The construction industry has always been infamous due to its staggering numbers of Occupational Health and Safety (OHS)-related injuries, resulting from overlooking all the crucial aspects ...endangering the involved workers’ lives. Considering this, there has been dearth of a study including all the essential Risk Parameters (RPs) for comprehensively assessing the OHS in the construction industry. Theretofore, a Holistic Occupational Health and Safety Risk Assessment Model (HOHSRAM) is developed in the current study to assess the safety and health of the Construction Workers (CWs’). The developed model is based on the integration of logarithmic fuzzy ANP, interval-valued Pythagorean fuzzy TOPSIS, and grey relational analysis. Based on the application of the developed HOHSRAM to a case of sustainable construction project, the following contributions have been noted; (1) calculating weights related to the safety decision makers having different backgrounds involved in the study using logarithmic-fuzzy-based constrained optimization algorithm, (2) involving the individual biases of the decision makers in the assessment stage, (3) determining all the essential RPs to comprehensively assess the OHS within the construction projects in a systematic way, (4) obtaining the final rankings of the identified safety risks under an interval-valued-Pythagorean fuzzy environment coupled with grey relational analysis. Additionally, it is discerned that the proposed model in this research outperforms the existing assessment methods used in the construction industry, through conducting a comprehensive comparative analysis. The developed HOHSRAM is verified to be beneficial for safety professionals by providing them with an inclusive ranking system, improving the well-being of the involved CWs.
This paper presents a comprehensive review of the significant studies exploited Artificial Neural Networks (ANNs) in BEA (Building Energy Analysis). To achieve a full coverage of the relevant studies ...to the scope of the research, a three-decade time span of the publishing date of the existing studies was taken into account. The review focuses on the studies utilized ANN to analyze the energy-related issues associated with buildings in major areas, including modeling of water heating and cooling systems, heating and cooling loads prediction, modeling heating ventilation air conditioning systems, indoor air temperature prediction, and building energy consumption prediction. Moreover, the findings of the abundant reviewed studies along with the potential future research to be carried out are discussed elaborately. Regarding the comprehensive review conducted, it is found out that the majority of studies focused on building energy consumption and indoor air temperature prediction. Additionally, it is observed that there has been a growing interest in the application of newly-developed ANNs to BEA areas, such as general regression neural network and recurrent neural network, due to their abilities in improving the modeling and prediction of buildings energy analysis. It is believed that this thorough review paper is useful for the researchers and scientific engineers working on the application of AI-based techniques to the building-energy-related areas to find out the relevant references and current state of the field.
•A comprehensive hybrid fuzzy-based occupational risk assessment model for the construction workers is developed.•All the crucial stages of a complete assessment approach are prudently taken into ...consideration.•A comprehensive step-by-step risk identification approach is proposed.•The proposed method for analyzing the identified risks is proved to produce very consistent and accurate results.•The adoption of suitable treatments for the pertinent risks is viable.•The robustness of the proposed assessment model is verified through its application to a real-life case study.
Although some studies have focused on assessing the related risks to the workers involved in the construction activities, the developed models hitherto are tangled with some shortfalls, including unstructured ways of risks identification, low consistency and accuracy of the used analysis methods, and paucity of prudent evaluation strategies. These shortcomings lead to taking imprudent and inappropriate mitigative actions. As such, a Comprehensive Hybrid Fuzzy-based Occupational Risk Assessment Model (CHFORAM) is developed in this paper to systematically identify, analyze, and evaluate the risks to which the construction workers are exposed. The application of the developed CHFORAM to a real-life case study was observed to have the following contributions: obtaining a detailed list of critical risks posing danger to the workers in a stepwise manner, obviating the need for having statistical data associated with incompletion and uncertainty, analyzing the identified risks with improved consistency and accuracy, proposing the effective treatment strategies to deal with the risks, and providing future plans for dealing with the risks at post-treatment stage. The application of CHFORAM to any projects can guide the safety inspectors and managers in taking the further jauntier mitigative actions, leading to the improvement in safety and health of the involved workers.
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.
Green buildings (GBs) have been adopted mainly to minimize the negative effects of the design, construction, and building operations on the environment. However, the GB-related activities have been ...found to be jeopardizing the occupational health and safety (OHS) of related projects, thereby debilitating the safety and health of respective crew members. Despite such vital issues, no study has been conducted yet to investigate the safety issues associated with GB construction projects in developing countries, where the inclination towards the adoption of GB is on the rise. Using this as a point of departure, the present study assesses the safety risks caused by GB projects with the use of a fuzzy-based RAM, through the lenses of the experts in Kazakhstan. The proposed RAM integrates Fuzzy Delphi Method (FDM) and Fuzzy Best Worst Method (FBWM). The FDM results clearly indicated that sustainable buildings continue to endanger the safety and health of respective workers, while fall from height and overexertion are found to be the leading causes of GB-associated risks using the FBWM. Despite the research limitations, this study prudently assessed the OHS-related risks to the LEED-based (the most widely used certification in the country) projects, and offered a fertile ground for future research to be conducted in developing economy settings. The findings indicated that the construction key players need to pay more attention to the riskiest GB-related hazards by investing their efforts in making the built environment truly sustainable in a not-too-distant future, which can improve the well-being of workers involved.
•The critical safety risks threatening the workers involved in green building construction projects are identified.•The critical safety risks in relation to GB projects are prioritized using fuzzy-based-constrained optimization model.•The prioritized safety risks are effectively evaluated using the concept of risk matrix.•The results produced from the study are validated through focus group discussion approach.
Risk decision matrix has widely been favoured by the researchers in the area of construction safety risk assessment. Although it provides the
construction safety professionals with the final ...illustration of the risks magnitude, it suffers from major shortcomings, including inability to considering the importance
of probability and severity, impaired analysis resulting from the use of raw numbers for ratings, and the limited range of classifications for assessing the risks. All
these shortages give an impaired insight to the concerned parties, deteriorating the involved workers’ safety. As such, this paper aims to develop a novel Risk Assessment
Model (RAM) through the integration of the Fuzzy Best Worst Method (FBWM) with the Interval-Valued Fuzzy Technique for Order of Preference by Similarity to Ideal Solution
(IVFTOPSIS). Based on the application of RAM to a real-life case study, it was observed that the developed RAM contributes to the body of construction safety risk assessment
in five unique ways: (1) computing the importance of the two risk parameters (i.e. probability and severity) using fuzzy-reference-based comparisons, (2) obviating the needs
for having statistical data, (3) prioritizing the identified risks using the combination of interval-valued triangular fuzzy numbers with TOPSIS, (4) providing the safety
analysts with wider ranges of classifications for conducting risk assessment, and (5) providing the safety professionals with appropriate evaluation strategies for controlling
the analysed risks. The developed model in the study can be applied to any projects, giving a conclusive plan to the concerned safety professionals for adopting the further
prudent mitigation measurements.
Although quite a few studies have focused on Occupational Health and Safety (OHS)-related issues within the context of sustainable building construction projects, there has been dearth of a study ...developing a management framework to comprehensively identify, analyze, evaluate, and control the safety risks threatening the involved Green Building Construction Workers (GBCWs). The lack of such consideration leads not only to incurring additional costs for the stakeholders, but also overshadowing the impetus towards the adoption of sustainable developments within the construction industry. As such, using Hong Kong as a case study, a Holistic Z-numbers-based Risk Management Framework (HZRMF) is developed in the current study. The main contributions of the current research to the area of OHS linked to green building construction projects are as follows: (1) identifying all the critical safety risks associated with the relative green-oriented requirements through the proposed integration of Z-numbers with the Delphi technique, (2) calculating the final magnitudes of green-associated safety risks through a novel hybrid Z-numbers-based algorithm by considering the importance weights of risk parameters, (3) evaluating the analyzed safety risks using the proposed five-level strategy, (4) pinpointing the green-oriented requirements that are of high-criticality, and (5) providing a comprehensive list of treatment measures to control the evaluated green-associated safety risks. Ultimately, it was observed that the three most critical safety risks were associated with fall hazards. It is proven that the results and analyses produced in the study using the developed HZRMS could make massive inroads into improving the OHS of the involved GBCWs.
•All the potential safety risks posing danger to the green building construction workers are identified.•The critical safety risks are determined using the integrated Z-numbers-based Delphi technique.•The identified critical safety risks are analyzed using a hybrid Z-numbers-based Multi-Criteria Decision-Making method.•The analyzed safety risks are evaluated through the proposed five-level strategy.•A comprehensive list of practicable treatment measures for controlling the critical safety risks are obtained.
In recent years, many researchers across the world have addressed the implementation of Building Information Modelling (BIM) in the energy assessment of the built environment. However, several ...potential issues still need to be resolved in order to utilise the benefits provided by BIM to a maximum degree. To fill this gap, a systematic literature review is conducted in this study to critically investigate the utilisation of BIM tools in energy assessment. To achieve the above-mentioned objective, after shortlisting the relevant papers published hitherto, using keyword searching, a systematic review was undertaken, including the application of BIM in the contexts of different countries, types of BIM tools, BIM and Life Cycle Assessment (LCA) integration, energy affiliations, stakeholders’ involvement and their roles, uncertainty, and sensitivity analysis. The outcomes show the most widely used and effective BIM tools in different types of construction projects in various countries. The review of the literature clearly shows that BIM tools can effectively be used in the assessment of energy performance of buildings. The article gives insight to engineers, architecture, and decision makers to carefully select appropriate BIM tools in terms of energy assessment.
Building Information Modelling (BIM) has not been sufficiently proliferated in the developing construction communities. This is owing to the lack of incorporating the key success factors (KSFs) of ...BIM implementation in a phase-based roadmap to support implementing BIM in practice on a step-by-step approach. With this in mind, this work aims at (1) defining the KSFs for implementing BIM within the developing economies’ socio-economic environment, (2) investigating the interrelationships among the KSFs, and (3) establishing the KSFs in a phased approach to devise a roadmap for their implementation on a step-by-step basis. First, 18 KSFs for implementing BIM have been specified by systematically investigating the pertinent literature and interviewing six well-qualified practitioners in BIM from Egypt, as a developing country. Second, from ten Egyptian BIM experts, data on the influences of the KSFs on each other have been gathered, employing a matrix format-based questionnaire. Third, the experts’ evaluations have been processed, utilizing the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique. Proficiently, DEMATEL through its causal diagram portrayed the cause-and-effect relations map of the KSFs. Besides, it divided the KSFs into four clusters, each of which signifies a phase in the BIM implementation journey along with its corresponding priority as well as the priorities of the KSFs that it encompasses. The causal diagram indicated that phase one related KSFs of the BIM implementation journey: research and development investments, senior management support, and firm’s fiscal support contribute to the whole success of the developed BIM implementation roadmap. This study equips construction practitioners in the developing economies with a four-phased roadmap for applying the KSFs of BIM implementation journey in practice on a step-by-step basis. This contribution helps in better prioritizing their decisions and optimizing the allocation of their resources when applying BIM in their business. Hence, at a fast pace, BIM can be proliferated in those countries.
Due to the unique atmospheric conditions on Mars, the management of essential information in Mars buildings is of great importance. Even the smallest error or manipulation of data can create ...irreparable risks for residents. Martian buildings require a strong security shield to ensure accurate and unaltered data processing. In this article, the factors affecting the buildings of Mars and the lives of the inhabitants of Mars were identified and analyzed, and seven key factors were identified. These factors were then integrated into Mars building information systems using blockchain technology, defining four distinct alert levels for specific building conditions. This research is based on the simulation of Martian buildings, and there has been no laboratory case to test the proposed method until now. The findings showed that the proposed framework for Martian buildings was better than similar studies based on Earth, and there was no similar case to compare the results in Martian buildings. The ground-breaking integration of blockchain and building information modeling (BIM) on Mars opens up new opportunities for extraterrestrial building control methods and marks the beginning of the evolution of this field, but given that there is still no construction in the field of buildings on Mars, organizations and bodies that work in this field can use the results of this research to check the compatibility of the proposed method with Martian buildings.
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