far been neglected. Implementing a safety program is a proven initial step to improve safety. Therefore, the aim of this study is to identify the key elements of a safety program in the Iraqi construction industry. To verify and validate a list of safety program elements identified in the literature review, a mixed method approach was used by using interviews and questionnaire surveys. A final list of 25 elements were then analyzed using exploratory factor analysis. The analysis found that these elements can be grouped into four interrelated dimensions: management commitment and employee involvement, worksite analysis, hazard prevention and control systems, and safety and health training. This study contributes to the body of knowledge on safety in the Iraqi construction sector, a research area which has not been adequately investigated previously. They also help decision-makers focus on key elements that are needed to start improving safety performance in this context.