Digital-Twin-Based Structural Health Monitoring of Dikes

Recent structural health monitoring (SHM) strategies in civil engineering increasingly leverage digital twins, which digitally represent the structures being monitored as well as the SHM systems installed on the structures. Despite the widespread adoption of digital twin applications in recent years in SHM, there is a lack of agreement on a common definition of digital twins. Furthermore, there is no consensus on digital twin architectures and on the internal elements that constitute digital twins. A common digital twin definition would advance digital twin implementations and operability, and insights into digital twin architectures and digital twin elements would be vital to enhance the reliability and performance of digital-twin-based SHM systems. A significant number of digital twin definitions have been proposed and a plethora of reviews have been published; however, little emphasis has been given to digital twin architectures and internal elements. This paper presents a multivocal review of digital twins in civil engineering, aiming to provide a panorama of the digital twin landscape in civil engineering with explicit insights into the architectures and internal elements used in digital twin applications. From a methodological standpoint, the review follows a twofold approach that encompasses (i) peer-reviewed, indexed literature (“white literature”) as well as (ii) non-indexed sources (“gray literature”) that include industrial digital twin applications. Besides the multivocal review, a generic digital twin reference architecture is drawn from the review results and a digital twin definition is formulated both in an informal and formal (i.e., mathematical) manner. It is expected that the generic reference architecture and the definitions proposed in this study may serve as a blueprint for digital-twin-based SHM applications, with significant implications for researchers, practitioners, and policymakers in structural health monitoring.

Bridges are frequently subjected to permit loads. While deciding on permitting such loads, bridge owners usually adopt a tiered approach for structural analysis, assessment, and measurement. Under these complexities of decision-making, the owner can decide to adopt structural health monitoring (SHM) strategies in guiding the issuance of permits. A value of information (VoI) framework can be utilised by the owner to estimate the benefit of various SHM strategies. This study proposes a novel VoI framework which incorporates tiered assessments common in engineering practice. The proposed decision framework utilizes a generic approach to incorporate the successive tiers of measurement, analysis, and assessment. A real-world inspired case study of a reinforced concrete bridge pier crosshead subjected to high shear is used to demonstrate the proposed framework. Using a novel and practical tiered-assessment and multi-intervention option strategy, the potential monetary benefit of strain-based SHM strategies is quantified. It is found that the potential benefit of SHM is particularly high when high risks are involved. SHM is also found to be highly beneficial when slight changes in structural assessment could trigger different intervention actions by the stakeholder. The study also identifies the significant role that low-cost low-accuracy SHM strategies can play in decision guidance by providing adequate information for decision-making at a cheaper cost.

While the concept of BIM encourages the use of digital semantic models for communication and decision making across the entire lifecycle of assets, in the current practice, the use of BIM is predominantly limited to the design phase. The major issue with the use of BIM in the post-design phases is mainly the integration of non-design data (i.e., safety, productivity, and structural health, etc.) in the model, because in the current process this is done manually and therefore it is time-consuming and error prone. To address this gap, the concept of Digital Twin (DT) has emerged in recent years. In DT concept, cyber-physical system theory is utilized to use a wide array of sensory data to collect condition data about an existing asset and then integrate this data into the digital model. While a few pilot projects indicate the potentials of DT, the major limitation is that the current scope of DT is limited to operation and maintenance phase. This research argues that the DT concept can be extended to the entire lifecycle of the asset by trying to incorporate relevant sensory and non-sensory data into the digital model in an automated and systematic way. However, in the current literature there is no clear insight about such a holistic and life-cycle DT concept for infrastructure projects. Especially, there is very little understanding about how various sensory and non-sensory data from construction and operation phases can be seamlessly integrated into the 3D BIM models. Therefore, this research aims to develop a conceptual model for the architecture of Lifecycle DT (LDT) focusing on bridges. To this end, an ontological modeling approach was adopted to develop an overview of bridge LDT. Since this conceptual model would provide an insight into how to make conventional BIM models LDT-ready, it can be used as the basis for the transition towards the implementation of LDT for bridges. The developed conceptual model was validated through a set of interviews with experts. The findings of the research indicate a set of lifecycle information needs that the model should be equipped with, to cover the needs of different disciplines. These information pieces can be represented by a set of required fields in the BIM model. This way the sensory data are pre-allocated in the model at its early creation upgrading it into DT-ready. Apart from the sensory data a set of interlinked data pieces was identified among different databases of different disciplines. It is proposed that the LDT-ready BIM model serves also for linking these databases to enable a seamless information flow from one lifecycle phase to another. The allocation of all these data pieces occurred in an ontological model representing the data structure of the LDT-ready BIM model, as well as the relationships between different entities of the model. This ontological model offers an insight of a lifecycle modeling practice as well as an automated data incorporation in the model, confronting respective gaps in literature. Overall, the proposed solution enables a smooth transition towards an upgraded and more automated modeling practice as indicated by the concept of DT.

An intelligent BIM-enabled digital twin framework for real-time structural health monitoring using wireless IoT sensing, digital signal processing, and structural analysis

Structural identification using dynamical parameters (such as the natural vibration frequencies and mode shapes) is an important issue, especially in bridges or high-rise buildings. However, incorrect decisions could happen on the Structural Health Monitoring (SHM) strategy and the Structural System Identification (SSI) analysis that makes the sometimes expensive and time-consuming process useless due to the large uncertainty of the resulting estimations. This paper discusses the role of the SHM strategy and the SSI analysis based on the constrained observability method (COM) and decision trees (DT) in reducing the estimation error. Here, the COM uses subsets of natural frequencies and/or modal-shapes to deal with the nonlinearity of the SSI derived from the operational aspects of the methods, and combines the unknown items including frequencies and mode shapes into an optimization process. Next, a decision-support tool based on decision trees is applied to help engineers to establish the best SHM + SSI strategy yielding the most accurate estimations. The principle and steps of this new method, the combination of constrained observability m,ethod and decision trees, are presented for the first time. After that, a numerical model of a bridge case is used to show how to choose the optimal strategy, when factors such as the structure layout, span length, measurement set, and parameters of the COM are included as decision variables. The importance ranking of these four factors is the layout, measurement set, parameters of the COM, and length through the sensitivity analysis of the COM estimated. Last, a real bridge is used to validate this methodology under the undamaged and damaged scenarios by comparing an Error Index, which shows the optimal SHM + SSI strategy works well no matter the bridge is damaged or not. The presented analysis leads to significant insights that can help the decision-making of the optimal SHM + SSI strategy, avoiding erroneous decisions if this tool is not used beforehand.

To achieve real-time structural health monitoring (SHM), a concept of digital twin - a digital copy of a structure has been brought up and investigated. It provides an up-to-date virtual model of structures, with the integration of physical as well as data information. The goal of this research is to provide faster and more accurate procedures to capture the spatial information required by a digital twin of a concrete structure using 3D point cloud data. Given that the method is intended for real-scale structures, such as bridges, the work can be divided to 3 steps: (1) to segment and extract geometric information for structural components; (2) to convert the geometry information to FE mesh with consideration of element types; (3) to assign material property as well as boundary conditions based on extracted components type. Linear FE analyses have been carried out to evaluate the structural performance based on the FE model created from the point cloud. The automation of such a process is an essential part of the creation of a digital twin of infrastructures.

The widespread adoption of Building Information Modeling (BIM) and the recent emergence of Internet of Things (IoT) applications offer several new insights and decision-making capabilities throughout the life cycle of the built environment. In recent years, the ability of real-time connectivity to online sensors deployed in an environment has led to the emergence of the concept of the Digital Twin of the built environment. Digital Twins aim to achieve synchronization of the real world with a virtual platform for seamless management and control of the construction process, facility management, environment monitoring, and other life cycle processes in the built environment. However, research in Digital Twins for the built environment is still in its nascent stages and there is a need to understand the advances in the underlying enabling technologies and establish a convergent context for ongoing and future research. This paper conducted a systematic review to identify the development of the emerging technologies facilitating the evolution of BIM to Digital Twins in built environment applications. A total of 100 related papers including 23 review papers were selected and reviewed. In order to systematically classify the reviewed studies, the authors developed a five-level ladder categorization system based on the building life cycle to reflect the current state-of-the-art in Digital Twin applications. In each level of this taxonomy, applications were further categorized based on their research domains (e.g., construction process, building energy performance, indoor environment monitoring). In addition, the current state-of-art in technologies enabling Digital Twins was also summarized from the reviewed literature. It was found that most of the prior studies conducted thus far have not fully exploited or realized the envisioned concept of the Digital Twin, and thus classify under the earlier ladder categories. Based on the analysis of the reviewed work and the trends in ongoing research, the authors propose a concept of an advanced Digital Twin for building management as a baseline for further studies.

The integration of Digital Twin technology with Physics based models and Scientific Machine Learning (SciML) offers a transformative approach for Nondestructive Evolution (NDE) and Structural Health Monitoring (SHM). Detection and prediction of crack evolution under local stress through wave propagation demands physics-based understanding of crack-wave interaction. Traditional computational methods struggle to simultaneously capture crack growth dynamics and guided wave interactions due to the complexities of remeshing and high computational costs. The study demonstrates the simultaneous simulation of crack propagation and guided wave interactions without requiring mesh updates. Further, by leveraging physics-informed neural networks (PINNs) under SciML approaches, a Digital Twin framework can bridge this gap, providing real-time, data-driven insights into Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE). This study presents a Digital Twin Deep Link framework that coupled physics-based models with SciML to accurately predict crack initiation, growth patterns, and guided wave interactions in stiffened structures. Case studies illustrate how physics-informed AI enables the identification of crack signatures in sensor data, providing a robust mechanism for defect detection and material state assessment. The results highlight the potential of SciML-powered Digital Twins in SHM and NDE, paving the way for AI-driven diagnostics and autonomous monitoring systems.

A digital twin-based framework for damage detection of a floating wind turbine structure under various loading conditions based on deep learning approach

The concept of digital twins (DT)s enhances traditional structural health monitoring (SHM) by integrating real-time data with digital models for predictive maintenance and decision-making whilst combined with finite element modelling (FEM). However, the computational demand of FE modelling necessitates surrogate models for real-time performance, alongside the requirement of inverse structural analysis to infer overall behaviour via the measured structural response of a structure. A FEM-based machine learning (ML) model is an ideal option in this context, as it can be trained to perform those calculations instantly based on FE-based training data. However, the performance of the surrogate model depends on the ML model architecture. In this light, the current study investigates three distinct ML models to surrogate FE modelling for DTs. It was identified that all models demonstrated a strong performance, with the tree-based models outperforming the performance of the neural network (NN) model. The highest accuracy of the surrogate model was identified in the random forest (RF) model with an error of 0.000350, whilst the lowest inference time was observed with the trained XGBoost algorithm, which was at approximately 1 millisecond. By leveraging the capabilities of ML, FEM, and DTs, this study presents an ideal solution for implementing real-time DTs to advance the functionalities of current SHM systems.

The digital twin (DT) concept has a key role in the future of the smart manufacturing industry. This review paper aims to investigate the development of the digital twin concept, its maturity and its vital role in the fourth industrial revolution. Having identified its potential functionalities for the digitalisation of the manufacturing industry, the digital twin concept, its origin and perspectives from both the academic and industrial sectors are presented. The identified research gaps, trends and technical limitations hampering the implementation of digital twins are also discussed. In particular, this review attempts to address the research question on how the digital twin concept can support the realisation of an integrated, flexible and collaborative manufacturing environment which is one of the goals projected by the fourth industrial revolution. To address this, a conceptual framework supporting an integrated product-process digital twin for application in digitised manufacturing is proposed. The application and benefits of the proposed framework are presented in three case studies.

'Digital twins', also called 'digital patient twins' or 'virtual human twins' - digital patient-specific models derived from multimodal health data - are a strong focus in health care and are emerging as a promising tool for improving personalized care in uro-oncology. These models can integrate clinical, genomic, imaging and histopathological information to simulate organ behaviour and disease progress as well as predict responses to treatments. The concept of digital twins has shown potential in various fields, but its application in uro-oncology is still evolving, with few assessments of their feasibility and clinical utility. The advent of artificial intelligence adds a new dimension to their development, potentially enabling the synthesis of diverse, high-quality datasets to improve modelling accuracy and support real-time decision-making. However, substantial challenges exist, including data integration, patient privacy, computational demands and ethical frameworks. In addition, the interpretability of predictions remains essential for gaining clinical trust and guiding patient-centred decisions. The use of digital twins in uro-oncology has the potential to improve patient stratification and treatment planning; however, barriers must be overcome for their successful implementation in clinical routine. By integrating new technologies, fostering interdisciplinary collaboration and prioritizing transparency, digital twins could shape the future of precision uro-oncology.

Structural health monitoring (SHM) is a critical technology for ensuring infrastructure safety, extending their service life, and reducing their maintenance costs. With the rapid development of digital twin (DT) technology, an increasing number of studies have implemented DT in SHM systems. This study provides a detailed analysis of the role of DT in SHM through a comprehensive literature review, specifically examining its applications in damage detection, dynamic response monitoring, and maintenance management. The paper first reviews advances in DT applications across various fields, then systematically discusses how DT enhances monitoring accuracy, enables real-time performance, and supports predictive maintenance strategies in SHM. Finally, technical challenges and future research directions for DT implementation in SHM are explored. The findings highlight DT’s significant potential to improve both the efficiency and the accuracy of structural monitoring systems, while proposing innovative solutions for intelligent infrastructure management.

To achieve real-time structural health monitoring (SHM), a concept of digital twin - a digital copy of a structure has been brought up and investigated. It provides an up-to-date virtual model of structures, with the integration of physical as well as data information. The goal of this research is to provide faster and more accurate procedures to capture the spatial information required by a digital twin of a concrete structure using 3D point cloud data. Given that the method is intended for real-scale structures, such as bridges, the work can be divided to 3 steps: (1) to segment and extract geometric information for structural components; (2) to convert the geometry information to FE mesh with consideration of element types; (3) to assign material property as well as boundary conditions based on extracted components type. Linear FE analyses have been carried out to evaluate the structural performance based on the FE model created from the point cloud. The automation of such a process is an essential part of the creation of a digital twin of infrastructures.

Model-based damage identification for Structural Health Monitoring (SHM) remains an open issue in the literature. Along with the computational challenges related to the modeling of full-scale structures, classical single-model structural identification (St-Id) approaches provide no means to guarantee the physical meaningfulness of the inverse calibration results. In this light, this work introduces a novel concept of multi-class digital twins (DTs) formed by a population of competing models, each representing a different failure mechanism. The forward models in the DT are replaced by computationally efficient meta-models, and continuously calibrated using monitoring data. If an anomaly in the structural performance is detected, a model selection approach based on the Bayesian information criterion (BIC) is used to identify the most plausibly activated failure mechanism. The potential of the proposed approach is illustrated through two case studies, including a numerical planar truss and a real-world historical construction: the Muhammad Tower in the Alhambra fortress.