This study critically examines the implications of international transport corridor projects for Central Asian countries, focusing on the Western-backed Transport Corridor Europe-Caucasus-Asia (TRACECA), the Chinese initiative “One Belt—One Road”, and the International North-South Transport Corridor (INSTC) supported by the Russian Federation, India, and Iran. The analysis underscores the risks associated with Western projects, highlighting a need for a more explicit commitment to substantial infrastructure investments and persistent contradictions among key investors and beneficiaries. While the Chinese initiative presents significant benefits such as transit participation, infrastructure development, and economic investments, it also carries risks, notably an increased debt burden and potential monopolization by Chinese corporations. The study emphasizes that Central Asian countries, though indirect beneficiaries of INSTC, may not be directly involved due to geographical constraints. Study findings advocate for Central Asian nations to balance foreign investments, promote economic integration, and safeguard political and economic sovereignty. The study underscores the region’s wealth of natural and human resources, emphasizing the potential for increased demand for goods and services with improved living standards, strategically positioning these countries in the evolving global economic landscape.

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.

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.

Projects implemented under life cycle contracts have become increasingly common in recent years to ensure the quality of construction and maintenance of energy infrastructure facilities. A key parameter for energy facility construction projects implemented under life cycle contracts is their duration and deadlines. Therefore, the systematic identification, monitoring, and comprehensive assessment of risks affecting the timing of work on the design and construction is an urgent practical task. The purpose of this work is to study the strength of the influence of various risks on the duration of a project implemented on the terms of a life cycle contract. The use of the expert assessment method allows for identifying the most likely risks for the design and construction phases, as well as determining the ranges of deviations from the baseline indicator. Using the obtained expert evaluations, a model reflecting the range and the most probable duration of the design and construction works under the influence of risk events was built by the Monte-Carlo statistical method. The results obtained allow monitoring and promptly detecting deviations in the actual duration of work from the basic deadlines set in the life cycle contract. This will give an opportunity to accurately respond to emerging risks and build a mutually beneficial relationship between the parties to life cycle contracts.