Digital twin-based collapse fragility assessment of a long-span cable-stayed bridge under strong earthquakes

Lin, Kaiqi; Xu, You Lin; Lu, Xinzheng; Guan, Zhongguo; Li, Jianzhong

doi:10.1016/j.autcon.2020.103547

Cited by 89 publications

(33 citation statements)

References 41 publications

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“…Common virtual modeling methods in DT practice mainly include the following. The first method utilizes 3D modeling software, including BIM, 3ds Max and simulation software (Boje et al 2020 ; Lin et al 2021 ), and many researchers have independently developed modeling programs (Lai et al 2021 ). The second method performs modeling using measuring instruments and equipment, such as laser scanners and infrared imaging systems (Dollner 2020 ).…”

Section: Development Of Dt-related Technology In the Infrastructure Fieldmentioning

confidence: 99%

Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities

Dong

2022

Nat Hazards

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Natural hazards, which have the potential to cause catastrophic damage and loss to infrastructure, have increased significantly in recent decades. Thus, the construction demand for disaster prevention and mitigation for infrastructure (DPMI) systems is increasing. Many studies have applied intelligence technologies to solve key aspects of infrastructure, such as design, construction, disaster prevention and mitigation, and rescue and recovery; however, systematic construction is still lacking. Digital twin (DT) is one of the most promising technologies for multi-stage management which has great potential to solve the above challenges. This paper initially puts forward a scientific concept, in which DT drives the construction of intelligent disaster prevention and mitigation for infrastructure (IDPMI) systematically. To begin with, a scientific review of DT and IDPMI is performed, where the development of DT is summarized and a DT-based life cycle of infrastructures is defined. In addition, the intelligence technologies used in disaster management are key reviewed and their relative merits are illustrated. Furthermore, the development and technical feasibility of DT-driven IDPMI are illustrated by reviewing the relevant practice of DT in infrastructure. In conclusion, a scientific framework of DT-IDPMI is programmed, which not only provides some guidance for the deep integration between DT and IDPMI but also identifies the challenges that inspire the professional community to advance these techniques to address them in future research.

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Section: Development Of Dt-related Technology In the Infrastructure Fieldmentioning

confidence: 99%

Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities

Dong

2022

Nat Hazards

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“…On the other hand, the digital twin can exploit artificial intelligence (AI) for rich data analytics-challenges of previous BIM and GIS applications [38,39]. Recently, digital twins have been applied widely in construction for different purposes such as information sharing among project participants [11], smart city level infrastructure management through linkage with GIS [40], sustainability (i.e., plan, design, operation, and maintenance) evaluation of railway station buildings [41], bridge construction management [42], collapse assessment [43], and real-time monitoring and anomaly detection for built assets [44]. These studies have shown that the digital twin can effectively monitor assets in near real-time and test 'what-if' scenarios to detect risks and optimize operation in a dynamic construction environment.…”

Section: Digital Twin For Real-time Monitoring and 'What-if' Analysismentioning

confidence: 99%

Digital Twin for Supply Chain Coordination in Modular Construction

Lee

2021

Applied Sciences

125

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Over the past decades, the construction industry has been attracted to modular construction because of its benefits for reduced project scheduling and costs. However, schedule deviation risks in the logistics process of modular construction can derail its benefits and thus interfere with its widespread application. To address this issue, we aim to develop a digital twin framework for real-time logistics simulation, which can predict potential logistics risks and accurate module arrival time. The digital twin, a virtual replica of the physical module, updates its virtual asset based on building information modeling (BIM) in near real-time using internet of thing (IoT) sensors. Then, the virtual asset is transferred and exploited for logistics simulation in a geographic information system (GIS)-based routing application. We tested this framework in a case project where modules are manufactured at a factory, delivered to the site via a truck, and assembled onsite. The results show that potential logistical risks and accurate module arrival time can be detected via the suggested digital twin framework. This paper’s primary contribution is the development of a framework that mediates IoT, BIM, and GIS for reliable simulation which predicts potential logistics risks and accurate module delivery time. Such reliable risk prediction enables effective supply chain coordination, which can improve project performance and the widespread application of modular construction.

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“…Such a model has the potential for predicting future performance under extreme loads and directing repair and maintenance operations. Lin et al [ 37 ] developed a digital twin of a scale model of the Sutong Bridge in China, specifically for characterizing seismic performance. By testing a physical scale model to failure under increasing earthquake excitations, the authors demonstrated finite element model updating procedures to accurately predict the failure modes and intensity measures of the physical specimen using the digital twin.…”

Section: Introductionmentioning

confidence: 99%

“…By testing a physical scale model to failure under increasing earthquake excitations, the authors demonstrated finite element model updating procedures to accurately predict the failure modes and intensity measures of the physical specimen using the digital twin. However, while successful at predicting collapse or existing damage to the structure, neither [ 36 ] nor [ 37 ] employed the digital twin to incorporate observed damage and revise collapse predictions for the earthquake-damaged structure. Indeed, digital twins have yet to be fully exploited for the post-earthquake assessment problem.…”

Section: Introductionmentioning

confidence: 99%

Post-Earthquake Building Evaluation Using UAVs: A BIM-Based Digital Twin Framework

Levine

Spencer

2022

Sensors

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Computer vision has shown potential for assisting post-earthquake inspection of buildings through automatic damage detection in images. However, assessing the safety of an earthquake-damaged building requires considering this damage in the context of its global impact on the structural system. Thus, an inspection must consider the expected damage progression of the associated component and the component’s contribution to structural system performance. To address this issue, a digital twin framework is proposed for post-earthquake building evaluation that integrates unmanned aerial vehicle (UAV) imagery, component identification, and damage evaluation using a Building Information Model (BIM) as a reference platform. The BIM guides selection of optimal sets of images for each building component. Then, if damage is identified, each image pixel is assigned to a specific BIM component, using a GrabCut-based segmentation method. In addition, 3D point cloud change detection is employed to identify nonstructural damage and associate that damage with specific BIM components. Two example applications are presented. The first develops a digital twin for an existing reinforced concrete moment frame building and demonstrates BIM-guided image selection and component identification. The second uses a synthetic graphics environment to demonstrate 3D point cloud change detection for identifying damaged nonstructural masonry walls. In both examples, observed damage is tied to BIM components, enabling damage to be considered in the context of each component’s known design and expected earthquake performance. The goal of this framework is to combine component-wise damage estimates with a pre-earthquake structural analysis of the building to predict a building’s post-earthquake safety based on an external UAV survey.

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Digital twin-based collapse fragility assessment of a long-span cable-stayed bridge under strong earthquakes

Cited by 89 publications

References 41 publications

Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities

Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities

Digital Twin for Supply Chain Coordination in Modular Construction

Post-Earthquake Building Evaluation Using UAVs: A BIM-Based Digital Twin Framework

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