ORCID Profile
0000-0002-5825-5829
Current Organisations
University of Manchester
,
Hong Kong University of Science and Technology
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Publisher: Emerald
Date: 07-03-2022
DOI: 10.1108/ECAM-09-2021-0817
Abstract: In the study, a five-dimensional-safety risk assessment model (5D-SRAM) is developed to improve the construction safety risk assessment approaches available in the literature. To that purpose, a hybrid multi-dimensional fuzzy-based model is proposed, which provides a comprehensive ranking system for the safety risks existing in a project by considering the contextualization of the construction-related activities resulting in an accident. The developed 5D-SRAM is based on an amalgamation of different fuzzy-based techniques. Through the proposed fuzzy analytic hierarchy process (AHP) method, the importance weights of essential risk dimensions playing role in defining the magnitude of the construction-related risks are obtained, while a precise prioritized ranking system for the identified safety risks is acquired using the proposed fuzzy technique of order preference similarity to the ideal solution (FTOPSIS). Through the application of the proposed 5D-SRAM to a real-life case study – which is the case of green building construction projects located in Hong Kong – contributions are realized as follows: (1) determination of a more complete range of risk dimensions, (2) calculation of importance weightings for each risk dimension and (3) obtainment of a precise and inclusive ranking system for safety risks. Additionally, the supremacy of the developed 5D-SRAM against the other safety assessment approaches that are commonly adopted in the construction industry is proved. The developed 5D-SRAM provides the concerned safety decision-makers with not only all the crucial dimensions that play roles toward the magnitude of safety risks posing threats to the workers involved in construction activities, but also they are given hindsight regarding the importance weights of these dimensions. Additionally, the concerned parties are embellished with the final ranking of safety risks in a more comprehensive way than those of existing assessment methods, leading to sagacious adoption of future prudent strategies for dealing with such risks occurring on construction sites. Numerous studies have documented the safety risks faced by construction workers including proposals for risk assessment models. However, the dimensions considered by such models are limited, generally constrained to risk event probability combined with risk impact severity. Overlooking other dimensions that are essential towards the calculation of safety risks' magnitude culminates in overshadowing the further adoption of fruitful mitigative actions. To overcome this shortcoming, this study proposes a novel 5D-SRAM.
Publisher: Emerald
Date: 17-09-2021
DOI: 10.1108/ECAM-04-2021-0286
Abstract: Green walls (GWs), comprising living walls and green facades, have been touted as environmentally friendly products in architectural design. GWs can be viable in every aspect of sustainability they provide residents of buildings with a wide range of economic, social and environmental benefits. Despite this, the adoption rate of GW is still in its infancy stage, and the existing literature concerning the hindrances inhibiting GW adoption is very limited. To address these gaps, the aim of this paper is to identify and prioritize the hindrances to GW adoption in Hong Kong. After identifying 17 hindrances through an in-depth review of literature, the fuzzy Delphi method (FDM) is employed to refine the hindrances based on the local context with the help of 21 qualified experts in the field. Subsequently, Fuzzy Parsimonious Analytic Hierarchy Process (FPAHP) is exploited as a recently developed technique to prioritize the identified hindrances. Results reveal that the most significant hindrances to the adoption of GW are maintenance cost, high installation cost, difficulties in maintenance, sophisticated implementation and inducement to fire. Findings call for scholars to address ways to improve GW installation practices and methods in order to eradicate the hindrances and provide lessons for policymakers, assisting them in facilitating the larger-scale adoption of GW. Considering the dearth of studies on hindrances to the adoption of GWs, this paper provides a comprehensive outlook of the issue, providing knowledge that can be used as a building block for future scholars within the field. It also provides valuable insights for stakeholders within the construction industry about the hindrances to the adoption of GWs which could direct their efforts toward better implementation of it.
Publisher: MDPI AG
Date: 13-11-2020
Abstract: 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.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Haleh Sadeghi.