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Intelligent construction refers to the integration of digital technologies, data-driven methods, artificial intelligence, and automated systems throughout the building life cycle to improve the planning, design, construction, and operation of built assets. It encompasses building information modeling (BIM), Internet of Things (IoT), digital twins, artificial intelligence (AI), machine learning, robotics, and construction automation, which can be combined to support information integration, monitoring, analysis, decision-making, and automated or semi-automated construction processes ^[1][2]. Rather than referring to a single technology, intelligent construction represents an integrated approach in which digital information and computational technologies are used to improve construction processes, productivity, safety, quality, and decision-making across the project life cycle ^[1]. BIM provides structured digital information about building components and project processes, while IoT devices, sensors, reality-capture technologies, and other data sources can provide information about the physical construction environment. These data can be integrated into digital twins to establish connections between physical assets and their digital representations, supporting applications such as construction progress monitoring, quality control, safety management, resource and logistics management, and predictive analysis ^[2][3]. AI and machine learning can be applied to tasks such as cost and schedule prediction, risk assessment, image-based quality inspection, safety monitoring, and decision support ^[4]. Robotics and automated construction systems further extend intelligent construction from digital analysis to physical execution, including automated material handling, robotic assembly, autonomous construction equipment, inspection, and human–robot collaboration ^[5]. The implementation of intelligent construction faces technical, organizational, and workforce-related challenges. Data quality, interoperability, and data integration can affect the reliability and scalability of systems that combine BIM, IoT, digital twins, AI, and robotic technologies ^[2][3]. Differences in data structures and software platforms may hinder information exchange across project participants, while insufficient data quality can reduce the reliability of AI-based analysis and digital-twin applications ^[2][3]. Implementation may also require substantial investment in digital infrastructure, changes to established workflows, and personnel with appropriate digital, analytical, and technical skills ^[1][3]. Therefore, intelligent construction depends not only on the adoption of individual technologies but also on their effective integration with project processes, information management, and human expertise throughout the building life cycle.