Generative Artificial Intelligence (AI) holds significant potential for revolutionizing the Architecture, Engineering, and Construction (AEC) industry by automating complex tasks such as construction scheduling, hazard recognition, resource leveling, information retrieval from BIM, etc. However, realizing this potential requires a strategic approach to ensure effective utilization and maximum benefit. This paper presents guidelines for prompt design and engineering to elicit desired responses from ChatGPT, a Generative AI tool, in AEC applications. Key steps include understanding user intent, leveraging model capabilities, and optimizing prompt structures. By following these guidelines, stakeholders in the AEC industry can harness the power of Generative AI to improve construction scheduling processes, increase project efficiency, and ultimately drive innovation and growth in the industry. Several illustrative examples on construction scheduling and hazard recognition are provided to demonstrate the methodology proposed in this research. It is concluded that Generative AI, when effectively utilized, significantly enhances project scheduling and hazard recognition capability in the AEC industry with minimal error.

The construction industry remains among the most hazardous sectors globally, facing persistent safety challenges despite advancements in occupational health and safety OHS) measures. The objective of this study is to systematically analyze the use of Artificial Intelligence (AI) in construction safety management and to identify the most effective techniques, data modalities, and validation practices. The method involved a systematic review of 122 peer-reviewed studies published between 2016 and 2025 and retrieved from major academic databases. The selected studies were classified by AI technologies including Machine Learning (ML), Deep Learning (DL), Computer Vision (CV), Natural Language Processing (NLP), and the Internet of Things (IoT), and by their applications in real-time hazard detection, predictive analytics, and automated compliance monitoring. The results show that DL and CV models, particularly Convolutional Neural Network (CNN) and You Only Look Once (YOLO)-based frameworks, are the most frequently implemented for personal protective equipment recognition and proximity monitoring, while ML approaches such as Support Vector Machines (SVM) and ensemble algorithms perform effectively on structured and sensor-based data. Major challenges identified include data quality, generalizability, interpretability, privacy, and integration with existing workflows. The paper concludes that explainable, scalable, and user-centric AI integrated with Building Information Modeling (BIM), Augmented Reality (AR) or Virtual Reality (VR), and wearable technologies is essential to enhance safety performance and achieve sustainable digital transformation in construction environments.

Bridges are crucial components of infrastructure networks that facilitate national connectivity and development. According to the National Bridge Inventory (NBI) and the Federal Highway Administration (FHWA), the cost to repair U.S. bridges was recently estimated at approximately USD 164 billion. Traditionally, bridge inspections are performed manually, which poses several challenges in terms of safety, efficiency, and accessibility. To address these issues, this research study introduces a method using Unmanned Aerial Systems (UASs) to help automate the inspection process. This methodology employs UASs to capture visual images of a concrete bridge deck, which are then analyzed using advanced machine learning techniques of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to detect damage and delamination. A case study on the Beyer Road Concrete Bridge in Michigan is used to demonstrate the developed methodology. The findings demonstrate that the ViT model outperforms the CNN in detecting bridge deck damage, with an accuracy of 97%, compared to 92% for the CNN. Additionally, the ViT model showed a precision of 96% and a recall of 97%, while the CNN model achieved a precision of 93% and a recall of 61%. This technology not only enhances the maintenance of bridges but also significantly reduces the risks associated with traditional inspection methods.

Temperature segregation in Hot Mixed Asphalt (HMA) pavement construction leads to performance problems, such as reduced fatigue life. During construction, Quality Assurance (QA) inspection procedures are required to evaluate the pavement condition and detect the segregated areas. In traditional HMA highway construction inspection processes, temperature differences are investigated manually, by sampling the HMA behind the paver. In these processes, inspectors are required to work adjacent to traffic and alongside moving or backing equipment. These processes do not provide a complete temperature profile of the mat, endanger the inspectors’ safety, and require on-site experienced inspectors. An Uncrewed Aerial System (UAS) enables HMA pavement construction inspection to be conducted within a remote, non-destructive, safe, and efficient framework. The objective of this research is to design an automated UAS imaging workflow for HMA pavement construction inspection, mainly locating temperature segregation. The primary contribution of this paper is to provide Departments of Transportations (DOTs) and contractors with workflows for creating enhanced remote inspection procedures and detailed thermal profiles of the placed HMA mat. The application of the proposed workflow is illustrated using an HMA construction project in Michigan.

The growing number of older bridges has resulted in an increase in structural flaws, demanding frequent inspections and maintenance. Structural degradation accelerates post-damage recovery, emphasizing the necessity of preventive interventions. The use of Uncrewed Aerial Vehicle Systems (UASs) for bridge inspections represents a significant development in structural health monitoring (SHM). Traditional inspection methods are labor-intensive, time-consuming, expensive, and require access to high or difficult-to-reach areas, posing safety risks to inspectors. This study focuses on identifying drones that can efficiently support bridge inspection activities. Key factors influencing UAS selection include flight performance, flying modes, cost, sensor capabilities, payload capacity, and controller communication. The primary objective of this paper is to provide guidance to inspectors and transportation agencies regarding the capabilities and limitations of commercially available drones. It also outlines potential cost considerations associated with drone selection, including pilot skill level, platform cost, and sensor integration. These factors may vary depending on the type and complexity of the bridge being inspected. By addressing these aspects, this paper aims to assist decision-makers in making informed choices regarding the use of UASs for bridge inspection applications.

Artificial Intelligence (AI) is revolutionizing Construction Management (CM) through automation, predictive analytics, and real-time decision-making throughout the project lifecycle.This study aims to provide a comprehensive and structured synthesis of AI models and their applications in CM. This paper presents a systematic review of 191 peer-reviewed articles published between 2020 and 2025, aiming to integrate the current state of AI implementation in CM, focusing on AI methods and models and their applications in CM. Compared to previous reviews that take these factors individually or focus narrowly on specific techniques, this study offers a comprehensive taxonomy that systematically maps AI techniques against CM functions and integration platforms. The results reveal that AI applications are primarily concentrated in risk and safety management, decision support, and monitoring and control, while domains such as legal analytics, robotics, and cybersecurity remain underexplored. Furthermore, Computer Vision (CV) and Deep Learning (DL) dominate tasks such as safety monitoring and defect detection, whereas Machine Learning (ML) and optimization algorithms are widely applied in cost estimation and scheduling. It also addresses developments rarely covered in construction research, including Generative AI (Gen-AI), Explainable AI (XAI), and transformer models, presenting a strategic framework for the widespread adoption of AI in the construction environment. This study contributes a structured taxonomy that systematically links AI models with CM functions and enabling technologies, providing a comprehensive synthesis of emerging trends and research gaps.