| 1. |
- Belloni, Valeria, et al.
(författare)
-
Cosmo-skymed range measurements for displacement monitoring using amplitude persistent scatterers
- 2020
-
Ingår i: IGARSS 2020 - 2020 IEEE international geoscience and remote sensing symposium. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 2495-2498
-
Konferensbidrag (refereegranskat)abstract
- Synthetic Aperture Radar (SAR) satellite data are widely used to monitor deformation phenomena impacting the Earth's surface (e.g. landslides, glacier motions, subsidence, and volcano deformations) and infrastructures (e.g. bridges, dams, buildings). The analysis is generally performed using the Differential SAR Interferometry (DInSAR) technique that exploits the phase information of SAR data. However, this technique suffers for lack of coherence among the considered stack of images, and it can only be adopted to monitor slow deformation phenomena. In the field of geohazards monitoring and glacier melting, the Offset Tracking technique has been also widely investigated. This approach is based on the amplitude information only but it reaches worse accuracy compared to DInSAR. To overcome the limitations of DInSAR and Offset Tracking, in the last decade, a new technique called Imaging Geodesy has been investigated exploiting the amplitude information and the precise orbit of the modern SAR platforms (i.e. TerraSAR-X, COSMO-SkyMed). In this study, an investigation of using COSMO-SkyMed data for Earth surface monitoring was performed. The developed approach was applied to a set of imagery acquired over the Corvara (Northern Italy) area, which is affected by a fast landslide with yearly displacements up to meters. Specifically, two well identifiable and stable human-made Amplitude Persistent Scatterers (APSs) were considered to estimate the residual errors of COSMO-SkyMed sensor during the acquisition period between 2010 and 2015. Then, the same methodology was applied to estimate the displacement of a Corner Reflector (CR) located in the landslide area. Finally, the results were compared to the available GPS reference trend showing a good agreement.
|
|
| 2. |
- Belloni, Valeria, et al.
(författare)
-
Crack Monitoring from Motion (CMfM) : Crack detection and measurement using cameras with non-fixed positions
- 2023
-
Ingår i: Automation in Construction. - : Elsevier BV. - 0926-5805 .- 1872-7891. ; 156
-
Tidskriftsartikel (refereegranskat)abstract
- The assessment of cracks in civil infrastructures commonly relies on visual inspections carried out at night, resulting in limited inspection time and an increased risk of crack oversight. The Digital Image Correlation (DIC) technique, employed in structural monitoring, requires stationary cameras for image collection, which proves challenging for long-term monitoring. This paper describes the Crack Monitoring from Motion (CMfM) methodology for automatically detecting and measuring cracks using non-fixed cameras, combining Convolutional Neural Networks and photogrammetry. Through evaluation using images obtained from laboratory tests on concrete beams and subsequent comparison with DIC and a pointwise sensor, CMfM demonstrates accurate crack width computation within a few hundredths of a millimetre when compared to the sensor. This method exhibits potential for effectively monitoring temporal crack evolution using non-fixed cameras.
|
|
| 3. |
- Belloni, Valeria, et al.
(författare)
-
py2DIC : A New Free and Open Source Software for Displacement and Strain Measurements in the Field of Experimental Mechanics
- 2019
-
Ingår i: Sensors. - : MDPI. - 1424-8220. ; 19:18
-
Tidskriftsartikel (refereegranskat)abstract
- Thanks to the advances in computer power, memory storage and the availability of low-cost and high resolution digital cameras, Digital Image Correlation (DIC) is currently one of the most used optical and non-contact techniques for measuring material deformations. A free and open source 2D DIC software, named py2DIC, was developed at the Geodesy and Geomatics Division of the Sapienza University of Rome. Implemented in Python, the software is based on the template matching method and computes the 2D displacements and strains of samples subjected to mechanical loading. In this work, the potentialities of py2DIC were evaluated by processing two different sets of experimental data and comparing the results with other three well known DIC software packages Ncorr, Vic-2D and DICe. Moreover, an accuracy assessment was performed comparing the results with the values independently measured by a strain gauge fixed on one of the samples. The results demonstrate the possibility of successfully characterizing the deformation mechanism of the investigated materials, highlighting the pros and cons of each software package.
|
|
| 4. |
- Belloni, Valeria, et al.
(författare)
-
Tack Project: Tunnel and bridge automatic crack monitoring using deep learning and photogrammetry
- 2020
-
Ingår i: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. - : Copernicus GmbH. ; , s. 741-745
-
Konferensbidrag (refereegranskat)abstract
- Civil infrastructures, such as tunnels and bridges, are directly related to the overall economic and demographic growth of countries. The aging of these infrastructures increases the probability of catastrophic failures that results in loss of lives and high repair costs; all over the world, these factors drive the need for advanced infrastructure monitoring systems. For these reasons, in the last years, different types of devices and innovative infrastructure monitoring techniques have been investigated to automate the process and overcome the main limitation of standard visual inspections that are used nowadays. This paper presents some preliminary findings of an ongoing research project, named TACK, that combines advanced deep learning techniques and innovative photogrammetric algorithms to develop a monitoring system. Specifically, the project focuses on the development of an automatic procedure for crack detection and measurement using images of tunnels and bridges acquired with a mobile mapping system. In this paper, some preliminary results are shown to investigate the potential of a deep learning algorithm in detecting cracks occurred in concrete material. The model is a CNN (Convolutional Neural Network) based on the U-Net architecture; in this study, we tested the transferability of the model that has been trained on a small available labeled dataset and tested on a large set of images acquired using a customized mobile mapping system. The results have shown that it is possible to effectively detect cracks in unseen imagery and that the primary source of errors is the false positive detection of crack-like objects (i.e., contact wires, cables and tile borders).
|
|
| 5. |
- Sjölander, Andreas, Ph.D, 1983-, et al.
(författare)
-
Dataset to track concrete cracking using DIC with fixed and moving camera
- 2022
-
Annan publikationabstract
- Today, Digital Image Correlation (DIC) has become a standardized method to track displacements and crack-propagation of civil engineering structures in a laboratory environment. The benefit of using DIC over other standard methods is that it is contact-free and only requires a standard DSLR camera. Moreover, the displacement can be tracked over the entire image, which is a great advantage compared to the limitations of standard sensors that only measure the deformation at a specific point. In standard DIC, the displacements are directly extracted from the images. Hence, the position of the camera must be fixed during the entire test. Therefore, DIC is commonly used in a laboratory environment to measure displacement during short-term testing, e.g. testing of the structural capacity of a reinforced concrete beam. The data presented in this paper was used to verify a newly developed and innovative photogrammetric algorithm, Deformation from Motion (DfM). This algorithm overcomes the standard limitation of traditional DIC and enables high-accuracy measurements to be performed using a camera with no fixed position. As a reference, the crack propagation was on one side monitored with a LVDT and on the other side with a camera with a fixed position. During testing, a moving camera also captured imagery on both sides.
|
|
| 6. |
- Sjölander, Andreas, Ph.D, 1983-, et al.
(författare)
-
Experimental dataset to assess the structural performance of cracked reinforced concrete using Digital Image Correlation techniques with fixed and moving cameras
- 2023
-
Ingår i: Data in Brief. - : Elsevier BV. - 2352-3409. ; 51
-
Tidskriftsartikel (refereegranskat)abstract
- The infrastructure is in many countries aging and continuous maintenance is required to ensure the safety of the structures. For concrete structures, cracks are a part of the structure's life cycle. However, assessing the structural impact of cracks in reinforced concrete is a complex task. The purpose of this paper is to present a dataset that can be used to verify and compare results of the measured crack propagation in concrete with the well-known Digital Image Correlation (DIC) technique and with Crack Monitoring from Motion (CMfM), a novel photogrammetric algorithm that enables high accurate measurements with a non-fixed camera. Moreover, the data can be used to investigate how existing cracks in reinforced concrete could be implemented in a numerical model.The first potential area to use this dataset is structural engineering. The data can be used to verify non-linear material models used in a finite element (FE) software to simulate the structural response of reinforced concrete. In particular, the data can be used to investigate how existing cracks should be modelled in a FE model. The second potential area is within image processing techniques with a focus on DIC. Until recently, DIC suffered from one major disadvantage; the camera must be fixed during the entire period of data collection. Naturally, this decreases the flexibility and potential of using DIC outside the laboratory. In a recently published paper [1], an innovative photogrammetric algorithm (CMfM) that enables the use of a moving camera, i.e. a camera that is not fixed during data acquisition, was presented. The imagery of this dataset [2] was used to verify the potential of this algorithm and could be used to validate other approach for non-fixed cameras.The dataset presented in this paper includes data collected from a three-point bending test performed in a laboratory environment on uncracked and pre-cracked reinforced concrete beams. Structural testing was performed using a displacement-controlled set-up, which continuously recorded the force and the vertical displacement of a centric-placed loading piston. First, the response of three uncracked beams was recorded. Thereafter, photos of the resulting cracks were taken, and a detailed mapping was presented. Material properties for the concrete, e.g., compressive strength, are presented together with testing of the tensile capacity of the reinforcement and a compressive test of the soft fiber boards used at the support to ensure good contact between steel and concrete. Then, the structural response of the pre-cracked beams was tested. During this test, four fixed cameras were used to monitor the crack propagation at different locations on the beam. Images are presented at the start of the load sequences and at pre-defined load stops during the testing. Hence, the crack opening captured in the images can be correlated to the force-displacement data. Moreover, a non-fixed camera was used to capture additional imagery at the location of each fixed camera.
|
|
| 7. |
- Sjölander, Andreas, Ph.D, 1983-, et al.
(författare)
-
Monitoring of structural performance of cracked reinforced concrete using DIC and CMfM
- 2023
-
Annan publikationabstract
- This dataset contains data from three-point bending test of uncracked and cracked reinforced concrete in a laboratory environment.First, uncracked beams were tested to a load level close to the maximum capacity. The cracks were thereafter mapped before the beams were tested until failure. During the second test, the crack propagation was monitored using four fixed cameras and one non-fixed camera. The data contains measured force and displacement from the test, imagery from the fixed and non-fixed cameras as well as documentation of initial cracks and structural testing of reinforcement. A full description of the dataset is provided in the paper "Experimental dataset to assess the structural performance of cracked reinforced concrete using Digital Image Correlation techniques with fixed and moving cameras" published in Data in Brief.
|
|
| 8. |
- Sjölander, Andreas, 1983-, et al.
(författare)
-
TACK – an autonomous inspection system for tunnels
- 2022
-
Ingår i: Proceedings of the 47th ITA-AITES World Tunnel Congress, Copenhagen, Denmark, 5-8 September, 2022.
-
Konferensbidrag (refereegranskat)abstract
- Tunnels in hard rock are typically supported with a thin layer of fibre-reinforced shotcrete in combination with rock bolts. Cracks in the shotcrete could lead to corrosion of the fibres, which reduces the residual strength and could lead to downfall of shotcrete. Therefore, routine inspections are carried out to maintain a safe tunnel. Today, visual inspections are mainly performed, which is timeconsuming and prone to human errors. TACK (Tunnel Automatic CracK Detection) is a research project that aims to develop an autonomous tunnel inspection method based on a hybrid approach of photogrammetry and deep-learning. Data from the tunnel is collected with a mobilemapping system equipped with LiDAR sensors and high-resolution cameras. LiDAR data is used to create a 3D model of the tunnel. Then, a deep-learning approach is used to automatically detect cracks in the acquired images. Once the cracks are detected, a novel photogrammetric algorithm is used to calculate the geometric features of the cracks, i.e. length and width. Finally, the risk associated with the cracks is assessed, and critical sections in need of repair or visual inspections can be pointed out. This paper presents a case-study based on data collected from one tunnel.
|
|
| 9. |
- Sjölander, Andreas, Ph.D, 1983-, et al.
(författare)
-
Towards Automated Inspections of Tunnels: A Review of Optical Inspections and Autonomous Assessment of Concrete Tunnel Linings
- 2023
-
Ingår i: Sensors. - : MDPI. - 1424-8220. ; 23:6, s. 3189-3189
-
Tidskriftsartikel (refereegranskat)abstract
- In recent decades, many cities have become densely populated due to increased urbanization, and the transportation infrastructure system has been heavily used. The downtime of important parts of the infrastructure, such as tunnels and bridges, seriously affects the transportation system’s efficiency. For this reason, a safe and reliable infrastructure network is necessary for the economic growth and functionality of cities. At the same time, the infrastructure is ageing in many countries, and continuous inspection and maintenance are necessary. Nowadays, detailed inspections of large infrastructure are almost exclusively performed by inspectors on site, which is both time-consuming and subject to human errors. However, the recent technological advancements in computer vision, artificial intelligence (AI), and robotics have opened up the possibilities of automated inspections. Today, semiautomatic systems such as drones and other mobile mapping systems are available to collect data and reconstruct 3D digital models of infrastructure. This significantly decreases the downtime of the infrastructure, but both damage detection and assessments of the structural condition are still manually performed, with a high impact on the efficiency and accuracy of the procedure. Ongoing research has shown that deep-learning methods, especially convolutional neural networks (CNNs) combined with other image processing techniques, can automatically detect cracks on concrete surfaces and measure their metrics (e.g., length and width). However, these techniques are still under investigation. Additionally, to use these data for automatically assessing the structure, a clear link between the metrics of the cracks and the structural condition must be established. This paper presents a review of the damage of tunnel concrete lining that is detectable with optical instruments. Thereafter, state-of-the-art autonomous tunnel inspection methods are presented with a focus on innovative mobile mapping systems for optimizing data collection. Finally, the paper presents an in-depth review of how the risk associated with cracks is assessed today in concrete tunnel lining.
|
|
| 10. |
- Valeria, Belloni, et al.
(författare)
-
TACK project : Tunnel Automatic CracK monitoring using deep learning and photogrammetry
- 2022
-
Ingår i: Earth Observation for Environmental Monitoring 41<sup>st</sup> EARSeL Symposium, Paphos, Cyprus, 13-16 September 2022.
-
Konferensbidrag (refereegranskat)abstract
- Crack detection and measurement are standard procedures during infrastructure inspections all over the world. Traditionally, infrastructures are only visually inspected. To accomplish this, tunnels must be closed and inspections are carried out during the night to minimize infrastructure downtime. Also, the limited time and the length of the system make it impossible to accurately inspect tunnels which increases the risk that cracks are not detected. Recently, inspections have been carried out using semi-automatic methods where a mobile mapping equipment is used to capture the scene and reconstruct the 3D model (the socalled digital twin) of tunnels using geomatics sensors (visible and infrared cameras, LiDAR, IMU). Then, the digital twin and the images are manually analysed to find cracks and mark their extent. Due to the large amount of data, the method is time-consuming, inefficient and affected by errors. The aim of this work is to improve efficiency and accuracy of inspections.
|
|
