A Concept Model to Scale the Impact of Safety Risk in a Construction Project Using a Semi Quantitative Method

Purpose This paper aims to explore the integration of artificial intelligence (AI) in smart cities (SC) as a key aspect of enhancing urban governance and achieving the UN's Sustainable Development Goals (SDGs). This integration presents significant opportunities and certain risks that require careful and proportionate management. Design/methodology/approach Adopting a multidisciplinary approach, and using a hierarchical impact assessment method, this study suggests how to measure the impact of AI-enhanced SC projects on ethical principles throughout respective projects’ life cycle. Having outlined a typology of SC applications, and having matched them with specific AI models, this paper develops and applies an analytical framework that serves as a valuable tool for monitoring ethical aspects of AI-based projects implemented in SC. Findings The study presents a descriptive model and a single visual scheme that synthesize the analytical framework. These tools provide support to public and private stakeholders, including citizens, NGOs and academic and research institutes by offering a unified method to assess and understand the ethical implications of complex SC systems. Through a hierarchical approach, the study aggregates the impacts of child nodes at each layer. Originality/value The study's contribution consists in the introduction of a comprehensive analytical framework that enables a structured assessment of ethical implications in AI-enhanced SC projects. By providing a clear approach for monitoring ethical impacts, the research fills a gap in literature as well as in practice as regards responsible and ethical use of AI in urban governance.

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Methodology for the Selection of Risk Response Actions while Considering Corporate Objectives in the Metalworking Industry

Cuadros-López,

Bustos-Useche,

Bustos-Useche

2024

Ing.

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Context: Projects in metalworking companies are affected by risk. Proper risk management depends on the responses provided to improve the project plan. However, multiple potential actions may result in constraints due to multiple factors. The purpose of this article is to propose a hybrid approach to solve the problem of selecting risk response actions while considering strategic objectives, fuzzy logic, and simulation. Method: First, 334 risks were identified through a literature review and a discussion with experts. These were then filtered, resulting in 70 operational risks. Subsequently, the ten critical risks were prioritized using the risk matrix. Then, using Monte Carlo simulation and correlation analysis, the activities most affected by the risks were identified. Finally, potential response actions were designed for each case, and fuzzy logic and quality function deployment were applied to evaluate them. Results: The selected responses were framed within the strategic objectives, i.e., customer satisfaction, business profitability, and implementation of new technologies. This, while considering some corporate attributes that the actions had to meet finishing the project on time, having low costs, and meeting the scope. The selected actions had a better profile than others seeking to minimize time or costs. Conclusions: EPCC projects are complex and often suffer from gaps in scope, time, and cost. Risk analysis and the selection of responses in the planning phase help to improve performance. This study developed a risk response plan for a project executed in Brazil. Risks were identified, classified, and mitigated using simulations, resulting in an 11-day reduction in the project’s estimated duration.

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A Concept Model to Scale the Impact of Safety Risk in a Construction Project Using a Semi Quantitative Method

Cited by 3 publications

References 5 publications

Proposal of a multidimensional risk assessment methodolgy to assess ageing workforce in a manufacturing industry: A pilot case study

Proposal of a multidimensional risk assessment methodolgy to assess ageing workforce in a manufacturing industry: A pilot case study

AI-driven innovation in smart city governance: achieving human-centric and sustainable outcomes

Methodology for the Selection of Risk Response Actions while Considering Corporate Objectives in the Metalworking Industry

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