Health assessment of dams under various environmental conditions using structural health monitoring techniques: a state-of-art review

Sivasuriyan, Arvindan; Vijayan, Dhanasingh Sivalinga; Munusami, Ravindiran; Devarajan, Parthiban

doi:10.1007/s11356-021-16749-3

Cited by 22 publications

(4 citation statements)

References 81 publications

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“…The primary aim of this review is to identify the structures associated with inland navigation, explore the state-of-the-art developments in their monitoring, and identify the challenges associated with their implementations. Although dams in navigable rivers can be considered as a part of INS, recent reviews of (i) health monitoring of concrete dams 51,52 and dams under various environmental conditions, 53 and (ii) dam monitoring data analysis methods 54 already exist, and, therefore, SHM of dams is not reviewed in this paper.…”

Section: Review Organizationmentioning

confidence: 99%

Structural health monitoring of inland navigation structures and ports: a review on developments and challenges

Negi

Kromanis

Dorée

et al. 2023

Structural Health Monitoring

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Inland navigation structures (INS) facilitate transportation of goods in rivers and canals. Transportation of goods over waterways is more energy efficient than on roads and railways. INS, similar to other civil structures, are aging and require frequent condition assessment and maintenance. Countries, in which INS are important to their economies, such as the Netherlands and the United States, allocate significant budgets for maintenance and renovation of exiting INS, as well as for building new structures. Timely maintenance and early detection of a change to material or geometric properties (i.e., damage) can be supported with the structural health monitoring (SHM), in which monitored data, such as load, structural response, environmental actions, are analyzed. Huge scientific efforts are realized in bridge SHM, but when it comes to SHM of INS, the efforts are significantly lower. Therefore, the SHM community has opportunities to develop new solutions for SHM of INS and convince asset owners of their benefits. This review article, first, articulates the need to keep INS safe to use and fit for purpose, and the challenges associated with it. Second, it defines and reviews sensors, sensing technologies, and approaches for SHM of INS. Then, INS and their components, including structures in ports, are identified, described, and illustrated, and their monitoring efforts are reviewed. Finally, the review article emphasizes the added value of SHM systems for INS, concludes on the current achievements, and proposes future trajectories for SHM of INS and ports.

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Section: Review Organizationmentioning

confidence: 99%

Structural health monitoring of inland navigation structures and ports: a review on developments and challenges

Negi

Kromanis

Dorée

et al. 2023

Structural Health Monitoring

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“…It facilitates the rapid assessment and monitoring of the dam's safety operation status, providing insights into the working conditions and variations of hydraulic structures under adverse loads and various influencing factors [3][4][5]. By analyzing monitoring data, one can make correct judgments and evaluations regarding the safety level of the engineering structure, thereby providing a basis for the safe operation of reservoir dams [6]. It is beneficial for timely assessment and response to emergencies, offering scientific and timely data information for on-site inspections.…”

Section: Introductionmentioning

confidence: 99%

A Dam Safety State Prediction and Analysis Method Based on EMD-SSA-LSTM

Yang,

Xiang,

Wang

et al. 2024

Water

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The safety monitoring information of the dam is an indicator reflecting the operational status of the dam. It is a crucial source for analyzing and assessing the safety state of reservoir dams, possessing strong real-time capabilities to detect anomalies in the dam at the earliest possible time. When using neural networks for predicting and warning dam safety monitoring data, there are issues such as redundant model parameters, difficulty in tuning, and long computation times. This study addresses real-time dam safety warning issues by first employing the Empirical Mode Decomposition (EMD) method to decompose the effective time-dependent factors and construct a dam in a service state analysis model; it also establishes a multi-dimensional time series analysis equation for dam seepage monitoring. Simultaneously, by combining the Sparrow Optimization Algorithm to optimize the LSTM neural network computation process, it reduces the complexity of model parameter selection. The method is compared to other approaches such as RNN, GRU, BP neural networks, and multivariate linear regression, demonstrating high practicality. It can serve as a valuable reference for reservoir dam state prediction and engineering operation management.

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“…On the other hand, Acoustic emission methods capture acoustic waves scattered by damage [4]. These data are then analyzed to locate and quantify damage, assess the underlying reason (diagnosis), and estimate the future performance of the structure (prognosis) [4,5] The guarantee of life and economic safety is always a strong motivation [6] especially for civil infrastructures which comprise bridges [7], buildings [8], towers [9], pipelines [10], tunnels [11], and dams [12]. These structures are made of steel, composites, concrete, and wood, which gives means to our modern society to function.…”

Section: Introductionmentioning

confidence: 99%

From data to insight, enhancing structural health monitoring using physics-informed machine learning and advanced data collection methods

Rizvi,

Abbas

2023

Eng. Res. Express

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Owing to recent advancements in sensor technology, data mining, machine learning and cloud computation, structural health monitoring (SHM) based on a data-driven approach has gained more popularity and interest. The data-driven methodology has proved to be more efficient and robust compared with traditional physics-based methods. The past decade has witnessed remarkable progress in machine learning, especially in the field of deep learning which are effective in many tasks and has achieved state-of-the-art results in various engineering domains. In the same manner, deep learning has also revolutionized SHM technology by improving the effectiveness and efficiency of models, as well as enhancing safety and reliability. To some extent, it has also paved the way for implementing SHM in real-world complex civil and mechanical infrastructures. However, despite all the success, deep learning has intrinsic limitations such as its massive-labelled data requirement, inability to generate consistent results and lack of generalizability to out-of-sample scenarios. Conversely, in SHM, the lack of data corresponding to a different state of the structure is still a challenging task. Recent development in physics-informed machine learning methods has provided an opportunity to resolve these challenges in which limited-noisy data and mathematical models are integrated through machine learning algorithms. This method automatically satisfies physical invariants providing better accuracy and improved generalization. This manuscript presents the sate of- -the-art review of prevailing machine learning methods for efficient damage inspection, discuss their limitations, and explains the diverse applications and benefits of physics-informed machine learning in the SHM setting. Moreover, the latest data extraction strategy and the internet of things (IoT) that support the present data-driven methods and SHM are also briefly discussed in the last section.

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Health assessment of dams under various environmental conditions using structural health monitoring techniques: a state-of-art review

Cited by 22 publications

References 81 publications

Structural health monitoring of inland navigation structures and ports: a review on developments and challenges

Structural health monitoring of inland navigation structures and ports: a review on developments and challenges

A Dam Safety State Prediction and Analysis Method Based on EMD-SSA-LSTM

From data to insight, enhancing structural health monitoring using physics-informed machine learning and advanced data collection methods

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