Novel Sensing Approaches for Structural Deformation Monitoring and 3D Measurements

Castro-Toscano, Moisés J.; Rodríguez-Quiñonez, Julio C.; Sergiyenko, Oleg; Flores-Fuentes, Wendy; Ramírez-Hernández, L.; Hernández-Balbuena, Daniel; Линднер, Ларс; Rascón, Raúl

doi:10.1109/jsen.2020.3031882

Cited by 16 publications

(6 citation statements)

References 37 publications

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“…As it is becoming more crucial as a decision maker, AI is becoming increasingly capable of processing enormous volumes of complicated data in a very short period. With the advent of new software and advances in AI, the system will be able to discern this need on its own and take over quality monitoring of the parts [154][155][156]. Using artificial intelligence, 3D printer technology can definitely help to fix many parts of bridges in the coming years in a better way.…”

Section: Discussion and Remarksmentioning

confidence: 99%

The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges

et al. 2022

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The challenges of urban administration are growing, as the population, automobiles, and cities rise. Making cities smarter is thus one of the most effective solutions to urban issues. A key feature of the “smart cities” of today is that they use cutting-edge technology in their infrastructure and services. With strategic planning, the smart city utilizes its resources in the most efficient manner. With reduced expenses and enhanced infrastructure, smart cities provide their residents with more and better services. One of these important urban services that can be very helpful in managing cities is structural health monitoring (SHM). By combining leading new technologies like the Internet of Things (IoT) with structural health monitoring, important urban infrastructure can last longer and work better. A thorough examination of recent advances in SHM for infrastructure is thus warranted. Bridges are one of the most important parts of a city’s infrastructure, and their building, development, and proper maintenance are some of the most important aspects of managing a city. The main goal of this study is to look at how artificial intelligence (AI) and some technologies, like drone technology and 3D printers, could be used to improve the current state of the art in SHM systems for bridges, including conceptual frameworks, benefits and problems, and existing methods. An outline of the role AI and other technologies will play in SHM systems of bridges in the future was provided in this study. Some novel technology-aided research opportunities are also highlighted, explained, and discussed.

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Section: Discussion and Remarksmentioning

confidence: 99%

The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges

et al. 2022

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“…For short away distance, the limitations can be adjusted by the implementation of machine learning strategies through measurement correction methods, but for long distances, it is not possible with this configuration due to the photoelectric sensor capacity to perceive the reflected laser signal. 38 It is structured with laser positioning and an optical scanning aperture, to obtain 3D coordinates through laser dynamic triangulation principles, as shown in Figure 3(a), where the (x, y, z) coordinate is calculated by equations ( 3)-( 5) 39,40…”

Section: D Optical Scanning System For Displacement Measurementmentioning

confidence: 99%

“…It is structured with laser positioning and an optical scanning aperture, to obtain 3D coordinates through laser dynamic triangulation principles, as shown in Figure 3(a), where the ( x , y , z ) coordinate is calculated by equations (3)–(5) 39,40…”

Section: D Optical Scanning System For Displacement Measurementmentioning

confidence: 99%

“…4 However, its default design based on the use of photodiode presented limitations at measuring 3D coordinates in environments with optical noise; especially in outdoor environments with a high concentration of sunlight, resulting in the saturation of the photoelectric sensor in the approximate range of daytime between 10:00 and 18:00 h (at Mexicali, Baja California, Mexico, where the experiments were performed, a place worldwide known as the city that captured the sun). 41 After a comparison of the different kind of photoelectric sensors such as photodiodes, phototransistors, light-emitting diode (LED) as a receiver, infrared LED’s as a receiver, and inclusive the light-dependent resistor (LDR), it has been observed that the use of an LED as a photoelectric sensor, in special those working at the wavelength range of blue light (∼427–476 nm), 39,42 it is possible to discriminate the sunlight noise with the support of digital filters, such as finite impulse response (FIR), and the Savitzky–Golay filter, enabling the system to work in hostile environments with lighting contamination. 43 Besides the use of computational techniques based on pattern classification, like linear discriminant analysis (LDA) and linear regression (LR), it is possible to classify the different light sources detected to discriminate those that are not of interest.…”

Section: D Optical Scanning System For Displacement Measurementmentioning

confidence: 99%

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A structural health monitoring method proposal based on optical scanning and computational models

Flores-Fuentes

Alba-Corpus

Sergiyenko

et al. 2022

International Journal of Distributed Sensor Networks

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This article proposes a method for continuous bridge displacement monitoring combining the dynamic triangulation scanning and load estimation by vehicle image recognition. The vehicle–bridge interaction is a non-stationary dynamic process parameter of high relevance to understanding the static instability behavior of bridges. The knowledge of the load on a bridge in the specific time when its structural spatial coordinates are measured allows correlating the bridge displacement with the effect of vehicle–bridge interaction. The evaluation of such correlation is mandatory in order to verify if the observed bridge displacement is due to the nature of its operation or due to it is presenting structural damage. The proposed method is continuous structural health monitoring method, based on the combination of three approaches evaluated at laboratory environment: (1) a three-dimensional optical scanning system for displacement measurement, (2) a load measurement system for vehicle–bridge interaction assessment, and (3) a two-measurement systems data correlation; to be implemented in bridges at a real environment to collect their historical behavior. Overall, for each approach, the measurement systems’ principles, the laboratory experimental methodology followed, and results obtained are presented.

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“…In recent years, with the development of surveying and mapping technology, terrestrial laser scanners (TLS), known for their high precision and high efficiency, have been widely used in deformation monitoring, such as bridge monitoring and tunnel monitoring [6]- [9]. Reference [10] proposed a method to extract the deformation features of the highway bridge head-based TLS point cloud for bridge monitoring.…”

Section: Introductionmentioning

confidence: 99%

Automatic Deformation Extraction Method of Buildings in Mining Areas Based on TLS Point Clouds

Wang

Jiang

et al. 2022

IEEE Access

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Presently, the deformation extraction of buildings in mining areas using terrestrial laser scanner (TLS) point clouds is performed manually. Automatic deformation extraction holds great significance for building deformation monitoring in mining areas. Therefore, this study proposes an automatic extraction method for building deformation in mining areas using TLS point clouds. The corner points of doors and windows on the wall are considered as key points and the wall deformation in the mining area is extracted with minimal manual intervention. First, the input data were preprocessed, including 2D boundary point cloud acquisition and denoising (using a distance slope filter). Next, the key points were extracted via three steps: boundary line splitting, seed key point clustering, and key point judgment. Finally, the 3D coordinates of the key points and the relationship between the key points of the two phases were established to calculate the deformation value. The results confirmed negligible difference between the deformation value extracted using this method and the real value. Most of the errors were between −5 and 5 mm, and only a few exceeded ±5 mm; however, no error exceeded ±9 mm. The deformation value obtained using this method was almost identical to that obtained using the manual method, and the absolute error between them was below 8 mm. The performance verification of this method showed that the proposed distance slope filter removed the noise points more effectively compared to that of the traditional denoising filters, i.e., statistical and radius filters, establishing its suitability for the complex measurement environment of mining areas. During the automatic extraction of key point coordinates, the root mean square error (RMSE) values were below 2.0 mm; RMSE values during manual extraction were below 7.1 mm. The proposed method demonstrated greater stability than that of the manual extraction method.INDEX TERMS Deformation monitoring, mining damage assessment, point cloud, terrestrial laser scanner (TLS).

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Novel Sensing Approaches for Structural Deformation Monitoring and 3D Measurements

Cited by 16 publications

References 37 publications

The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges

The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges

A structural health monitoring method proposal based on optical scanning and computational models

Automatic Deformation Extraction Method of Buildings in Mining Areas Based on TLS Point Clouds

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