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Damage Detection Based on Smartphones in Buildings
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Damage Detection Based on Smartphones in Buildings

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 14608

Special Issue Editors

Prof. Dr. Xuefeng Zhao

E-Mail Website
Guest Editor
School of Civil Engineering, Dalian University of Technology, Dalian 116024, China
Interests: civil engineering; damage detection; safety evaluation; numerical simulation; crowd sensing; deep learning; unmanned aerial vehicle; monitoring
Prof. Dr. Niannian Wang

E-Mail Website
Guest Editor
School of Water Conservancy Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: civil engineering; damage detection; safety evaluation; numerical simulation; crowd sensing; deep learning; unmanned aerial vehicle; monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Damage Identification of buildings has recently received considerable attention in the light of maintenance and retrofitting of existing structures under service loads and after natural disasters. Over the past few decades, smartphone-based imaging and sensing platforms have emerged as promising alternatives for health monitoring and damage detection of buildings, offering practical features such as portability and cost-effectiveness, particularly in regions with limited access to damage detection of buildings. At the same time, almost everyone has a smartphone, and each smartphone can be used as a sensor. Sensor interconnection of the masses can form mobile crowd sensing network, so that everyone can participate in building damage detection. On the other hand, smartphones can be combined with unmanned aerial vehicle (UAV), intelligent terminal and automatic equipment to detect building damages more efficiently. More importantly, the detection method can also be embedded with artificial intelligence, deep learning and other advanced technologies to make the damage detection of buildings more efficient and intelligent.

In this Special Issue, we invite you to contribute original research articles and reviews on any aspects related to smartphone-based damage detection of buildings, including but not limited to:

Structural health monitoring, damage detection, diagnosis and characterisation of damage of building; in situ field test methods, nondestructive techniques, laboratory tests and analysis; Simulation and modelling: deep learning and finite element models; Digitalisation and documentation, mobile crowd sensing, data bases; New methodologies, digital and innovative technologies, building information modelling (BIM).

Prof. Dr. Xuefeng Zhao
Dr. Niannian Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Smartphone
  • Crowd sensing
  • Unmanned Aerial Vehicle
  • Damage detection
  • Machine vision
  • Deep learning

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Published Papers (4 papers)

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Research

21 pages, 6919 KiB  
Article
Response Characteristics of Pre-Stressed Strand Cables Subjected to Low-Velocity Impact: Experiment Test
by Zhijie Wu, Yuchao Yang, Yachao Hu and Feng Liu
Buildings 2023, 13(2), 330; https://doi.org/10.3390/buildings13020330 - 22 Jan 2023
Viewed by 1872
Abstract
This paper introduces some experimental data measured from 63 impact tests of pre−stressed strand cables. The test specimens consist of seven steel wires that have a length equivalent to 100 times the outside diameter. To ensure consistency with the engineering service status, the [...] Read more.
This paper introduces some experimental data measured from 63 impact tests of pre−stressed strand cables. The test specimens consist of seven steel wires that have a length equivalent to 100 times the outside diameter. To ensure consistency with the engineering service status, the strand cables are fully installed in a specially designed device and are axially pre−stretched to 0% to 40% of the ultimate bearing capacity before being subjected to lateral impact. The mass of the indenter is 50.34 kg, and the maximum impact velocity reaches 13 m/s. Two dimensionless variables, axial force and input kinetic energy, are used to control the experimental parameters. The recorded test data show that input energy and pre−stress level are the key factors governing the impact behavior, which is mainly characterized by plastic deformation controlled by the combination of tension and flexure, and the dynamic fracture concentrated in the impact zone is controlled by the joint effects of compression, tension and shear. As the impact energy increases, the dynamic mode of the test specimen changes from elastic rebound to plastic deformation, and finally evolves into fracture of some or all steel wires, which correspond to slight, partial and total loss of pre−tension, respectively. An increase in the level of pre−stress will significantly reduce the critical displacement of the structural failure but has little effect on the critical failure energy. The present paper provides a basic experimental data and mechanical analysis framework for the analysis, design and evaluation of the mechanical behavior of strands under accidental lateral impact. Full article
(This article belongs to the Special Issue Damage Detection Based on Smartphones in Buildings)
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Graphical abstract

25 pages, 8598 KiB  
Article
Dynamic Characteristic Monitoring of Wind Turbine Structure Using Smartphone and Optical Flow Method
by Wenhai Zhao, Wanrun Li, Boyuan Fan and Yongfeng Du
Buildings 2022, 12(11), 2021; https://doi.org/10.3390/buildings12112021 - 18 Nov 2022
Cited by 2 | Viewed by 2511
Abstract
The dynamic characteristics of existing wind turbine structures are usually monitored using contact sensors, which is not only expensive but also time-consuming and laborious to install. Recently, computer vision technology has developed rapidly, and monitoring methods based on cameras and UAVs (unmanned aerial [...] Read more.
The dynamic characteristics of existing wind turbine structures are usually monitored using contact sensors, which is not only expensive but also time-consuming and laborious to install. Recently, computer vision technology has developed rapidly, and monitoring methods based on cameras and UAVs (unmanned aerial vehicles) have been widely used. However, the high cost of UAVs and cameras make it difficult to widely use them. To address this problem, a target-free dynamic characteristic monitoring method for wind turbine structures using portable smartphone and optical flow method is proposed by combining optical flow method with robust corner feature extraction in ROI (region of interest). Firstly, the ROI region clipping technology is introduced after the structural vibration video shooting, and the threshold value is set in the ROI to obtain robust corner features. The sub-pixel displacement monitoring is realized by combining the optical flow method. Secondly, through three common smartphone shooting state to monitor the structural displacement, the method of high pass filtering combined with adaptive scaling factor is used to effectively eliminate the displacement drift caused by the two shooting states of standing and slightly walking, which can meet the requirements of structural dynamic characteristics monitoring. After that, the structural displacement is monitored by assembling the telephoto lens on the smartphone. The accuracy of displacement monitored by assembling the telephoto lens on the smartphone is investigated. Finally, the proposed monitoring method is verified by the shaking table test of the wind turbine structure. The results show that the optical flow method, combined with smartphones, can accurately identify the dynamic characteristics of the wind turbine structure, and the smartphone equipped with a telephoto lens is more conducive to achieving low-cost wind turbine structure dynamic characteristics monitoring. This research can provide a reference for evaluating the condition of wind turbine structures. Full article
(This article belongs to the Special Issue Damage Detection Based on Smartphones in Buildings)
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Graphical abstract

17 pages, 4245 KiB  
Article
Rapid Reconstruction of 3D Structural Model Based on Interactive Graph Cuts
by Siyu Han, Linsheng Huo, Yize Wang, Jing Zhou and Hongnan Li
Buildings 2022, 12(1), 22; https://doi.org/10.3390/buildings12010022 - 29 Dec 2021
Cited by 8 | Viewed by 2631
Abstract
The image-based 3D reconstruction technique has been applied in many scenarios of civil engineering, such as earthquake prevention and disaster reduction, construction monitoring, and intelligent city construction. However, the traditional technique is time-consuming, and the modeling efficiency has become a bottleneck limiting its [...] Read more.
The image-based 3D reconstruction technique has been applied in many scenarios of civil engineering, such as earthquake prevention and disaster reduction, construction monitoring, and intelligent city construction. However, the traditional technique is time-consuming, and the modeling efficiency has become a bottleneck limiting its application in emergency scenarios. In this paper, a rapid reconstruction method is proposed which combines the traditional image-based 3D reconstruction technique and an interactive graph cuts algorithm. Firstly, a sequence of images is collected around the target structure. Then, the images are preprocessed using the interactive iterative graph cuts algorithm to extract the target from each image. Finally, the resulting sequence of images is used to perform the 3D reconstruction. During the preprocessing, only a few images require manual intervention while the rest can be processed automatically. To verify the modeling accuracy of the proposed method, a column that has been destroyed is selected as a target for 3D reconstruction. The results show that compared with the traditional method, the modeling efficiency of the fast reconstruction method is doubled. In addition, the modeling accuracy is 97.65%, which is comparable to the modeling accuracy of the traditional method (97.73%); as well, by comparing the point clouds, the alignment between the two models is tremendously close, with tiny difference. The proposed rapid reconstruction method can be applied in emergency scenarios, such as rapid assessment in post-disaster situations. Full article
(This article belongs to the Special Issue Damage Detection Based on Smartphones in Buildings)
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21 pages, 2827 KiB  
Article
Shaking Table Tests and Validation of Multi-Modal Sensing and Damage Detection Using Smartphones
by Ruicong Han and Xuefeng Zhao
Buildings 2021, 11(10), 477; https://doi.org/10.3390/buildings11100477 - 14 Oct 2021
Cited by 12 | Viewed by 3013
Abstract
Structural health monitoring (SHM) systems using modal- and vibration-based methods, particularly wireless systems, have been widely investigated in relation to the monitoring of damage states in civil infrastructures such as bridges and buildings. Unlike many current efforts in developing wireless sensors, one can [...] Read more.
Structural health monitoring (SHM) systems using modal- and vibration-based methods, particularly wireless systems, have been widely investigated in relation to the monitoring of damage states in civil infrastructures such as bridges and buildings. Unlike many current efforts in developing wireless sensors, one can instead leverage the suite of sensors, network transmission, data storage, and embedded processing capabilities built into modern smartphones for SHM. The objective of this work was to assess and validate the use of smartphones for the monitoring of artificial damage states in a three-story steel frame model subjected to shaking table-induced earthquake excitations. The steel frame was a 2D structure with six rotary viscous dampers installed at the beam–column joints, which were used for simulating different damage states at their respective locations; the columns were also replaced with ones of reduced cross-sectional areas to further emulate damage. In addition to instrumenting the frame with conventional tethered sensors, Apple iPhones (pre-loaded with customized smartphone apps to record acceleration and inter-story displacement) were also installed. Shaking table tests were then conducted on the undamaged and damaged frames, while conventional sensors’ and smartphones’ responses were collected and compared. Wavelet packet decomposition was employed to analyze the acceleration data to detect damage in two different cases. Structural displacements were also computed from acceleration measurements and compared with displacement measurements to further validate the quality of smartphone sensor measurements. Full article
(This article belongs to the Special Issue Damage Detection Based on Smartphones in Buildings)
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