The artificial intelligence (AI)-based architect’s profile’s selection (simply iSelection) uses a polymathic mathematical model and AI-subdomains’ integration for enabling automated and optimized human resources (HR) processes and activities. HR-related processes and activities in the selection, support, problem-solving, and just-in-time evaluation of a transformation manager’s or key team members’ polymathic profile (TPProfile). Where a TPProfile can be a classical business manager, transformation manager, project manager, or an enterprise architect. iSelection-related selection processes use many types of artifacts, like critical success factors (CSF), AI-subdomain’ integration environments, and an enterprise-wide decision-making system (DMS). iSelection focuses on TPProfiles for various kinds of transformation projects, like the case of the transformation of enterprises’ HRs (EHR) processes, activities, and related fields, like enterprise resources planning (ERP) environments, financial systems, human factors (HF) evolution, and AI-subdomains. The iSelection tries to offer a well-defined (or specific) TPProfile, which includes HF’s original-authentic capabilities, education, affinities, and possible polymathical characteristics. Such a profile can also be influenced by educational or training curriculum (ETC), which also takes into account transformation projects’ acquired experiences. Knowing that selected TPProfiles are supported by an internal (or external) transformation framework (TF), which can support standard transformation activities, and solving various types of iSelection’s problems. Enterprise transformation projects (simply projects) face extremely high failure rates (XHFR) of about 95%, which makes EHR selection processes very complex.

Scholars widely agree that modular technologies can significantly improve environmental sustainability compared to traditional building methods. There has been considerable debate about the viability of replacing traditional cast-in-place structures with modular construction projects. The primary purpose of this study is to determine the feasibility of using modular technology for construction projects in island areas. Thus, it is necessary to investigate the potential problems and suitable solutions associated with modular building project implementation. This study is accomplished through the use of qualitative and quantitative methods. It systematically examines desk research based on the wide academic literature and real case studies, collating secondary data from government files, news articles, professional blogs, and interviews. This research identifies several important barriers to the use of modular construction projects. Among the issues are the complexity of stakeholder engagement, limited practical skills and construction methodologies, and a scarcity of manufacturing capacity specialised for modular components. Fortunately, these unresolved challenges can be mitigated through fiscal incentives and governmental regulations, induction training programmes, efficient management strategies, and adaptive governance approaches. As a result, the findings support the feasibility of starting and advancing modular building initiatives in island areas. Project developers will likely be more willing to embrace and commit resources to initiate modular building projects. Additional studies can be undertaken to acquire the most recent first-hand data for detailed validation.

The US Infrastructure Investment and Job Act (IIJA), also commonly referred to as the Bipartisan Infrastructure Bill, passed in 2021, has drawn international attention. It aims to help to rebuild US infrastructure, including transportation networks, broadband, water, power and energy, environmental protection and public works projects. An estimated $1.2 trillion in total funding over ten years will be allocated. The Bipartisan Infrastructure Bill is the largest funding bill for US infrastructure in the recent history of the United States. This review article will specifically discuss funding allocations for roads and bridges, power and grids, broadband, water infrastructure, airports, environmental protection, ports, Western water infrastructure, electric vehicle charging stations and electric school buses in the new spending of the Infrastructure Investment and Job Act and why these investments are urgently necessary. This article will also briefly discuss the views of think tank experts, the public policy perspectives, the impact on domestic and global arenas of the new spending in the IIJA, and the public policy implications.

This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.

The present paper discusses the case of the Madrid Nuevo Norte Project (MNNP) in order to examine the relation of this mega-project with the city’s sustainable development. For this reason, the study used a qualitative approach using semi-structural interviews with experts (Madrid’s town hall, Madrid State, and the program management office and other external) that relayed strongly with MNNP. The expert panel requirements are split in six expertise areas: sustainability, urban development, urban planning, government or public affairs, project management or Madrid Nuevo Norte (MNN) key stakeholders. The study highlighted the vital importance of MNNP as a flagship sustainable project for the rest of Europe, that meets sustainability criteria for contributing substantially in the improvement of the quality of life of final users and for the community in general. For instance, it contributes to the regeneration of the city’s degraded area, to the interconnection of an isolated part of the city and public transportation connection, improving the external image of Madrid. Despite of it, there are some challenges that should be carefully managed such as applying sustainable solutions from other cities not properly tailored to Madrid, housing pricing accessibility increase due to the lack of terrain in Madrid and the politization of the project as discussion topic between local parties. In this context, local authorities should give particular emphasis in complying with the principles of sustainability for improving the overall performance of MNNP, ensuring social justice and prosperity for the people of Madrid.