Search results

1 – 4 of 4
Per page
102050
Citations:
Loading...
Access Restricted. View access options
Article
Publication date: 26 December 2024

Nazi Soltanmohammadlou, Carol K.H. Hon, Robin Drogemuller, Moslem Sheikhkhoshkar and Farzad Rahimian

This paper aims to analyze the current state of technological advancements research in addressing the diverse risk factors involved in earthmoving equipment operations through…

22

Abstract

Purpose

This paper aims to analyze the current state of technological advancements research in addressing the diverse risk factors involved in earthmoving equipment operations through Rasmussen's (1997) risk management framework. It examines how existing technologies research capture, manage and disseminate risk information across various levels of safety management by defining their core functionalities. The research highlights gaps in current technological solutions research regarding the flow of information in the risk management framework. It emphasizes the need for an integrated approach in technological advancements to enhance the holistic safety management approach capable of capturing various risks across different levels of risk management.

Design/methodology/approach

This research employs a multistep approach. Initially, earthmoving equipment risk factors and functionalities of technological solutions were identified through a systematic review of current scholarly works. Subsequently, social network analysis (SNA) and Pareto analysis were applied to evaluate and determine the importance of risk factors and functionalities of technologies for improving them.

Findings

The findings highlight the importance of multilevel approaches that expand technological functionalities to address risk factors across all levels of Rasmussen's (1997) risk management framework. The current combination of technological advancements focuses primarily on on-site monitoring, congested work sites, site layout/path planning, utility problems, safety training, and blind spot and visibility. Site monitoring and warning systems, supported by sensors and computer vision (CV), are pivotal for identifying risks and enabling data-driven safety management. However, workforce-level cognitive factors (W1-W6), which influence safety behavior, remain underexplored for enhancing their functionality to anticipation and response during the operation. Prevention is the core function of current technological solutions, emphasizing the need to address human and equipment risk factors such as sources of hazards in earthmoving operations. Learning: AI as a data-driven approach and IoT systems are key for future development, and when grounded in ontology-based knowledge of earthwork, they gain a structured vision of earthmoving equipment types, their interactions and the earthwork activities. It enhances the capabilities of these technologies to capture and manage complex interactions between hazard sources (human and equipment), supporting comprehensive risk factors across all levels of the risk management framework.

Originality/value

This paper elucidates that technological solutions for safety management in earthmoving equipment operations require a more holistic approach—grounded in an understanding of functionalities of technologies—to effectively capture risks across various levels of Rasmussen (1997) risk management. It emphasizes that technological solutions should not only address isolated hazards but also ensure the continuous flow of information on multiple risk factors across the risk management framework.

Details

Smart and Sustainable Built Environment, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2046-6099

Keywords

Available. Content available

Abstract

Details

Smart and Sustainable Built Environment, vol. 13 no. 3
Type: Research Article
ISSN: 2046-6099

Access Restricted. View access options
Article
Publication date: 6 February 2024

Moslem Sheikhkhoshkar, Hind Bril El Haouzi, Alexis Aubry and Farook Hamzeh

In academics and industry, significant efforts have been made to lead planners and control teams in evaluating project performance and control. In this context, numerous control…

149

Abstract

Purpose

In academics and industry, significant efforts have been made to lead planners and control teams in evaluating project performance and control. In this context, numerous control metrics have been devised and put into practice, often with little emphasis on analyzing their underlying concepts. To cover this gap, this research aims to identify and analyze a holistic list of control metrics and their functionalities in the construction industry.

Design/methodology/approach

A multi-step analytical approach was conducted to achieve the study’s objectives. First, a holistic list of control metrics and their functionalities in the construction industry was identified. Second, a quantitative analysis based on social network analysis (SNA) was implemented to discover the most important functionalities.

Findings

The results revealed that the most important control metrics' functionalities (CMF) could differ depending on the type of metrics (lagging and leading) and levels of control. However, in general, the most significant functionalities include managing project progress and performance, evaluating the look-ahead level’s performance, measuring the reliability and stability of workflow, measuring the make-ready process, constraint management and measuring the quality of construction flow.

Originality/value

This research will assist the project team in getting a comprehensive sensemaking of planning and control systems and their functionalities to plan and control different dynamic aspects of the project.

Details

Smart and Sustainable Built Environment, vol. 13 no. 3
Type: Research Article
ISSN: 2046-6099

Keywords

Access Restricted. View access options
Article
Publication date: 6 May 2020

Saeed Akbari, Farzad Pour Rahimian, Moslem Sheikhkhoshkar, Saeed Banihashemi and Mostafa Khanzadi

Successful implementation of infrastructure projects has been a controversial issue in recent years, particularly in developing countries. This study aims to propose a decision…

281

Abstract

Purpose

Successful implementation of infrastructure projects has been a controversial issue in recent years, particularly in developing countries. This study aims to propose a decision support system (DSS) for the evaluation and prediction of project success while considering sustainability criteria.

Design/methodology/approach

To predict sustainable success factor, the study first developed its sustainable success factors and sustainable success criteria. These then formed a decision table. A rough set theory (RST) was then implemented for rules generation. The decision table was used as the input for the rough set, which returned a set of rules as the output. The generated rulesets were then filtered in fuzzy inference system (FIS), before serving as the basis for the DSS. The developed prediction tool was tested and validated by applying data from a real infrastructure project.

Findings

The results show that the developed rough set fuzzy method has strong ability in evaluation and prediction of the project success. Hence, the efficacy of the DSS is greatly related to the rule-based system, which applies RST to generate the rules and the result of the FIS was found to be valid via running a case study.

Originality/value

Use of DSS for predicting the sustainable success of the construction projects is gaining progressive interest. Integration of RST and FIS has also been advocated by the seminal literature in terms of developing robust rulesets for impeccable prediction. However, there is no preceding study adopting this integration for predicting project success from the sustainability perspective. The developed system in this study can serve as a tool to assist the decision-makers to dynamically evaluate and predict the success of their own projects based on different sustainability criteria throughout the project life cycle.

Details

Construction Innovation , vol. 20 no. 4
Type: Research Article
ISSN: 1471-4175

Keywords

1 – 4 of 4
Per page
102050