Intelligent construction refers to the integration of digital mtechnodeling, sensor networks, automationlogies, data-driven methods, artificial intelligence, and data analyticautomated systems throughout the building life cycle to generate, manage, and share a computable digital representation of a physical facility that supports design,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, and ope automation, which can be combined to support information integrational, monitoring, analysis, decision-making, and automated or semi-automated construction processes ^[1][2]. TRathe foundationalr than referring to a single technology is building information modeling,, intelligent construction represents an integrated approach in which creates a three-dimensional object-oriented model embedding geometric, material, cost, scheduledigital information and computational technologies are used to improve construction processes, productivity, safety, quality, and performance data,decision-making across the project life cycle ^[1]. BIM anprovid extends it with real-time data from sitees structured digital information about building components and project processes, while IoT devices, sensors, Internet-of-Things devicreality-capture technologies, and terrestrial laser scanning to form aother data sources can provide information about the physical construction environment. These data can be integrated into digital twin that synchronizes the physicals to establish connections between physical assets and their digital representations, supporting applications such as construction process with its virtual model in near real timegress monitoring, quality control, safety management, resource and logistics management, and predictive analysis ^[2][3]. TAI and mache approach supports automatine learning can be applied to tasks such as cost and schedule prediction, risk assessment, image-based quantity takeoff, multi-discipllity inspection, safety monitoring, and decision support ^[4]. Robotics anary clash detection,d automated construction systems further extend intelligent construction progress trackfrom digital analysis to physical execution, including automated material handling, resourcobotic assembly, autonomous construction equipment, inspection, and human–robot collaboration ^[5]. The implementallocation optimtion of intelligent construction faces technical, organizational, and predictive safety monitoringworkforce-related challenges. Data quality, interoperability, and data integration can affect the reliability and scalability of systems that combine BIM, IoT, digital twins, AI, and itrobotic technologies ^[2][3]. Differelinces on standardized open datain data structures and software platforms may hinder information exchange schemas such as the Indusacross project participants, while insufficient data quality can reduce the reliability of AI-based analysis and digital-twin applications ^[2][3]. Implementration may Foundation Classalso require substantial investment in digital infrastructure, changes to enable interoperability acstablished workflows, and personnel with appropriate digital, analytical, and technical skills ^[1][3]. Therefoss different software platforms and among all participatingre, intelligent construction depends not only on the adoption of individual technologies but also on their effective integration with project stakeholdersprocesses, information management, and human expertise throughout the entire built facility lifecycle ^[3]building life cycle.