Optimizing cost, time, and contamination control in cleanroom construction using advanced Bim, digital twin, and ai-driven project management solutions

This systematic review and meta-analysis investigates the evolving role of Building Information Modeling (BIM) in managing multidimensional risks associated with sustainable bridge infrastructure projects. Drawing upon the PRISMA methodology, the study rigorously screened and analyzed a total of 87 peer-reviewed journal articles and conference papers published between 2010 and 2024. The selected literature was sourced from five leading academic databases—Scopus, Web of Science, IEEE Xplore, ScienceDirect, and the ASCE Library—ensuring comprehensive coverage of global research on BIM-based risk management practices. The review focuses on how BIM contributes to the identification, analysis, mitigation, and continuous monitoring of technical, financial, environmental, and operational risks throughout the lifecycle of bridge projects. Findings reveal that BIM’s capabilities extend well beyond 3D visualization, offering robust tools for 4D scheduling, 5D cost estimation, and real-time data integration that collectively enhance proactive decision-making. BIM facilitates early risk detection through clash detection, geospatial analysis, and scenario simulations during the planning and design stages, reducing design inconsistencies and constructability issues. Moreover, the study synthesizes evidence demonstrating how BIM supports cost and schedule risk control by enabling dynamic simulation of construction sequences and integration with live pricing data, resulting in reduced cost overruns and schedule delays. The review also highlights the increasing convergence of BIM with Internet of Things (IoT) technologies, digital twins, and Geographic Information Systems (GIS), enabling real-time monitoring of structural health, predictive maintenance, and environmental risk modeling in operational phases. Lifecycle-oriented risk governance through BIM emerged as a key theme, particularly in studies focused on sustainability assessments, material optimization, and long-term asset performance. Institutional adoption patterns, regulatory influences, and policy frameworks were also examined, revealing that countries with mandated BIM protocols exhibit more advanced integration of BIM into public infrastructure risk management. Despite persistent challenges such as fragmented data standards, skill gaps, and limited interoperability, the majority of the reviewed literature—accumulating over 3,400 citations—converges on the conclusion that BIM is an essential enabler of risk-informed, sustainable bridge engineering.

This study investigated how Building Information Modeling (BIM) and Digital Twin (DT) technologies influenced risk reduction in civil infrastructure projects using a cross-sectional quantitative design. The dataset comprised 168 civil infrastructure projects across highways (36.9%), bridges (24.4%), rail corridors (19.6%), tunnels (10.7%), and water infrastructure (8.3%), delivered through design–bid–build (39.9%), design–build (32.1%), and PPP/alliance procurement (28.0). Digital implementation showed mid-to-high BIM practice (BIM adoption intensity M = 3.74, SD = 0.71; BIM maturity M = 3.58, SD = 0.68) and more dispersed DT practice (DT utilization M = 3.12, SD = 0.89; DT predictive capacity M = 3.26, SD = 0.84), while integration maturity remained moderate (M = 3.41, SD = 0.79). Risk reduction outcomes demonstrated substantial variability (cost risk reduction M = 64.8, SD = 12.5; schedule M = 61.3, SD = 13.9; safety M = 55.7, SD = 15.6; quality M = 59.9, SD = 14.2; operational M = 67.2, SD = 11.8; composite index M = 61.8, SD = 10.7). Correlations were positive across domains, with integration maturity showing the strongest association with the composite risk index (r = 0.61). Reliability and validity were adequate for all constructs (Cronbach’s alpha 0.81–0.89, composite reliability 0.86–0.92, AVE 0.53–0.64), and collinearity remained acceptable (VIF 1.20–2.27). Hierarchical regression indicated that BIM predictors explained the largest incremental variance in cost and schedule risk reduction (ΔR² = 0.18–0.20), DT predictors contributed most to safety and operational risk reduction (ΔR² = 0.15–0.22), and integration maturity added broad multi-risk explanatory power, especially for the composite index (ΔR² = 0.14, β = 0.41, p < .001). Moderation tests showed complexity weakened BIM scheduling effects, whereas data reliability and team digital capability strengthened DT and integration impacts. Overall, BIM stabilized delivery risks, DTs strengthened safety and operational reliability, and BIM–DT integration yielded the most comprehensive multidimensional risk reduction.

Purpose This paper aims to fill the research gap on digital twin technology and its broad applicability during construction by shedding light on its interaction with Building Information Modeling (BIM) from a construction project management perspective. It presented the true potential of the digital twins in the construction phase of the project lifecycle. Design/methodology/approach The paper employed a two-step methodology that included a comprehensive synthesis of the literature on digital twins through the construction management lens and a questionnaire survey to assess the impact of digital twin services brought to light on parallel BIM uses. Findings The paper provides validated applications and many advantages of the digital twin on construction project management. It suggests that the industry should take advantage of 10 digital twin services introduced to eliminate the low efficiency and lack of productivity that the construction industry is still facing. Research limitations/implications The paper is one of the rare and pioneering studies that addresses the interaction between the digital twin and BIM from a construction management perspective with a quantitative approach examining the reflection of academic publications on the industry and their reception among industry professionals. Practical implications The paper provides a meaningful definition for the industry by grounding the concept of digital twin in existing technologies and their practical applications. This provides a roadmap for managers to approach the problems and BIM limitations they need to overcome in their companies or projects with tailor-made solutions. Originality/value The paper is one of the pioneering quantitative studies that fulfills an identified need to investigate digital twin technology for construction management and its extensive applicability for quickly evolving construction sites.

The integration of AI with Building Information Modeling (BIM) and Digital Twin technology is reshaping the architectural landscape, bridging the gap between cutting-edge construction practices and the preservation of cultural heritage. This article explores how AI-driven BIM streamlines construction through predictive maintenance, cost optimization, and real-time project adjustments, enabling the creation of efficient, resilient, and sustainable structures. Simultaneously, digital twin technology, powered by AI, offers new avenues for historic preservation by producing data-rich, 3D models that allow for precise monitoring and virtual restoration of heritage sites. By examining real-world applications in both modern buildings and historic landmarks, this article demonstrates how AI’s role in architecture not only advances building practices but also safeguards architectural history for future generations.

A digital twin (DT) system is a virtual system that can provide a comprehensive description of a real physical system. The DT system continuously receives data from physical sensors and user input information and provides information feedback to the physical system. It is an emerging technology that utilizes an advanced Internet of Things (IoT) to connect different objects, which is in high demand in various industries and its research literature is growing exponentially. Traditional physical systems provide data support for the monitoring of physical objects such as buildings through digital modeling techniques, data acquisition tools, human computer interfaces, and building information models (BIM). However, DT can offer much more than data presentation. DT uses the received data to perform operations such as analysis, prediction, and simulation, and finally transmits the analysis results to the physical system as feedback. Compared with other physical systems, DT has the characteristics of bidirectional data exchange and real-time autonomous management. The plant factory control system based on digital twin technology continuously measures the power consumption of electrical equipment through the sensors of the physical system and makes the corresponding virtual color-coded gradient map based on the obtained data. The darker the virtual device is, the more power it currently requires, and just based on the shade of color gives the user a very intuitive idea of the current power usage of the electronic device. There has been extensive research on digital twin technology, but there are few studies on implementing plant factories based on digital twin technology. This paper proposes the idea of combining digital twin technology with plant factories to provide research directions for future smart agriculture. It proves that smart agricultural production with sustainability can also benefit from this idea.

Digital Twin technology is transforming how buildings are designed, operated, and optimized, serving as a key enabler of smarter, more energy-efficient, and sustainable built environments. By creating dynamic, data-driven virtual replicas of physical assets, Digital Twins support continuous monitoring, predictive maintenance, and performance optimization across a building’s lifecycle. This paper provides a structured review of current developments and future trends in Digital Twin applications within the building sector, particularly highlighting their contribution to decarbonization, operational efficiency, and performance enhancement. The analysis identifies major challenges, including data accessibility, interoperability among heterogeneous systems, scalability limitations, and cybersecurity concerns. It emphasizes the need for standardized protocols and open data frameworks to ensure seamless integration across Building Management Systems (BMSs), Building Information Models (BIMs), and sensor networks. The paper also discusses policy and regulatory aspects, noting how harmonized standards and targeted incentives can accelerate adoption, particularly in retrofit and renovation projects. Emerging directions include Artificial Intelligence integration for autonomous optimization, alignment with circular economy principles, and coupling with smart grid infrastructures. Overall, realizing the full potential of Digital Twins requires coordinated collaboration among researchers, industry, and policymakers to enhance building performance and advance global decarbonization and urban resilience goals.

• Examined various battery types, BMS architectures, and modelling techniques. • Explored deterministic, non-deterministic, and hybrid SOC estimation methods. • Discussed SOH estimation and RUL prediction using various methods. • Exploration of digital twin technologies and battery digital twins (BDTs) for enhancing BMS functionality. • Discussion of future research directions in battery state estimation. Accurate state estimation is fundamental for the safety, reliability, and performance of electric vehicle (EV) batteries. This review highlights the critical need for precise evaluation of key battery states such as state of charge (SOC), state of health (SOH), and remaining useful life (RUL). It surveys the existing modelling and estimation techniques used in lithium-ion battery (LIB) systems. It explores parameter identification methods and the challenges associated with real-time monitoring and predictive diagnostics in dynamic EV environments. The paper further investigates the transformative role of digital twin (DT) technologies in addressing these challenges. By integrating battery digital twins technologies(BDTs): Internet of Things (IoT), cloud computing, artificial intelligence (AI), and extended reality (XR), it offers a virtual replica of the battery system. That enables continuous monitoring, predictive maintenance, and performance optimization. The review delves into the architecture, functions, challenges and future perspectives of battery digital twins (BDTs). And it emphasizes their potential to enhance traditional battery management systems (BMS) through intelligent, adaptive control. Through comparative analysis and identification of research gaps, this review provides a roadmap for future advancements in state estimation and digital twin integration.

The importance of technical disclosure in construction of project is described and the necessity of application of BIM (Building Information Modeling) technology used in technical disclosure is analyzed correspondingly. Many actual cases of technical disclosure in construction used BIM technology in our company with its application are discussed subsequently. The results show that the BIM technology can simplify the procedures of technical disclosure on the construction phase, and multi-software work together based on BIM provides a guidance for different types of projects. Many advantages of BIM such as construction simulation, 3D roaming can improve the quality of technical disclosure. Finally, recommendations and requirements for the specific implementation of BIM on the construction phase are proposed: the BIM model for technical disclosure is the most unique model in the entire life cycle; the locally refined extent is much more than the final completion model, which should be paid more attention to in the whole process of program development when BIM implemented.

The idea of a smart city has evolved in recent years from limiting the city’s physical growth to a comprehensive idea that includes physical, social, information, and knowledge infrastructure. As of right now, many studies indicate the potential advantages of smart cities in the fields of education, transportation, and entertainment to achieve more sustainability, efficiency, optimization, collaboration, and creativity. So, it is necessary to survey some technical knowledge and technology to establish the smart city and digitize its services. Traffic and transportation management, together with other subsystems, is one of the key components of creating a smart city. We specify this research by exploring digital twin (DT) technologies and 3D model information in the context of traffic management as well as the need to acquire them in the modern world. Despite the abundance of research in this field, the majority of them concentrate on the technical aspects of its design in diverse sectors. More details are required on the application of DTs in the creation of intelligent transportation systems. Results from the literature indicate that implementing the Internet of Things (IoT) to the scope of traffic addresses the traffic management issues in densely populated cities and somewhat affects the air pollution reduction caused by transportation systems. Leading countries are moving towards integrated systems and platforms using Building Information Modelling (BIM), IoT, and Spatial Data Infrastructure (SDI) to make cities smarter. There has been limited research on the application of digital twin technology in traffic control. One reason for this could be the complexity of the traffic system, which involves multiple variables and interactions between different components. Developing an accurate digital twin model for traffic control would require a significant amount of data collection and analysis, as well as advanced modeling techniques to account for the dynamic nature of traffic flow. We explore the requirements for the implementation of the digital twin in the traffic control industry and a proper architecture based on 6 main layers is investigated for the deployment of this system. In addition, an emphasis on the particular function of DT in simulating high traffic flow, keeping track of accidents, and choosing the optimal path for vehicles has been reviewed. Furthermore, incorporating user-generated content and volunteered geographic information (VGI), considering the idea of the human as a sensor, together with IoT can be a future direction to provide a more accurate and up-to-date representation of the physical environment, especially for traffic control, according to the literature review. The results show there are some limitations in digital twins for traffic control. The current digital twins are only a 3D representation of the real world. The difficulty of synchronizing real and virtual world information is another challenge. Eventually, in order to employ this technology as effectively as feasible in urban management, the researchers must address these drawbacks.

Spatial process is one of the basic movement forms of ecological environment, social economy and geographical system. Spatial analysis refers to a series of theories and techniques for analyzing, simulating, predicting and regulating spatial processes. With the support of BIM (building information modelling) technology, it can realize the real-time sharing of information, effectively reducing the shortcomings of traditional construction management process. Thus, the cooperation among different departments and specialties in the process of spatial analysis of geographic information is realized. The information integration of BIM technology mainly reflects the integration of the design process and the integration of design information. There is information when there are differences, and information is a reproduction of differences. Therefore, regional differences will inevitably lead to the generation of geospatial information flow. The powerful function of BIM technology can realize the simulation processing of geographic information analysis. BIM technology has changed the way of information transmission in the life cycle of construction projects, and effectively improved the connection efficiency between design and management. This paper studies the construction method of geographic information spatial analysis system based on BIM technology.

This paper presents an autonomous drilling advisory system powered by digital twins and AI solutions. Such an advisory system aims to automate real-time monitoring and parameter optimization, reduce subject-matter experts, and meet the demands for safer and more efficient drilling toward autonomous operation. The methodology proposed in this research involves the creation of a comprehensive Digital Twin model that accurately replicates the drilling process by integrating hydraulic, thermal dynamic, and mechanical models. To ensure high model accuracy, an auto-calibration approach is developed, driven by real-time data, to fine-tune the Digital Twin models. Additionally, AI-based model reasoning techniques are employed to detect potential hazards and risks ahead of the bit proactively. This is achieved by comparing the ideal behavior of the digital twin replica with the actual behavior observed from downhole and the rig. As a result, real-time diagnostics are generated to supervise ongoing operations, accompanied by suggestions to mitigate identified risks. Furthermore, the system leverages the capabilities of the Digital Twin and optimization methods to create multiple combinations of operational parameters. These parameters are optimized by ranking the predicted performance derived from the Digital Twin. The optimized operational parameters are automatically generated as forward advice to drillers, enabling them to make informed decisions and enhance drilling performance. Testing results on multiple wells from different operators are presented, showcasing the system's capabilities in real-time monitoring and drilling parameter optimization. The system demonstrates its effectiveness in providing diagnostic messages with early anomaly detection during drilling and casing running. These diagnostic warnings include losses, leakage, poor hole cleaning, and stuck pipe, enabling proactive intervention to mitigate risks. Furthermore, the system optimizes operational parameters during drilling and tripping in real-time without requiring human intervention. This optimization covers parameters such as flow rate, rotary speed (RPM), and rate of penetration (ROP) during drilling, and tripping speed during tripping in and pulling out of the hole. The time savings achieved through the use of optimized parameters are quantified for both cases, demonstrating a substantial improvement in operational efficiency while maintaining safety margins. The scalability and adaptability of the system are also highlighted, emphasizing its ability to accommodate diverse drilling scenarios and integrate with existing solutions in various deployment conditions. The proposed methodology demonstrates the development of a robust and efficient system that enhances decision-making and improves drilling performance. In addition, the results highlight the potential benefits of combining AI and Digital Twin technologies in the drilling industry, paving the way for future innovations and advancements in the field.

Digital Twin (DT) technology is a pivotal innovation within the built environment industry, facilitating digital transformation through advanced data integration and analytics. DTs have demonstrated significant benefits in building design, construction, and asset management, including optimising lifecycle energy use, enhancing operational efficiency, enabling predictive maintenance, and improving user adaptability. By integrating real-time data from IoT sensors with advanced analytics, DTs provide dynamic and actionable insights for better decision-making and resource management. Despite these promising benefits, several challenges impede the widespread adoption of DT technology, such as technological integration, data consistency, organisational adaptation, and cybersecurity concerns. Addressing these challenges requires interdisciplinary collaboration, standardisation of data formats, and the development of universal design and development platforms for DTs. This paper provides a comprehensive review of DT definitions, applications, capabilities, and challenges within the Architecture, Engineering, and Construction (AEC) industries. This paper provides important insights for researchers and professionals, helping them gain a more comprehensive and detailed view of DT. The findings also demonstrate the significant impact that DTs can have on this sector, contributing to advancing DT implementations and promoting sustainable and efficient building management practices. Ultimately, DT technology is set to revolutionise the AEC industries by enabling autonomous, data-driven decision-making and optimising building operations for enhanced productivity and performance.

Abstract. Technology has advanced and progressed tremendously, and the term city is being elevated to a new level where the smart city has been introduced globally. Recent developments in the concept of smart city have led to a renewed interest in Digital Twin. Using precise Building Information Modelling (BIM) consolidated with big data and sensors, several attempts have been made to establish digital twin smart cities. In recent years, several researchers have sought to determine the capability of smart city and digital twin for various taxonomies such as development and urban planning purposes, built environment, manufacturing, environmental, disaster management, and healthcare. Despite being beneficial in many disciplines, especially in manufacturing, built environment, and urban planning, these existing studies have shown a lack of aspect in terms of emergency or disaster-related as opposed to the elements mentioned above. This is because the researcher has not treated emergencies or disasters in much detail. Therefore, an extensive review on smart city, digital twin, BIM and disaster management and technology that revolves around these terms were summarised. In general, 39 articles from prominent multidisciplinary databases were retrieved over the last two decades based on the suggested PRISMA workflow. These final articles were analysed and categorised into four themes based on the research content, gist, and keywords. Based on the review of 39 articles related to smart city, digital twin and BIM, a workflow for the smart city digital twin and the conceptual framework for indoor disaster management was proposed accordingly. The establishment of smart city digital twins solely for an indoor emergency can be beneficial to urbanites, and it could provide numerous benefits for enhanced situation assessment, decision making, coordination, and resource allocation.

The present study uses a bibliometric and systematic literature review (SLR) to examine the use of Building Information Modeling (BIM), the Internet of Things (IoT), and Digital Twins (DT) in the construction industry. The network visualization and other approaches based on the Web of Science (WOS) database and the patterns of research interactions were explored in 1879 academic publications using co-occurrence and co-citation investigations. Significant publications, conferences, influential authors, countries, organizations, and funding agencies have been recognized. Our study demonstrates that BIM, IoT, and DT in construction, Heritage BIM (HBIM), Smart Contracts, BIM, and Ontology, and VR and AR in BIM and DT are the main study themes. Finally, several prospective areas for future study are identified, including BIM and Metaverse technology, BIM and Artificial Intelligence (AI), Metaheuristic algorithms for optimization purposes in BIM, and the Circular Economy with BIM and IoT.

The Halal supply chain, which encompasses the sectors of food, medicines, cosmetics, has enormous challenges to comply with Islamic rules, maintain product traceability and combating fraud. Given the increasing global demand for Halal products, the demand for technology innovations to enhance supply chain transparency and ensure Halal compliance is growing. This paper explores the application of Digital Twin (DT) technology in Halal supply chain management, highlighting its ability to address these challenges. The main research question of this paper is as follows: How can Digital Twin technology facilitate transparency, traceability, and compliance in the Halal supply chains? The current study employs a qualitative methodology through a review of the extant literature and reviews of industrial case studies. The study reveals that the Digital Twin technology promises to considerably improve the operational excellence of Halal supply chains through real-time Halal status monitoring ensuring that products comply with religious and ethical requirements along the supply chain. The main applications include tracking Halal certification, preventing cross-contamination, and enhancing transparency for certification bodies, manufacturers, and consumers. The results indicate that Digital Twin technology has the potential to transform Halal supply chains by reducing fraud, improving operational efficiencies, and building consumer trust. As a comprehensive Halal compliance solution, Digital Twin can enable a more transparent and trustworthy Halal certification process on a global scale. The study extends the knowledge body on technology innovations in supply chain management especially with religious and ethical product certifications and sets the stage for future research in the area.