Deformation monitoring of load-bearing reinforced concrete beams

Tsvetkov, R. V.; Шардаков, И. Н.; Shestakov, A.; Gusev, G.; Epin, V.

doi:10.1016/j.prostr.2017.07.028

Cited by 4 publications

(7 citation statements)

References 7 publications

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“…However, reliable estimation outcomes can be obtained only using many local strain sensors. Tsvetkov et al proposed an automatic deformation monitoring system for RC beams in an air bridge 13 . They measured crack width and vertical displacements (deflections) of beams by using IP cameras (sensors).…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying deflections of reinforced concrete beams under seismic loads by bio‐inspired optimization of deep residual learning

Chou

Karundeng

Truong

et al. 2022

Structural Contr & Hlth

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Summary The seismic performance of a building must be evaluated after it has been affected by an earthquake load. In the evaluation process, building codes and standards require that the drift of the structure is determined to assess structural performance. This study provides an innovative method that helps engineers in measuring the deflection of reinforced concrete (RC) beams. An imagery deep learning model, called residual networks (ResNet), is used to classify the deflection based on observation by computer vision. However, determining the optimal values of the hyperparameters of this model is a challenge. Therefore, a hybrid model that integrates the bio‐inspired optimization (i.e., jellyfish search [JS] algorithm) and ResNet is developed. The input data that are used to train the model are images that are collected in RC structural experiments. This experiment involved 29 cantilever beams with various RC designs. These specimen RC beams were tested under simulated seismic loads with lateral displacement control. After each load had been applied to the beam, four single‐lens digital cameras captured images from the east, west, north, and south. Then, the performance of computer vision‐based JS–ResNet was evaluated by comparing its accuracy with that of the original ResNet using default hyperparameters. The results of the analysis show that the proposed JS–ResNet model achieves higher accuracy than conventional ResNet. Therefore, the hybrid model can provide insights in similar visual surveillance tasks.

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Section: Literature Reviewmentioning

confidence: 99%

Identifying deflections of reinforced concrete beams under seismic loads by bio‐inspired optimization of deep residual learning

Chou

Karundeng

Truong

et al. 2022

Structural Contr & Hlth

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“…They measured the displacements resulting from pressure change on uneven and irregular surfaces by combining the digital photogrammetry and Delaunay triangulation methods (Luo et al , 2017). Tsvetkov et al (2017) used close-range photogrammetry for deformation monitoring of load-bearing reinforced concrete beams. They used internet protocol cameras, which transfer images to the server with wireless technologies (Tsvetkov et al , 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Tsvetkov et al (2017) used close-range photogrammetry for deformation monitoring of load-bearing reinforced concrete beams. They used internet protocol cameras, which transfer images to the server with wireless technologies (Tsvetkov et al , 2017). Barazzetti and Scaioni (2010) presented the development and implementation of three image-based methods used to detect and measure the displacements of a vast number of points in the case of laboratory testing on construction materials.…”

Section: Introductionmentioning

confidence: 99%

Precise multi-image pointing (MIP) applied on convergence close-range photogrammetry images

Esmaeili

Ebadi

Saadatseresht

et al. 2020

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Purpose Displacement measurement in large-scale structures (such as excavation walls) is one of the most important applications of close-range photogrammetry, in which achieving high precision requires extracting and accurately matching local features from convergent images. The purpose of this study is to introduce a new multi-image pointing (MIP) algorithm is introduced based on the characteristics of the geometric model generated from the initial matching. This self-adaptive algorithm is used to correct and improve the accuracy of the extracted positions from local features in the convergent images. Design/methodology/approach In this paper, the new MIP algorithm based on the geometric characteristics of the model generated from the initial matching was introduced, which in a self-adaptive way corrected the extracted image coordinates. The unique characteristics of this proposed algorithm were that the position correction was accomplished with the help of continuous interaction between the 3D model coordinates and the image coordinates and that it had the least dependency on the geometric and radiometric nature of the images. After the initial feature extraction and implementation of the MIP algorithm, the image coordinates were ready for use in the displacement measurement process. The combined photogrammetry displacement adjustment (CPDA) algorithm was used for displacement measurement between two epochs. Micro-geodesy, target-based photogrammetry and the proposed MIP methods were used in a displacement measurement project for an excavation wall in the Velenjak area in Tehran, Iran, to evaluate the proposed algorithm performance. According to the results, the measurement accuracy of the point geo-coordinates of 8 mm and the displacement accuracy of 13 mm could be achieved using the MIP algorithm. In addition to the micro-geodesy method, the accuracy of the results was matched by the cracks created behind the project’s wall. Given the maximum allowable displacement limit of 4 cm in this project, the use of the MIP algorithm produced the required accuracy to determine the critical displacement in the project. Findings Evaluation of the results demonstrated that the accuracy of 8 mm in determining the position of the points on the feature and the accuracy of 13 mm in the displacement measurement of the excavation walls could be achieved using precise positioning of local features on images using the MIP algorithm.The proposed algorithm can be used in all applications that need to achieve high accuracy in determining the 3D coordinates of local features in close-range photogrammetry. Originality/value Some advantages of the proposed MIP photogrammetry algorithm, including the ease of obtaining observations and using local features on the structure in the images rather than installing the artificial targets, make it possible to effectively replace micro-geodesy and instrumentation methods. In addition, the proposed MIP method is superior to the target-based photogrammetric method because it does not need artificial target installation and protection. Moreover, in each photogrammetric application that needs to determine the exact point coordinates on the feature, the proposed algorithm can be very effective in providing the possibility to achieve the required accuracy according to the desired objectives.

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“…Combining the digital photogrammetry with the Delaunay triangulation, they could measure the pressure-induced displacements on irregular non-smooth surfaces [15]. In another work, Tsvetkov et al (2017) used the close-range photogrammetry for deformation monitoring of load-bearing reinforced concrete [16]. For this purpose, they used IP cameras with a server to which the captured images were transferred wirelessly.…”

Section: Introductionmentioning

confidence: 99%

“…The methods were scored based on a 0-5 scale, with a higher score in each parameter indicating better performance of the considered in that parameter. load-bearing reinforced concrete [16]. For this purpose, they used IP cameras with a server to which the captured images were transferred wirelessly.…”

Section: Introductionmentioning

confidence: 99%

Application of UAV Photogrammetry in Displacement Measurement of the Soil Nail Walls Using Local Features and CPDA Method

Esmaeili

Ebadi

Saadatseresht

et al. 2019

IJGI

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The high cost of land across urban areas has made the excavation a typical practice to construct multiple underground stories. Various methods have been used to restrain the excavated walls and keep them from a possible collapse, including nailing and anchorage. The excavated wall monitoring, especially during the drilling and restraining operations, is necessary for preventing the risk of such incidents as an excavated wall collapse. In the present research, an unmanned aerial vehicle (UAV) photogrammetry-based algorithm was proposed for accurate, fast and low-cost monitoring of excavated walls. Different stages of the proposed methodology included design of the UAV photogrammetry network for optimal imaging, local feature extraction from the acquired images, a special optimal matching method and finally, displacement estimation through a combined adjustment method. Results of implementations showed that, using the proposed methodology, one can achieve a precision of ±7 mm in positioning local features on the excavated walls. Moreover, the wall displacement could be measured at an accuracy of ±1 cm. Having high flexibility, easy implementation, low cost and fast pace; the proposed methodology provides an appropriate alternative to micro-geodesic procedures and the use of instrumentations for excavated wall displacement monitoring.

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Deformation monitoring of load-bearing reinforced concrete beams

Cited by 4 publications

References 7 publications

Identifying deflections of reinforced concrete beams under seismic loads by bio‐inspired optimization of deep residual learning

Identifying deflections of reinforced concrete beams under seismic loads by bio‐inspired optimization of deep residual learning

Precise multi-image pointing (MIP) applied on convergence close-range photogrammetry images

Application of UAV Photogrammetry in Displacement Measurement of the Soil Nail Walls Using Local Features and CPDA Method

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