Risks and uncertainties are inevitable in construction projects and can drastically change the expected outcome, negatively impacting the project’s success. However, risk management (RM) is still conducted in a manual, largely ineffective, and experience-based fashion, hindering automation and knowledge transfer in projects. The construction industry is benefitting from the recent Industry 4.0 revolution and the advancements in data science branches, such as artificial intelligence (AI), for the digitalization and optimization of processes. Data-driven methods, e.g., AI and machine learning algorithms, Bayesian inference, and fuzzy logic, are being widely explored as possible solutions to RM domain shortcomings. These methods use deterministic or probabilistic risk reasoning approaches, the first of which proposes a fixed predicted value, and the latter embraces the notion of uncertainty, causal dependencies, and inferences between variables affecting projects’ risk in the predicted value. This research used a systematic literature review method with the objective of investigating and comparatively analyzing the main deterministic and probabilistic methods applied to construction RM in respect of scope, primary applications, advantages, disadvantages, limitations, and proven accuracy. The findings established recommendations for optimum AI-based frameworks for different management levels—enterprise, project, and operational—for large or small data sets.

Real estate markets are ideal investment options that lead to the construction industry’s and the economy’s growth. Therefore, having appropriate investment and valuation strategies is a critical success factor. Most established valuation methods emphasize market value and economic factors and are ignorant about buildings’ technical and structural attributes. Therefore, due to the process ambiguity and lack of information access, the estimated price usually differs from the real property value. In this research, a revised valuation framework is proposed based on the life cycle cost (LCC) of residential properties, focusing on the operation phase. LCC consists of all costs related to an asset during different phases of its lifecycle, and it helps determine the net present value of the property. For systematically storing and analyzing technical and financial information, building information modeling (BIM) was proposed. Despite being widely used in the design and construction phases, its application and competitive advantage to real estate developers and managers during the operation phase are not transparent. This research benefitted from the 5D BIM model with a level of development (LOD) of 300 to increase the transparency and validity of valuation. An 18.25% difference between the calculated price of two case studies in Tehran and their inflated market prices proved this assertion.

While risks in construction projects have severe consequences on the project schedule, budget, quality, and safety, the realm of Risk Management (RM) falls short in terms of efficiency, productivity, and automation. Artificial Intelligence technologies, especially Machine Learning, can address these issues and utilize risk data effectively for informed decision-making. However, due to the infrequent and unstructured data registration in projects, deterministic RM approaches with a frequentist inference are inapplicable to such small databases and cannot represent the actual risk exposure accurately. This research proposes two solutions to compensate for the data scarcity issue: a) Elicitation, which allows for the integration of subjective and experience-based expert opinions with the existing objective project database, and b) Synthetic data generation using Generative Adversarial Networks (GANs) for data augmentation. A probabilistic model based on a Bayes inference is developed, where experts' opinions are quantified and used for learning the structure and primary parameters in a Bayesian Networks (BN) representing the overall risk network of the case study. A case study of 44 construction projects in Italy is utilized for belief updates in the network, and cross-validation and elicitation methods are employed to validate the results. The results confirm the effectiveness of both solutions, as the overall model accuracy increased by 18% using GANs for synthetic generation and the collective experts' opinions served as a basis to prevent the overfitting of the model to the limited project data. These findings underscore the superiority of probabilistic ML approaches in limited databases, contributing to the body of knowledge in the construction RM field and to the enhancement of precision and productivity of RM practices in the industry.

The uptake of a circular economy (CE) in the building sector is challenging, primarily due to the complexity associated with the design process and the dynamic interaction among architects, engineers, and construction (AEC) stakeholders. The standard and typical design process and construction methods raise concerns about building life cycles. Buildings should not only fulfill current needs, but one also needs to consider how they will function in the future and throughout their lifetime. To address these complexities, early planning is required to guide designers in holistically applying systems thinking to deliver CE outcomes. This paper outlines a critical review of CE implementation in buildings, with a proposed trifecta of approaches that significantly contribute to the development of circular buildings (CBs). The findings outline a proposed visualized framework with a conceptual formula that integrates CE design strategies to simplify and enhance AEC stakeholders’ perception of the circularity sequence in buildings. By strategically integrating loop-based strategies with the value retention process (VRP) and design for X (DFX) strategies, along with efficient assessment tools and technologies, it becomes feasible to embrace a CE during the design phase. The outcome of this review informs AEC stakeholders to systematically and strategically integrate the critical dimensions of a CE throughout the building life cycle, striking a balance between environmental concern, economic value, and future needs.