Design and evaluation of 5G-based architecture supporting data-driven digital twins updating and matching in seismic monitoring

Gattulli, Vincenzo; Franchi, Fabio; Graziosi, Fabio; Marotta, Andrea; Rinaldi, Claudia; Potenza, Francesco; Sabatino, Umberto Di

doi:10.1007/s10518-022-01329-8

Cited by 15 publications

(6 citation statements)

References 41 publications

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“…These models were deemed appropriate for the present study due to their common characteristics. Firstly, they have been successful in data-driven structural health-monitoring [63][64][65][66][67] and investigating infrastructure problems in previous studies [28,33,68,69]. Secondly, the existing studies have demonstrated the effectiveness of Random Forest [70,71], XGboost [22,28], and ANN [25,29,33,72,73] in capturing the complex and nonlinear relationships among the variables involved in predicting bridge condition rating.…”

Section: Results Of Training and Evaluating Modelsmentioning

confidence: 99%

Development and Utilization of Bridge Data of the United States for Predicting Deck Condition Rating Using Random Forest, XGBoost, and Artificial Neural Network

Fard,

Sadeghi Naieni Fard

2024

Remote Sensing

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Accurately predicting the condition rating of a bridge deck is crucial for effective maintenance and repair planning. Despite significant research efforts to develop deterioration models, the efficacy of Random Forest, eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN) in predicting the condition rating of the nation’s bridge decks has remained unexplored. This study aims to assess the effectiveness of these algorithms for deck condition rating prediction at the national level. To achieve this, the study collected bridge data, which includes National Bridge Inventory (NBI), traffic, and climate regions gathered using Geospatial Information Science (GIS) and remote sensing techniques. Two datasets were collected: bridge data for a single year of 2020 and historical bridge data covering a five-year period from 2016 to 2020. Three models were trained using 319,404 and 1,246,261 bridge decks in the single-year bridge data and the five-year historical bridge data, respectively. Results show that the use of historical bridge data significantly improves the performance of the models compared to the single-year bridge data. Specifically, the Random Forest model achieved an overall accuracy of 83.4% and an average F1 score of 79.7%. In contrast, the XGBoost model achieved an overall accuracy of 79.4% and an average F1 score of 77.5%, while the ANN model obtained an overall accuracy of 79.7% and an average F1 score of 78.4%. Permutation-based variable importance reveals that NBI, traffic, and climate regions significantly contribute to model development. In conclusion, the Random Forest, XGBoost, and ANN models, trained using updated historical bridge data, provide useful tools for accurately predicting the condition rating of bridge decks in the United States, allowing infrastructure managers to efficiently schedule inspections and allocate maintenance resources.

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Section: Results Of Training and Evaluating Modelsmentioning

confidence: 99%

Development and Utilization of Bridge Data of the United States for Predicting Deck Condition Rating Using Random Forest, XGBoost, and Artificial Neural Network

Fard,

Sadeghi Naieni Fard

2024

Remote Sensing

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“…More efficient programming languages were identified to present the system using a micro-service architecture. Additionally, issues identified during the testing phase of prior system implementations were resolved [ 37 ].…”

Section: Description Of the Smart-wired Monitoring Systemmentioning

confidence: 99%

Developing and Testing High-Performance SHM Sensors Mounting Low-Noise MEMS Accelerometers

Crognale,

Rinaldi,

Potenza

et al. 2024

Sensors

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Recently, there has been increased interest in adopting novel sensing technologies for continuously monitoring structural systems. In this respect, micro-electrical mechanical system (MEMS) sensors are widely used in several applications, including structural health monitoring (SHM), in which accelerometric samples are acquired to perform modal analysis. Thanks to their significantly lower cost, ease of installation in the structure, and lower power consumption, they enable extensive, pervasive, and battery-less monitoring systems. This paper presents an innovative high-performance device for SHM applications, based on a low-noise triaxial MEMS accelerometer, providing a guideline and insightful results about the opportunities and capabilities of these devices. Sensor nodes have been designed, developed, and calibrated to meet structural vibration monitoring and modal identification requirements. These components include a protocol for reliable command dissemination through network and data collection, and improvements to software components for data pipelining, jitter control, and high-frequency sampling. Devices were tested in the lab using shaker excitation. Results demonstrate that MEMS-based accelerometers are a feasible solution to replace expensive piezo-based accelerometers. Deploying MEMS is promising to minimize sensor node energy consumption. Time and frequency domain analyses show that MEMS can correctly detect modal frequencies, which are useful parameters for damage detection. The acquired data from the test bed were used to examine the functioning of the network, data transmission, and data quality. The proposed architecture has been successfully deployed in a real case study to monitor the structural health of the Marcus Aurelius Exedra Hall within the Capitoline Museum of Rome. The performance robustness was demonstrated, and the results showed that the wired sensor network provides dense and accurate vibration data for structural continuous monitoring.

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“…Because DT integrates artificial intelligence methods such as support vector machines and neural networks, it is highly accurate in predicting fire scale, flood degree, and hurricane trajectory [129][130][131]. At the same time, with the maturity of the Internet of Things technology, DT can quickly and accurately share the disaster situation and issue early warnings of the crisis to people who may be affected by the disaster [132].…”

Section: Mitigationmentioning

confidence: 99%

A Review of Digital Twin Applications in Civil and Infrastructure Emergency Management

Cheng

Hou

2023

Buildings

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Natural disasters can cause severe damages to civil infrastructure and lead to extensive economic losses and casualties. To improve the emergency response capability of civil infrastructure under extreme circumstances such as natural disasters and human-caused hazards, intelligent technology for infrastructure emergency management has been extensively studied. As an emerging paradigm of interdisciplinary convergence, digital twins (DTs) can integrate intelligent technology into different stages of emergency management and provide a new solution for the emergency management of civil infrastructure (EMCI). However, applications of DT in EMCI have several limitations and are mostly case by case. However, the sector needs more generalisable lessons to address the greater value of DT in the context of EMCI. To address this gap, we first carry out a systematic literature review and analyse the latest progress and previous research deficiencies of DT by taking the scientometrical approach. Next, a framework is proposed to explain how DT can be applied to the mitigation, preparation, response, and recovery stages of EMCI. Lastly, the trends and prospects of DT applications in EMCI are discussed. Overall, the knowledge gained from this study will promote the research and development of more-viable DTs to address the sector’s demand for emergency management.

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Design and evaluation of 5G-based architecture supporting data-driven digital twins updating and matching in seismic monitoring

Cited by 15 publications

References 41 publications

Development and Utilization of Bridge Data of the United States for Predicting Deck Condition Rating Using Random Forest, XGBoost, and Artificial Neural Network

Development and Utilization of Bridge Data of the United States for Predicting Deck Condition Rating Using Random Forest, XGBoost, and Artificial Neural Network

Developing and Testing High-Performance SHM Sensors Mounting Low-Noise MEMS Accelerometers

A Review of Digital Twin Applications in Civil and Infrastructure Emergency Management

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