Heating, Ventilation, and Air Conditioning (HVAC) systems contribute a considerable share of total global energy consumption and carbon dioxide emissions, putting them at the heart of the issues of decarbonization and removing barriers to achieving net-zero emissions and sustainable development goals. Nevertheless, the effective implementation of artificial intelligence (AI)-based methods to optimize energy efficiency while ensuring occupant comfort in multifarious settings remains to be fully realized. This paper provides a systematic review of state-of-the-art practices (2018 and later) using AI algorithms like machine learning (ML), deep learning (DL), and other computation-based techniques that have been deployed to boost HVAC system performance. The review highlights that AI-driven control strategies can reduce energy consumption by up to 40% by dynamically adapting to environmental conditions and occupancy levels. Compared to other work that focuses on single aspects of HVAC management, this work deals with the methods of control and maintenance in a comprehensive manner. Rather than focusing on abstract applications of machine learning models, this study underlines their applicability in HVAC systems, bridging the science–practice gap. This study highlights the prospective role AI could play, on the one hand, by enhancing HVAC systems’ incorporation, energy consumption, and building technologies, while, on the other hand, also addressing the potential uses AI can have in practical applications in the future, bridging gaps and addressing challenges.

The negative impacts of change orders on construction projects are well documented in the literature. Effective management of change orders requires a comprehensive approach to identify and analyze their causes. A change order in construction projects is usually an outcome of combining several interrelated causes, rather than a single cause. Most of the previous studies have considered the causes of change orders as independent causes. However, the causes of change orders have a complicated causal structure, which makes it difficult to independently analyze and prioritize them. Therefore, this study contributes to the body of knowledge in change order management by proposing a fuzzy cognitive map (FCM) approach, which has the capability to analyze the causes of change orders considering their entire causal interactions. The root causes of change orders, as well as the direct ones, can be identified and prioritized by the proposed approach. The manageability of the causes can also be analyzed by the FCM approach. The performance of the proposed approach is evaluated by implementing it in a real construction project. A modeling-validating approach is used to construct and validate the FCM model. It is believed that the proposed FCM approach presents a powerful tool for analyzing the causes of change orders. In addition, this research contributes to knowledge in the area of FCMs by proposing a new inference law.

Purpose: The purpose of this paper is to present a hybrid simulation approach for predicting the value of labor productivity taking account of various continuous influencing factors and the interactions between different agents involved in the project. Design/methodology/approach: The various continuous factors affecting labor productivity are simulated using system dynamics (SD). The heterogeneity of different agents involved in the project and their interactions is accounted using agent-based modelling (ABM). The developed ABM and SD models are finally integrated to simulate the value of labor productivity taking account of all the influencing factors. Findings: The proposed hybrid simulation tool is implemented in a real project to evaluate its perfomance. The value of labor productivity is simulated by taking account of all the influencing factors. The most appropriate execution strategy is then selected using the developed hybrid SD-ABM approach to improve productivity. It is shown that the number of working groups and their movement patterns affect the severity of the groups’ interferences which will in turn affect the value of labor productivity. Practical implications: This research helps project managers to predict and improve the value of labor productivity taking account of all the influencing factors. Originality/value: It is believed that the proposed hybrid SD-ABM simulation approach offers a novel and robust tool for modeling labor productivity because the effects of various continuous influencing factors and the interactions between different agents are taken into account through the combination of SD and ABM. Many complex problems faced in construction projects involve interacting elements of a different nature, and the integration of SD with ideas from ABM offers potential to combine the strengths of the two methodologies to solve the problem.

Stakeholders carry out construction projects under fast-changing conditions. The conditions can undermine the concept of a stable and prosperous construction plan without an appropriate permit and an active and targeted plan for environmental management. Therefore, the decision maker often faces many challenges of Multi-Criteria Decision-Making (MCDM) when it comes to solving the construction management proper response selection problem for planning delay changes when sustainable environment requirements are essential. Any addition, reduction, or modification of the original project plan is a change to the project and impacts the environment. Change occurrence is a probable issue while projects are implemented. One of the most complex tasks for the project manager is to work correctly and to find the most suitable decisions for the not precisely predetermined future expectations of a change. Therefore, the relevant criteria of values must reflect the uncertain properties of the problem model. Similar problems require fuzzy or grey MCDM methods. The paper introduces a new MCDM approach, which combines four different MCDM methods with grey numbers: the SWARA, TOPSIS-GM, Additive Ratio ASsessment with Grey Numbers (ARAS-G) techniques and Geometric Mean to cover uncertainty and improve the problem-solving model. An analysis of a case study has examined and highlighted four possible alternatives described by eight performance criteria (cost, duration, and some linguistic criteria). Stakeholders determined the best alternative, calculated the efficiency of choice, and practically implemented the best option.

Conflict between parties is a common issue in construction projects. In the present article, the conflicts be-tween contractor and employer in delayed Design-Bid-Build projects have been studied. Defining a case study, a dispute resolution method has been proposed. This case has been considered as a MCDM problem. This problem has been as-sumed as a discrete zero-sum two-person matrix game with grey numbers. Among the four alternatives available for con-tractor and employer in the proposed case study, termination is the last alternative that decision makers choose. Based on different risk values, authors determined the optimal solution for both parties. This article integrates some linguistic criteria together with time and cost, providing the better conditions to avoid lengthy bargaining.