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The construction industry is an industry which has gained notoriety when it comes to both physical and mental health problems. Compared to other industries, the construction sector has a higher prevalence of many stressors and mental health concerns. This calls for mechanisms to cope with these concerns. One coping mechanism propounded to help cope and adapt in the face of pressures and challenges is “Mental Toughness (MT)”. While mental toughness has been widely studied in various fields, there is a paucity of comprehensive research examining its significance among construction professionals. Thus, the motivation of this study is to establish the mental toughness characteristics among construction professionals in Ghana.

The construction industry is an industry which has gained notoriety when it comes to both physical and mental health problems. Compared to other industries, the construction sector has a higher prevalence of mental health concerns. One coping mechanism propounded to help cope and adapt in the face of pressures and challenges is “Mental Toughness (MT)”. While mental toughness has been widely studied in various fields, there is a paucity of comprehensive research examining its significance among construction professionals. Thus, the motivation of this study is to establish the mental toughness characteristics among construction professionals.

the study's findings revealed 13-factor model characteristics of mental toughness with 43 variables for mentally tough performers in the construction environment. These 13-factor models include Pressure Management (PM), Motivation (M), Emotional Intelligence (EI), Interpersonal self-belief (SB), Tough Attitude (TA), Job-related self-belief (SBB), Ethical Values (EV), Commitment (C), Focus (F), Optimism (OP), Expertise and Competence (EC), Imagery Control (IC) and Resilient (R).

The outcome of this study has significant practical implications for various stakeholders. For construction professionals the identified factors provide valuable insights into the psychological attributes and behaviours that contribute to mental toughness among construction professionals. Understanding these characteristics can empower professionals to develop strategies for coping with stress, maintaining focus, and fostering resilience in challenging construction environments to achieve optimum performance levels consistently.

The originality of this study's findings stems from the fact that it is among the first to provide greater insight into mental toughness characteristics considered by professionals (quantity surveyors, construction managers, engineers, project managers, architects, estate managers) in the construction industry.

Recent United Nations Climate Change Conferences recognise extreme climate change of heatwaves, floods and droughts as threatening risks to the resilience and success of public–private partnership (PPP) infrastructure projects. Such conferences together with available project reports and empirical studies recommend project managers and practitioners to adopt smart technologies and develop robust measures to tackle climate risk exposure. Comparatively, artificial intelligence (AI) risk management tools are better to mitigate climate risk, but it has been inadequately explored in the PPP sector. Thus, this study aims to explore the tools and roles of AI in climate risk management of PPP infrastructure projects.

Systematically, this study compiles and analyses 36 peer-reviewed journal articles sourced from Scopus, Web of Science, Google Scholar and PubMed.

The results demonstrate deep learning, building information modelling, robotic automations, remote sensors and fuzzy logic as major key AI-based risk models (tools) for PPP infrastructures. The roles of AI in climate risk management of PPPs include risk detection, analysis, controls and prediction.

For researchers, the findings provide relevant guide for further investigations into AI and climate risks within the PPP research domain.

This article highlights the AI tools in mitigating climate crisis in PPP infrastructure management.

This article provides strong arguments for the utilisation of AI in understanding and managing numerous challenges related to climate change in PPP infrastructure projects.