Digital Twin Technology for Bridge Maintenance using 3D Laser Scanning: A Review

There has been a significant surge in the interest in adopting cutting-edge new technologies in the civil engineering industry in recent times that monitor the Internet of Things (IoT) data and control automation systems. By combining the real and digital worlds, digital technologies, such as Digital Twin, provide a high-level depiction of bridges and their assets. The inspection, evaluation, and management of infrastructure have experienced profound changes in technological advancement over the last decade. Technologies like laser scanners have emerged as a viable replacement for labor-intensive, costly, and dangerous traditional methods that risk health and safety. The new maintenance techniques have increased their use in the construction section, particularly regarding bridges. This review paper aims to present a comprehensive and state-of-the-art review upon using laser scanners in bridge maintenance and engineering and looking deeper into the study field in focus and researchers’ suggestions in this field. Moreover, the review was conducted to gather, evaluate, and analyze the papers collected in the years from 2017 to 2022. The interaction of research networks, dominant subfields, the co-occurrence of keywords, and countries were all examined. Four main categories were presented, namely machine learning, bridge management system (BMS), bridge information modeling (BrIM), and 3D modeling. The findings demonstrate that information standardization is the first significant obstacle to be addressed before the construction sector can benefit from the usage of Digital Twin. As a result, this article proposes a conceptual framework for building management using Digital Twins as a starting point for future research.publishedVersio

Experimental and finite element analysis of the shear behaviour of UHPC beams

Master's Thesis Civil and Constructional Engineering BYG508 - University of Agder 2019The flexural behavior of reinforced concrete beams isobviously defined and can be managed with reasonableaccuracy.However, a solution has not been obtained for the shear capacity of beams, especially those without shear reinforcement, though numerous models have been established using different approaches. The reason is due to the complexity of shear behavior of reinforced concretebeams,where the load transfer through various componentsof concrete. In addition to this, there is also the effect of reinforcement and cross-section of the memberswhichis linked with dowel action and geometric parameters.All these aspects cause a challenge in quantifying the contribution of each parameter towards shear strength. The uncertainties of these parameters are the reason for not having a principal shear model inthe measurement of the shear capacity of reinforced or un-reinforced concrete beams.This master thesis hasthereforefocused on enhancing the shear resistance of reinforced concrete beams,among a suitable fibre dosage,and the use of UHPC. Experiments, as well as numerical analyses, have been conducted in this thesis. The experiments were divided into 3 parts: cubic and cylinder specimens at different ages to determine the compressive strength,as well as the modulus of elasticity, afour-point bending test on beams to investigate shear strength, and lastly, a three-point bending test on small-scale prismsto determinethe flexural tensile strength.In order to reach a deeper understanding of the shear behavior, finite element (FE) analyses were implemented utilizing the computer software ANSYS. Through ANSYS, several sets of analyses were completed on the simulation offour-point beam bending tests.The large-scale beams were all tested at the mechatronic laboratory at the University of Agder,usingthe four-point bending test. The midspan deflection was measured based on the available machines and a computer was used to register the values.The digital image correlation technique was used to extract the load-deflection curve of several points near to the diagonal shear crack. The experimental results confirm that using the fibrein UHPC beams,will increase the shear strength and the ductility. Replacing stirrups completely with fibres, leads to a reduction of beam depth as well as a decrease in stirrup assembly time.The results were compared with the estimations by Australian guideline, ACI 522, Sharma, Ashour et al., Narayana et al. and Imam et al. The results show that Ashour et al. and Narayana et al. formulas gave the most accurateprediction, while the formula proposed by Sharma wasthe the least accurate.Most of the Finite element modelingresultscorrelated well with ourexperimental results. Hence, using ANSYS may be therightsolution in the future to investigate UHPC beams and to develop design theories of UHPFRC

Numerical Analysis of Bearing Capacity of a Ring Footing on Geogrid Reinforced Sand

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Multiobjective optimization of building energy consumption and thermal comfort based on integrated BIM framework with machine learning-NSGA II

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Improving building occupant comfort through a digital twin approach:A Bayesian network model and predictive maintenance method

This study introduces a Bayesian network model to evaluate the comfort levels of occupants of two non-residential Norwegian buildings based on data collected from satisfaction surveys and building performance parameters. A Digital Twin approach is proposed to integrate building information modeling (BIM) with real-time sensor data, occupant feedback, and a probabilistic model of occupant comfort to detect and predict HVAC issues that may impact comfort. The study also uses 200000 points as historical data of various sensors to understand the previous building systems’ behavior. The study also presents new methods for using BIM as a visualization platform and for predictive maintenance to identify and address problems in the HVAC system. For predictive maintenance, nine machine learning algorithms were evaluated using metrics such as ROC, accuracy, F1-score, precision, and recall, where Extreme Gradient Boosting (XGB) was the best algorithm for prediction. XGB is on average 2.5% more accurate than Multi-Layer Perceptron (MLP), and up to 5% more accurate than the other models. Random Forest is around 96% faster than XGBoost while being relatively easier to implement. The paper introduces a novel method that utilizes several standards to determine the remaining useful life of HVAC, leading to a potential increase in its lifetime by at least 10% and resulting in significant cost savings. The result shows that the most important factors that affect occupant comfort are poor air quality, lack of natural light, and uncomfortable temperature. To address the challenge of applying these methods to a wide range of buildings, the study proposes a framework using ontology graphs to integrate data from different systems, including FM, CMMS, BMS, and BIM. This study’s results provide insight into the factors that influence occupant comfort, help to expedite identifying equipment malfunctions and point towards potential solutions, leading to more sustainable and energy-efficient buildings.publishedVersio

A review of the Digital Twin technology for fault detection in buildings

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A Review of the Digital Twin Technology in the AEC-FM Industry

The Architecture, Engineering, Construction, and Facility Management (AEC-FM) industry is increasingly affected by digital technologies that monitor sensor network data and control automation systems. Advances in digital technologies like Digital Twin offer a high-level representation of buildings and their assets by integrating the physical and digital world. This paper examines patterns, gaps, and trends in the AEC-FM sector and contributes to digitalization and automation solutions for building management. This work covers a broad range of research topics, from intelligent information management of complex models to building information management and the interaction of building systems, where researchers are increasingly interested in using the Digital Twin to manage their information and in developing new research lines focused on data interchange and the interoperability of building information modeling (BIM) and facility management (FM). After a complete bibliometric search of several databases and following selection criteria, 77 academic publications about the Digital Twin application in the AEC-FM industry were labeled and clustered accordingly. This study analyzed in detail the concept of key technologies, including “Digital Twin in Facility Lifecycle Management,” “Digital Twin-Information Integration Standards,” “Digital Twin-Based Occupants Centric Building Design,” “Digital Twin-Based Predictive Maintenance,” “Semantic Digital Twin for Facility Maintenance,” and “Digital Twin-Based Human Knowledge.” The findings show that information standardization is the first major hurdle that must be overcome before the actual use of Digital Twin can be realized in the AEC-FM industry. Based on that, this paper provides a conceptual framework of Digital Twin for building management as a starting point for future research.publishedVersio

A Comprehensive Review and Analysis of Nanosensors for Structural Health Monitoring in Bridge Maintenance: Innovations, Challenges, and Future Perspectives

This paper presents a thorough review and detailed analysis of nanosensors for structural health monitoring (SHM) in the context of bridge maintenance. With rapid advancements in nanotechnology, nanosensors have emerged as promising tools for detecting and assessing the structural integrity of bridges. The objective of this review is to provide a comprehensive understanding of the various types of nanosensors utilized in bridge maintenance, their operating principles, fabrication techniques, and integration strategies. Furthermore, this paper explores the challenges associated with nanosensor deployment, such as signal processing, power supply, and data interpretation. Finally, the review concludes with an outlook on future developments in the field of nanosensors for SHM in bridge maintenance.publishedVersio

Digital Twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian buildings

Numerous buildings fall short of expectations regarding occupant satisfaction, sustainability, or energy efficiency. In this paper, the performance of buildings in terms of occupant comfort is evaluated using a probabilistic model based on Bayesian networks (BNs). The BN model is founded on an in-depth anal- ysis of satisfaction survey responses and a thorough study of building performance parameters. This study also presents a user-friendly visualization compatible with BIM to simplify data collecting in two case studies from Norway with data from 2019 to 2022. This paper proposes a novel Digital Twin approach for incorporating building information modeling (BIM) with real-time sensor data, occupants’ feedback, a probabilistic model of occupants’ comfort, and HVAC faults detection and prediction that may affect occupants’ comfort. New methods for using BIM as a visualization platform, as well as a pre- dictive maintenance method to detect and anticipate problems in the HVAC system, are also presented. These methods will help decision-makers improve the occupants’ comfort conditions in buildings. However, due to the intricate interaction between numerous equipment and the absence of data integra- tion among FM systems, CMMS, BMS, and BIM data are integrated in this paper into a framework utilizing ontology graphs to generalize the Digital Twin framework so it can be applied to many buildings. The results of this study can aid decision-makers in the facility management sector by offering insight into the aspects that influence occupant comfort, speeding up the process of identifying equipment malfunc- tions, and pointing toward possible solutions.Digital Twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian buildingspublishedVersionPaid open acces