Aug 2026· International Journal of Next-Generation Engineering and Technology· Vol 3, pp. 38-44· 0 citations
Abstract
The construction sector is increasingly characterized by complex project environments, heterogeneous data sources, dynamic resource requirements, and growing expectations for operational efficiency and sustainability. Artificial intelligence (AI), scalable computational frameworks, and robotics provide an opportunity to address these challenges through automated decision-making, adaptive resource allocation, predictive monitoring, and digitally integrated construction operations. This research and review article develops a conceptual framework for integrating AI-driven scalability with robotics-enabled construction management, emphasizing the relationship between intelligent computational processes, automated physical execution, and sustainability-oriented decision-making. The study adopts a structured conceptual synthesis methodology based exclusively on the supplied literature. Although the provided references primarily concern biometric identification, feature extraction, system identification, and AI-driven software quality engineering, their methodological principles offer transferable insights into pattern recognition, adaptive systems, system-level automation, and scalable intelligent frameworks. Ramamurthy (2023), in particular, demonstrates the relevance of AI-driven frameworks for automating complex technical processes and improving systematic quality management. The proposed framework conceptualizes construction management as a cyber-physical decision system in which AI interprets project data, scalability mechanisms support computational growth, and robotics converts optimized decisions into physical actions. The analysis indicates that the principal value of AI-robotics integration lies not in automation alone but in establishing an adaptive feedback architecture capable of continuously sensing, evaluating, planning, executing, and learning. The paper further identifies interoperability, scalability, data reliability, human oversight, and contextual adaptability as critical implementation constraints. The resulting framework provides a theoretical foundation for future empirical research into intelligent, sustainable, and digitally integrated construction management systems.
The findings indicate that scalability should be understood not merely as increasing computational capacity but as the ability to expand AI-enabled construction processes without proportionally increasing coordination complexity, training requirements, or operational risk.
Takumi Suzuki, Mio Tanaka· International Journal of Adv...· 0 citations
The construction industry is increasingly dependent on digital sensing, three-dimensional perception, automated decision-making, and intelligent operational control to improve productivity while reducing resource consumption and environmental impacts. Intelligent robotics can provide physical capabilities for inspection, material handling, positioning, and site monitoring, whereas large language models (LLMs) can support interpretation, reasoning, task coordination, and human–machine interaction. However, the integration of these capabilities remains constrained by the reliability of spatial perception, the complexity of construction environments, and the difficulty of translating high-level decisions into executable robotic actions. This research and review paper develops a conceptual framework for integrating intelligent robotics, three-dimensional computer vision, and LLM-based decision support for sustainable construction operations. The methodology synthesizes the provided literature on 3D deep learning, RGB-D semantic segmentation, point-cloud understanding, object detection, and instance segmentation. The analysis indicates that robust 3D perception should constitute the foundational layer of an LLM-enabled construction intelligence architecture. Point-cloud and RGB-D models can provide spatially grounded information, while LLM-based reasoning can transform such information into interpretable operational recommendations. The proposed framework positions the LLM as a decision-support and coordination layer rather than an autonomous source of physical truth. This distinction is important because sustainable construction requires decisions that simultaneously consider operational efficiency, material utilization, safety-related constraints, and environmental performance. The resulting architecture provides a theoretical basis for integrating perception, reasoning, planning, and robotic execution while recognizing limitations associated with data quality, domain transfer, computational requirements, and decision reliability.
Tharindu Jayasinghe, Ishara Wijesinghe· International Journal of Adv...· 0 citations
This review examines recent progress in large AI models for intelligent manufacturing, covering model architectures, adaptation strategies, system integration, and applications across product development, production processes, equipment maintenance, and manufacturing services.
Baotong Chen, Lu Dai, Chuangjian Wang et al.· IEEE Access· 0 citations
The construction industry faces persistent productivity, safety, and coordination problems that cannot be solved by equipment upgrades alone. Under the broader agenda of intelligent construction, construction robots are becoming cyber-physical execution units that connect Building Information Modeling (BIM), sensing, artificial intelligence, and on-site operations. This paper analyzes the technological progress and deployment challenges of construction robots from two interrelated perspectives: autonomous navigation and human-robot collaboration. It argues that multi-sensor perception, BIM-assisted localization, Simultaneous Localization and Mapping (SLAM), improved path planning, and digital-twin-enabled safety monitoring have improved the feasibility of robotic systems in complex construction environments. However, wide deployment is still constrained by unstructured site conditions, model-to-reality discrepancies, fragmented data interfaces, low collaboration maturity, high implementation costs, and uncertain business incentives. The paper proposes four implementation strategies: open communication and data-interface standards, construction-specific safety guidelines for human-robot collaboration, financial and rental mechanisms for lowering the adoption threshold, and joint pilot projects involving industry, universities, research institutions, and end users. The study concludes that construction robots should not be framed as simple substitutes for human labor. Their more realistic role is to augment workers in hazardous, repetitive, precision-demanding, and data-intensive tasks while supporting a gradual transition from experience-based construction management to data-driven intelligent construction.
Yizhuo Li· Applied and Computational En...· 0 citations
The increasing complexity of construction projects has made traditional planning methods inadequate for managing dynamic variables. In this context, the integration of building information modelling (BIM) and artificial intelligence (AI) has been increasingly investigated as a promising approach to improve estimation accuracy, decision-making, and sustainable project execution. This systematic literature review, conducted according to the PRISMA guidelines, analysed 47 articles on BIM-AI integration for construction cost and time planning, categorising them into three clusters: time-oriented, cost-oriented, and multi-objective planning. The reviewed studies indicate that BIM-AI workflows may improve planning efficiency, estimation accuracy, and resource allocation. A limited subset of studies directly incorporated energy consumption, carbon emissions, or lifecycle performance into the optimisation objectives. By contrast, broader benefits related to material waste, rework, equipment idle time, and safety were mainly inferred from improvements in scheduling and resource management rather than directly quantified across the reviewed studies. Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) emerged as the most frequently adopted and consistently reported techniques, while Revit was the most adopted BIM platform. Despite its potential, BIM-AI integration still faces challenges related to software interoperability, data quality, interdisciplinary coordination, and the limited integration of explicit sustainability indicators within optimisation models. Future research should focus on developing standardised and adaptable frameworks that jointly address cost, time, resource efficiency, environmental impact, and lifecycle performance across different construction contexts.
Serena Vitaliano, Stefano Cascone, C. Arcidiacono· Sustainability· 0 citations
Pipeline robots represent intelligent systems engineered for confined and complex pipeline environments. They feature compact and flexible architectures, diverse locomotion modalities, and a high degree of functional integration. These systems have been widely applied in industrial equipment inspection, energy infrastructure maintenance, and the management of urban underground utilities. This review systematically analyzes the technological characteristics and recent advancements in pipeline robotics, with a focus on drive mechanisms and motion control strategies. It presents the evolution and comparative analysis of three primary drive architectures: passive-driven, self-driven, and compound-driven, with representative applications, advantages, and limitations. In addition, an integrated research framework is proposed that encompasses energy efficiency management, drive systems, adaptive mechanisms, perception, navigation, and intelligent adaptive control to address critical challenges in environmental adaptability, energy efficiency, and autonomous task execution. Finally, the review outlines emerging trends and unresolved technical bottlenecks, providing theoretical insights and practical guidance to support ongoing innovation and real-world deployment of pipeline robotic systems.
Cheng Liu, Ke Niu, Chengrong Kuang et al.· Robotica (Cambridge. Print)· 0 citations
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