This study investigates the current landscape of generative AI and LLM applications in architecture, engineering, and construction (AEC), focusing on trends, practical implications, educational strategies, and imperatives for upskilling. Employing a six-stage systematic review sourced from Google Scholar, Scopus and Web of Science, 120 papers were analyzed to provide a comprehensive understanding of the role of these technologies in shaping the future of the AEC industry. By addressing these objectives, the research contributes to enhancing knowledge about the potential impacts of generative AI and LLMs on the AEC industry and provides insights into strategies for leveraging these technologies effectively. This study underscores the transformative impact of AI and advanced technologies on the AEC sector and education. By enhancing learning experiences and optimizing construction processes, AI fosters personalized education and efficient project management. The study’s significance lies in its identification of necessary skills and competencies for professionals, ensuring effective AI integration. Implications include the need for continuous professional development, formal education, and practical training to leverage AI’s potential fully. This paves the way for sustainable, intelligent infrastructure and accessible, adaptive learning environments, driving innovation and efficiency in both fields.

A robot can accurately localize itself and navigate in an indoor environment based on information about the operating environment, often called a world or a map. While typical maps describe structural layouts of buildings, the accuracy of localization is significantly affected by non-structural building elements and common items, such as doors, appliances, and furniture. This study enhances the robustness and accuracy of indoor robot localization by dynamically updating the semantic building map with non-structural elements detected by sensors. We propose modified Adaptive Monte Carlo Localization (AMCL), integrating object recognition and map updating into the traditional probabilistic localization. With the proposed approach, a robot can automatically correct errors caused by non-structural elements by updating a semantic building map reflecting the current state of the environment. Evaluations in kidnapped robot and traditional localization scenarios indicate that more accurate and robust pose estimation can be achieved with the map updating capability.

Pose estimation of heavy construction equipment is the key technology for real-time safety monitoring in road construction sites where heavy equipment and workers on foot collaborate in proximity. Ultra-wideband (UWB) radios hold great promise among various sensing technologies for providing accurate object localization in indoor and outdoor environments. However, in a road construction environment with heavy vehicles and equipment, the performance of UWB radios drastically declines because of blockages in the transmission signal between the transmitter and receiver causing Non-Line of Sight (NLOS) situations. To address this deficiency, our study presents a real-time pose estimating system called ViPER+ that can overcome NLOS situations and accurately determine the boundary of heavy construction equipment with multiple UWB tags attached to the surface of the equipment. To remove the impact of NLOS signals, we introduced an input correction method prior to localization to correct the input of the localization algorithm. Evaluation of ViPER+ in a real construction environment indicates that embedding NLOS detection technique in UWB-based pose estimation resulted in 40% improvements in location accuracy and 25% improvement in update rate compared to its previous implementation (ViPER).

An obstacle toward construction robotization is the lack of methods to plan robot operations within the entire construction planning process. Despite the strength in modeling construction site conditions, 4D BIM technologies cannot perform construction robot task planning considering the contexts of given work environments. To address this limitation, this study presents a framework that integrates 4D BIM and robot task planning, presents an information flow for the integration, and performs high-level robot task planning and detailed simulation. The framework uniquely incorporates a construction robot knowledge base that derives robot-related modeling requirements to augment a 4D BIM model. Then, the 4D BIM model is converted into a robot simulation world where a robot performs a sequence of actions retrieving construction-related information. A case study focusing on the interior wall frame installation demonstrates the potential of systematic integration in achieving context-aware robot task planning and simulation in construction environments. The proposed framework can potentially be utilized by construction managers and planners to plan robot operations considering the construction situation.

An obstacle toward construction robotization is the lack of methods to plan robot operations within the entire construction planning process. Despite the strength in modeling construction site conditions, 4D BIM technologies cannot perform construction robot task planning considering the contexts of given work environments. To address this limitation, this study presents a framework that integrates 4D BIM and robot task planning, presents an information flow for the integration, and performs high-level robot task planning and detailed simulation. The framework uniquely incorporates a construction robot knowledge base that derives robot-related modeling requirements to augment a 4D BIM model. Then, the 4D BIM model is converted into a robot simulation world where a robot performs a sequence of actions retrieving construction-related information. A case study focusing on the interior wall frame installation demonstrates the potential of systematic integration in achieving context-aware robot task planning and simulation in construction environments.