Purpose: The objective of this research is to investigate the capabilities of the ChatGPT GPT-4 model, a form of artificial intelligence (AI), in comparison to human experts in the context of construction project risk management.Design/methodology/approach: Employing a mixed-methods approach, the study draws a qualitative and quantitative comparison between 16 human risk management experts from Finnish construction companies and the ChatGPT AI model utilizing anonymous peer reviews. It focuses primarily on the areas of risk identification, analysis, and control.Findings: ChatGPT has demonstrated a superior ability to generate comprehensive risk management plans, with its quantitative scores significantly surpassing the human average. Nonetheless, the AI model's strategies are found to lack practicality and specificity, areas where human expertise excels.Originality/value: This study marks a significant advancement in construction project risk management research by conducting a pioneering blind-review study that assesses the capabilities of the advanced AI model, GPT-4, against those of human experts. Emphasizing the evolution from earlier GPT models, this research not only underscores the innovative application of ChatGPT-4 but also the critical role of anonymized peer evaluations in enhancing the objectivity of findings. It illuminates the synergistic potential of AI and human expertise, advocating for a collaborative model where AI serves as an augmentative tool, thereby optimizing human performance in identifying and managing risks.

Modularization has recently attracted considerable interest among academics and practitioners. In the construction industry, several modular solutions have been introduced and examined; however, hardly any study comprehensively classifies the different modularization strategies according to their possibility to achieve various objectives of building investments. This research aims to develop a framework for identifying and classifying modularization strategies in construction according to their suitability to achieve the specific outcomes intended for a building or its subsystem. Using the literature on product modularity, product platforms and production strategies as a basis, this study has developed a theoretical framework that proposes connections between the main objectives of a building investment and the dimensions of modularization strategies. The framework is elaborated by testing the propositions, based on an empirical analysis of nine real-life case studies. The findings reveal that modularization can be a suitable approach to develop an innovative design solution; improve a project’s quality, cost and schedule performance; and enable flexibility in building use and maintenance. However, suggestions for achieving these objectives vary from project organization-driven to collaborative, integrated or supplier-driven modularization strategies. Developers, owners, contractors and module suppliers can use the framework when positioning and developing their roles in the construction supply chain.

In construction, the most relevant systems used for project management (PM) and project production management (PPM) in the planning and control phases are critical path method (CPM), last planner system (LPS), and location-based techniques (LB). Studies have addressed these systems, mostly in isolated fashions. This study aims to compare and contrast their use in terms of PM and PPM and clarify industry benefits in order to eliminate potential misunderstandings about their use. A survey was administered to construction professionals in Brazil, China, Finland, and the United States. No single system addresses all needs of PM and PPM. CPM is the dominant system when considering the following characteristics: primary industry types, type of organization, size of organization, professional position within the organization, and area of work. Contributions to knowledge include that CPM is a contract requirement with perceived benefits associated with critical path analysis; LB and LPS have perceived benefits regarding continuous flow and use of resources, treatment of interferences, and improving production control. All systems were found to have a similar level of benefits for management of contracts, delay and change, and evaluation of the root causes of delays. The industry can benefit from aligning project scheduling methods with project needs.

A new, process-oriented approach is needed in construction management. Lean construction emphasizes the concept of flow as a way to understand production in construction, yet there is still no accepted metric for measurement of flow quality. This has hampered research and practice. The proposed construction flow index (CFI) is a composite measure that reflects the quality of production flow in repetitive construction projects. It incorporates measures of work continuity for crews, processing continuity for locations, production rate variation, amounts of work in progress, interference and operation sequence logic. Expert knowledge was acquired to establish weights for the CFI parameters, and its use was tested in evaluating the planned and actual production flows for a number of projects. Project managers can use the CFI to evaluate the quality of their construction plans and to measure and communicate production flow quality status to trade crews, enabling management and improvement of production flow. The CFI is also a valuable tool for construction research. The CFI challenges traditional construction management by measuring flow, where standard practice only measures transformation (earned value). It challenges lean construction practice using the Last Planner System, suggesting that the percent plan complete measure of plan reliability is insufficient.