Extraction of Wall Cracks on Earthquake-Damaged Buildings Based on TLS Point Clouds

Jiang, Hongbo; Li, Qiang; Jiao, Qisong; Wang, Xin; Wu, Lie

doi:10.1109/jstars.2018.2857564

Cited by 21 publications

(9 citation statements)

References 23 publications

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“…Minerals 2020, 10, 174 3 of 19 template for the application of UAV in mapping river landscape as a research tool for this type of landscape [22]; the limits and advantages of photomodeling compared to structured light 3D scanning systems were demonstrated by Catalucci et al [23]; Fraštia et al provided a comprehensive and accurate macro-view of the morphology of inaccessible rock towers and blocks using LiDAR, which could not be obtained from field survey [24] and other studies. For related industries, such as civil engineering and architecture, we recognize TLS applications in documenting the actual state of buildings and their deformations [25][26][27]; in mechanical engineering to document complex machinery [28,29]; or transport engineering [30].…”

Section: Study Areamentioning

confidence: 99%

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TLS and SfM Approach for Bulk Density Determination of Excavated Heterogeneous Raw Materials

et al. 2020

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A frequently recurring problem in the extraction of mineral resources (especially heterogeneous mineral resources) is the rapid operative determination of the extracted quantity of raw material in a surface quarry. This paper deals with testing and analyzing the possibility of using unconventional methods such as digital close-range photogrammetry and terrestrial laser scanning in the process of determining the bulk density of raw material under in situ conditions. A model example of a heterogeneous deposit is the perlite deposit Lehôtka pod Brehmi (Slovakia). Classical laboratory methods for determining bulk density were used to verify the results of the in situ method of bulk density determination. Two large-scale samples (probes) with an approximate volume of 7 m 3 and 9 m 3 were realized in situ. 6 point samples (LITH) were taken for laboratory determination. By terrestrial laser scanning (TLS) measurement from 2 scanning stations, point clouds with approximately 163,000/143,000 points were obtained for each probe. For Structure-from-Motion (SfM) photogrammetry, 49/55 images were acquired for both probes, with final point clouds containing approximately 155,000/141,000 points. Subsequently, the bulk densities of the bulk samples were determined by the calculation from in situ measurements by TLS and SfM photogrammetry. Comparison of results of the field in situ measurements (1841 kg·m −3 ) and laboratory measurements (1756 kg·m −3 ) showed only a 4.5% difference in results between the two methods for determining the density of heterogeneous raw materials, confirming the accuracy of the used in situ methods. For the determination of the loosening coefficient, the material from both large-scale samples was transferred on a horizontal surface. Their volumes were determined by TLS. The loosening coefficient for the raw material of 1.38 was calculated from the resulting values.

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Section: Study Areamentioning

confidence: 99%

“…For related industries, such as civil engineering and architecture, we recognize TLS applications in documenting the actual state of buildings and their deformations [25][26][27]; in mechanical engineering to document complex machinery [28,29]; or transport engineering [30].…”

Section: Introductionmentioning

confidence: 99%

TLS and SfM Approach for Bulk Density Determination of Excavated Heterogeneous Raw Materials

et al. 2020

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“…However, this approach was mainly developed to extract the types and locations of pavement cracks rather than the crack widths. Jiang et al [ 27 ] used the inverse distance weighting method to generate point cloud raster images to realize crack detection for post-earthquake buildings. However, this required many shape features to remove false cracks.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu’s Algorithm

Chen

Huang

et al. 2021

Sensors

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Cracks are one of the main distresses that occur on concrete surfaces. Traditional methods for detecting cracks based on two-dimensional (2D) images can be hampered by stains, shadows, and other artifacts, while various three-dimensional (3D) crack-detection techniques, using point clouds, are less affected in this regard but are limited by the measurement accuracy of the 3D laser scanner. In this study, we propose an automatic crack-detection method that fuses 3D point clouds and 2D images based on an improved Otsu algorithm, which consists of the following four major procedures. First, a high-precision registration of a depth image projected from 3D point clouds and 2D images is performed. Second, pixel-level image fusion is performed, which fuses the depth and gray information. Third, a rough crack image is obtained from the fusion image using the improved Otsu method. Finally, the connected domain labeling and morphological methods are used to finely extract the cracks. Experimentally, the proposed method was tested at multiple scales and with various types of concrete crack. The results demonstrate that the proposed method can achieve an average precision of 89.0%, recall of 84.8%, and F1 score of 86.7%, performing significantly better than the single image (average F1 score of 67.6%) and single point cloud (average F1 score of 76.0%) methods. Accordingly, the proposed method has high detection accuracy and universality, indicating its wide potential application as an automatic method for concrete-crack detection.

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“…In recent years, terrestrial laser scanning technology has been widely used in different fields such as three-dimensional modeling, heritage protection, deformation monitoring, and forest structure investigation [13]. Experiments using TLS for continuous monitoring and analysis of building structural component damage demonstrate its effectiveness in structural modeling and analysis applications [14,15]; scanning assessment and 3D modeling of ancient buildings using TLS propose effective solutions for the conservation and maintenance of ancient buildings [16]; in civil engineering applications, high precision data obtained by TLS in combination with least squares are used for the quality assessment of building plane regularity [17]; scholars applied "alpha shapes algorithm" to LiDAR data contour line extraction and regularization for buildings and demonstrated the accuracy of the algorithm in LiDAR point cloud data extraction for building contour lines [18,19]; in postearthquake building loss analysis, scholars proposed a building shape analysis model based on ground-based Li-DAR data, which effectively solved the problems of building contour polygon sequence extraction, shape discrete parameter extraction, irregular building block division, and earthquake damage analysis [20,21]. In terms of judging the degree of postearthquake building damage in the regional spatial context, scholars proposed a model based on texture extraction and fuzzy system analysis of postearthquake airborne LiDAR data, which effectively solved the problem of judging the degree of postearthquake building damage [22].…”

Section: Introductionmentioning

confidence: 99%

A Model Study of Building Seismic Damage Information Extraction and Analysis on Ground‐Based LiDAR Data

Yang

Wen

Wang

et al. 2021

Advances in Civil Engineering

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Earthquake disasters can have a serious impact on people’s lives and property, with damage to buildings being one of the main causes of death and injury. A rapid assessment of the extent of building damage is essential for emergency response management, rescue operations, and reconstruction. Terrestrial laser scanning technology can obtain high precision light detection and ranging (LiDAR) point cloud data of the target. The technology is widely used in various fields; however, the quantitative analysis of building seismic information is the focus and difficulty of ground-based LiDAR data analysis processing. This paper takes full advantage of the high-precision characteristics of ground-based LiDAR data. A triangular network vector model (TIN-shaped model) was created in conjunction with the alpha shapes algorithm, solving the problem of small, nonvisually identifiable postearthquake building damage feature extraction bias. The model measures the length, width, and depth of building cracks, extracts the amount of wall tilt deformation, and labels the deformation zone. The creation of this model can provide scientific basis and technical support for postearthquake emergency relief, assessment of damage to buildings, extraction of deformation characteristics of other structures (bridges, tunnels, dams, etc.), and seismic reinforcement of buildings. The research data in this paper were collected by the author’s research team in the first time after the 2013 Lushan earthquake and is one of the few sets of foundation of LiDAR data covering the full range of postearthquake building types in the region, with the data information mainly including different damage levels of different structural types of buildings. The modeling analysis of this data provides a scientific basis for establishing the earthquake damage matrix of buildings in the region.

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Extraction of Wall Cracks on Earthquake-Damaged Buildings Based on TLS Point Clouds

Cited by 21 publications

References 23 publications

TLS and SfM Approach for Bulk Density Determination of Excavated Heterogeneous Raw Materials

TLS and SfM Approach for Bulk Density Determination of Excavated Heterogeneous Raw Materials

An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu’s Algorithm

A Model Study of Building Seismic Damage Information Extraction and Analysis on Ground‐Based LiDAR Data

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