Search results

This study aims to raise awareness of the key challenges, opportunities and priorities for evidence-based strategies’ application to inform building and urban design decisions.

This study uses deductive qualitative content and manifest analysis, using semi-structured interviews undertaken with building and urban design professionals who represent a UK-based organisation.

The challenges associated with the practical implementation of frameworks, potential application areas and perceived areas of concern have been identified. These not only include the need to practically test their use, but also to identify the most appropriate forums for their use. Participant responses indicate the need to further develop engagement strategies for their practical implementation, clearly communicating the benefits and efficiencies to all stakeholders.

Implications/ limitations of this study come with the fact that some of the respondents may possess inadequate professional experience in properly evaluating all the questions. Additionally, the information gathered is restricted to the UK geographical context, as well as coming from one organisation, because of data accessibility.

The findings of the study can be adopted by designers in the strategic definition level to overcome the key challenges associated with the use of evidence-based strategies, enhancing their decision-making processes.

As a theoretical contribution to knowledge, this study enhances the body of knowledge by identifying the challenges associated with the practical implementation of evidence-based strategies to inform building and urban design decisions. In practice, the findings aid urban planners, designers and academics in embedding and adopting strategies that enhance decision-making processes.

The purpose of this study is to test and validate a scale for measuring project resilience in the construction sector within the built environment. By identifying relevant indicators and items, the study aims to enhance the resilience of construction projects and minimize losses and failures resulting from disruptive events such as societal, technological, biological and environmental hazards (e.g. Covid-19, war in Ukraine, shortage of resources, etc.).

The study uses a quantitative approach, specifically exploratory factor analysis and confirmatory factor analysis, to evaluate the suitability, dimensionality and reliability of the proposed indicators and items for measuring project resilience in the construction sector.

The study found that 9 indicators and 34 items were suitable for measuring project resilience in the construction sector, and the proposed model showed good fit for the two dimensions of project resilience, which may have practical implications for project managers in the construction sector within the built environment.

The study proposes a new scale for measuring project resilience in the construction sector, which is a novel contribution to the field of project management. The study identifies specific indicators and items that are relevant to this industry, which may have practical implications for project managers in this sector. The study also highlights the need for further research to make the project resilience scale more robust and reliable.

Near-fault pulse-like ground motions have distinct and very severe effects on reinforced concrete (RC) structures. However, there is a paucity of recorded data from Near-Fault Ground Motions (NFGMs), and thus forecasting the dynamic seismic response of structures, using conventional techniques, under such intense ground motions has remained a challenge.

The present study utilizes a 2D finite element model of an RC structure subjected to near-fault pulse-like ground motions with a focus on the storey drift ratio (SDR) as the key demand parameter. Five machine learning classifiers (MLCs), namely decision tree, k-nearest neighbor, random forest, support vector machine and Naïve Bayes classifier , were evaluated to classify the damage states of the RC structure.

The results such as confusion matrix, accuracy and mean square error indicate that the Naïve Bayes classifier model outperforms other MLCs with 80.0% accuracy. Furthermore, three MLC models with accuracy greater than 75% were trained using a voting classifier to enhance the performance score of the models. Finally, a sensitivity analysis was performed to evaluate the model's resilience and dependability.

The objective of the current study is to predict the nonlinear storey drift demand for low-rise RC structures using machine learning techniques, instead of labor-intensive nonlinear dynamic analysis.

In the current economic scenario characterized by turbulence, innovation is a requisite for company's growth. The innovation activities are implemented through the realization of innovative project. This paper aims to prospect the promising opportunities coming from the application of Machine Learning (ML) algorithms to project risk management for organizational innovation, where a large amount of data supports the decision-making process within the companies and the organizations.

Moving from a structured literature review (SLR), a final sample of 42 papers has been analyzed through a descriptive, content and bibliographic analysis. Moreover, metrics for measuring the impact of the citation index approach and the CPY (Citations per year) have been defined. The descriptive and cluster analysis has been realized with VOSviewer, a tool for constructing and visualizing bibliometric networks and clusters.

Prospective future developments and forthcoming challenges of ML applications for managing risks in projects have been identified in the following research context: software development projects; construction industry projects; climate and environmental issues and Health and Safety projects. Insights about the impact of ML for improving organizational innovation through the project risks management are defined.

The study have some limitations regarding the choice of keywords and as well the database chosen for selecting the final sample. Another limitation regards the number of the analyzed papers.

The analysis demonstrated how much the use of ML techniques for project risk management is still new and has many unexplored areas, given the increasing trend in annual scientific publications. This evidence represents an opportunities for supporting the organizational innovation in companies engaged into complex projects whose risk management become strategic.