Jul 2026· International Journal For Multidisciplinary Research· Vol 8· 0 citations· 2 references
Abstract
Abstract
India's highway sector is experiencing a significant digital transformation driven by rapid infrastructure expansion, increasing freight movement and the need for sustainable asset management.¹ Traditional pavement management practices, based on periodic inspections and fragmented engineering records, are no longer adequate for managing complex highway networks efficiently.² The integration of Digital Twins with Building Information Modelling (BIM), intelligent design platforms, advanced pavement evaluation technologies and asset management systems offers a comprehensive framework for data-driven lifecycle management.³⁻⁵
This article examines the evolution of Digital Twin technology for smart pavement management in India. It discusses the integration of BIM, Bentley OpenRoads Designer, Geographic Information Systems (GIS), Falling Weight Deflectometer (FWD), Ground Penetrating Radar (GPR), LiDAR, Unmanned Aerial Vehicles (UAVs), Internet of Things (IoT) sensors and Artificial Intelligence (AI) to support predictive maintenance and optimise lifecycle performance.⁴⁻⁹ The paper also reviews implementation challenges, institutional collaboration, international best practices and future opportunities for adopting Digital Twin technology within Indian highway infrastructure. The proposed framework demonstrates how digital engineering can improve pavement performance, reduce lifecycle costs and contribute to resilient and sustainable transportation networks¹⁰.
Saudi Arabia’s Vision 2030 infrastructure portfolio requires buildings that can be designed, commissioned and operated as high-performing digital assets rather than static civil works. Mechanical, electrical and plumbing systems are central to this requirement because they determine energy demand, indoor environmental quality, water security, fire safety, maintainability and operational cost. This review develops a smart building management systems framework for enhancing MEP performance in Saudi infrastructure projects by synthesising literature published between 2020 and 2025 on building management systems, BIM, IoT, digital twins, energy management, facility management, interoperability and Saudi sustainability policy. The study follows a structured narrative review methodology supported by thematic coding and framework synthesis. The proposed model links BIM-based asset information, sensor networks, building automation, analytics dashboards, commissioning feedback and facility management workflows into a lifecycle performance loop. Findings indicate that the main value of smart building management is not isolated automation, but the integration of design intent, real-time data and operational governance. The review identifies six performance domains: energy and carbon reduction, HVAC stability, electrical load optimisation, water and pumping control, fire and life-safety readiness, and maintainable handover data. It concludes that Vision 2030 projects can improve long-term MEP outcomes when smart building systems are specified early, connected to open information standards, governed through measurable KPIs and embedded into owner-led facility management routines. The paper contributes a Saudi-oriented review framework, two visual models and practical KPI tables for researchers, consultants and infrastructure owners.
Urban water systems are increasingly challenged by climate extremes, aging infrastructure, and rising flood risks. Conventional water management practices remain fragmented across data, operations, and assets, limiting coordinated decision-making and scalable engineering deployment. Digital twins (DT) show great promise to overcome this fragmentation for resilient and efficient management. This review proposes an engineering-practice-oriented framework of digital twins for smart water management (DTSW). Utility demands are first structured through a scenario-oriented decomposition into points of interest (POIs), thereby linking practical engineering problems to digital variables. The review further summarizes a probabilistic graphical model-based scheme as the algorithmic backbone for POI implementation, and examines the key enabling technologies across organized data foundations, models, and real-time control. Particular attention is given to AI-empowered DTSW techniques, including soft sensing and data cleansing, hybrid modeling, and uncertainty-aware model deployment. Future development is discussed from the perspectives of proactive optimization, human-digital collaboration, and scalable engineering deployment. This review thus provides a structured framework for guiding the practical design and deployment of DT in urban water systems, facilitating coordinated, scalable and resilient water management.
Haozheng Wang, Jinkuo Li, Xuhui Dang et al.· Water Research· 0 citations
This framework proves an autonomous decision-making system that organically links inspection data with maintenance regulations by transforming static, manual-labor-centered maintenance workflows into intelligent automated models and increases the efficiency of railway infrastructure management while providing a scalable technical foundation for overall asset management of future smart-city infrastructure.
Minjae Jeon, Yong-Gyun Kim, Seok-Han Kim· Smart Cities· 0 citations
Unmanned aerial vehicle–based infrared thermography (UAV-IRT) has emerged as a promising approach for non-contact infrastructure inspection, enabling thermal diagnostics across large and difficult-to-access built-environment assets. At the same time, the Architecture, Engineering, Construction, and Facility Management (AEC/FM) industry is increasingly adopting digital technologies such as Building Information Modeling (BIM) and digital twins (DTs) to support data-driven asset management and lifecycle decision-making. However, thermal inspection outputs are often treated as isolated datasets rather than interoperable information linked with digital asset models, limiting their value for monitoring and predictive maintenance and constraining their integration into digital asset management workflows. This study presents a review of research at the intersection of UAV-IRT, BIM, and DT technologies. Based on a structured literature screening of 110 publications, this study covers UAV-enabled inspection, thermal data processing, multimodal sensing, and digital model integration. The review synthesizes research across four themes: UAV-IRT applications, thermal data processing and AI-assisted anomaly detection, BIM-based integration approaches, and digital-twin-enabled monitoring frameworks. The analysis reveals fragmentation across sensing, data processing, and integration workflows, highlighting key challenges that hinder scalable deployment in practical infrastructure inspection and management applications. The paper further discusses key directions for future research, including workflow standardization, multimodal sensing integration, AI-enabled decision support, scalable DT ecosystems, and workforce development. The findings demonstrate how UAV-IRT can support asset management by enabling proactive maintenance and improving inspection efficiency by reducing unnecessary inspection cycles and minimizing redundant field operations. These improvements lower resource consumption and operational costs while supporting more effective lifecycle management of infrastructure systems.
Ri Na, Tianjiao Zhao· Innovative Infrastructure So...· 0 citations
Saudi Arabia’s Vision 2030 has created one of the world’s most ambitious infrastructure delivery environments, where tourism, housing, mobility, utilities and public-realm assets must be delivered at speed while preserving quality, safety, environmental performance and long-term asset value. This review paper examines how smart construction project management can support Vision 2030 giga-projects by integrating Building Information Modeling (BIM), risk management and digital scheduling into a unified delivery governance model. The study synthesizes recent literature on BIM-enabled construction management, 4D scheduling, AI-supported schedule control, risk management in sustainable projects, contractual BIM governance, project management standards and the regenerative development agenda represented by Red Sea Global. The paper proposes an integrated framework in which BIM functions as the shared information environment, digital scheduling converts models into time-linked execution logic, risk management governs uncertainty, and project controls translate field data into leadership decisions. The review finds that the strongest value emerges when BIM, Primavera-style critical path scheduling, risk registers, HSE observations, procurement tracking, RFIs, change control and stakeholder reporting are managed as connected data streams rather than isolated administrative tools. For Saudi giga-projects, this integration can reduce design coordination gaps, improve visibility of schedule threats, support proactive risk response, enhance HSE and quality governance, and strengthen alignment with sustainability and local-content goals. The paper contributes a Vision 2030-oriented delivery model for construction leaders managing complex, multi-stakeholder infrastructure programs in Saudi Arabia and comparable rapidly developing economies.
The rapid evolution of digital technologies is fundamentally reshaping the landscape of civil engineering, shifting the industry toward data-driven paradigms. This study provides a critical-thinking synthesis of how remote sensing, Geographic Information System (GIS), and Building Information Modeling (BIM) converge to facilitate enhanced decision-making throughout the infrastructure lifecycle. While traditional methods often suffer from data fragmentation and localized perspectives, this review identifies a significant research gap in the seamless integration of multi-scale spatial data into automated engineering workflows. Through an application-driven framework, this paper evaluates the comparative performance of various sensing platforms, ranging from InSAR-based satellite monitoring for regional deformation to UAV-borne LiDAR for high-precision as-built modeling. Furthermore, it examines the pivotal role of Artificial Intelligence (AI) in bridging the gap between raw data acquisition and actionable intelligence. By critically discussing technical limitations, such as data uncertainty and the “black-box” nature of automated processing, the study highlights the need for a hybrid approach that combines the efficiency of remote sensing with rigorous ground-truthing. The findings underscore that the future of civil engineering lies not in standalone technologies, but in a unified digital ecosystem where GIS serves as the spatial backbone and BIM as the micro-scale digital twin, together enabling more resilient and proactive infrastructure management.
Niswah Selmi Kaffa, A. Kartikasari, Ririn Wuri Rahayu et al.· Jurnal Ilmiah Geomatika· 0 citations