Aerial Crack View: Crack monitoring in concrete bridges through image processing acquired by UAV

2019 ◽  
Author(s):  
Jónatas Valença ◽  
Bruno Santos ◽  
André Araújo ◽  
Eduardo Júlio
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Human Error ◽  
State Of The Art ◽  
Minimum Cost ◽  
Cost Effective ◽  
Concrete Bridges ◽  
Crack Monitoring ◽  
Automatic Methods ◽  
Existing Bridges

<p>The development of methods aiming at assuring the maintenance of existing bridges and at minimum cost is a priority. Inspection and diagnosis methods are usually planned based on periodic visual inspections. In the case of concrete bridges, the characterization of cracking plays an extremely important role to monitor their structural health. However, current inspections are rudimentary and work intensive (inspectors take hand notes and pictures that later upload to reports and management software), prone to human error (since cracks are detected by inspectors onsite) and expensive (‘underbridge’ trucks are required). Therefore, alternative automatic methods will represent a breakthrough regarding the state-of-the-art.</p><p>In this paper the method Aerial Crack View (ACV) is presented. ACV is a semi-automatic method to detect cracks in concrete bridges, based on processing images acquired by terrestrial working-station (TWS) and unmanned aerial vehicles (UAVs). The method was applied in Rainha Santa Isabel Bridge, in Coimbra, Portugal, for validation. ACV revels advantages such as, fastness because the detection of cracks is automatically conducted and cost-effective since UAVs can provide information on less accessible elements dismissing expensive access means.</p><p>.</p>

scholarly journals INTERACTIVE CADASTRAL BOUNDARY DELINEATION FROM UAV DATA

2018 ◽  
Vol IV-2 ◽  
pp. 81-88 ◽  
Author(s):  
S. Crommelinck ◽  
B. Höfle ◽  
M. N. Koeva ◽  
M. Y. Yang ◽  
G. Vosselman
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Land Tenure ◽  
State Of The Art ◽  
Cost Effective ◽  
Contour Detection ◽  
Local Boundary ◽  
Manual Delineation ◽  
Cost Paths ◽  
Cadastral Surveying ◽  
Least Cost Paths

Unmanned aerial vehicles (UAV) are evolving as an alternative tool to acquire land tenure data. UAVs can capture geospatial data at high quality and resolution in a cost-effective, transparent and flexible manner, from which visible land parcel boundaries, i.e., cadastral boundaries are delineable. This delineation is to no extent automated, even though physical objects automatically retrievable through image analysis methods mark a large portion of cadastral boundaries. This study proposes (i) a methodology that automatically extracts and processes candidate cadastral boundary features from UAV data, and (ii) a procedure for a subsequent interactive delineation. Part (i) consists of two state-of-the-art computer vision methods, namely gPb contour detection and SLIC superpixels, as well as a classification part assigning costs to each outline according to local boundary knowledge. Part (ii) allows a user-guided delineation by calculating least-cost paths along previously extracted and weighted lines. The approach is tested on visible road outlines in two UAV datasets from Germany. Results show that all roads can be delineated comprehensively. Compared to manual delineation, the number of clicks per 100&amp;thinsp;m is reduced by up to 86&amp;thinsp;%, while obtaining a similar localization quality. The approach shows promising results to reduce the effort of manual delineation that is currently employed for indirect (cadastral) surveying.

scholarly journals Robust Detection, Classification and Localization of Defects in Large Photovoltaic Plants Based on Unmanned Aerial Vehicles and Infrared Thermography

2020 ◽  
Vol 10 (17) ◽  
pp. 5948 ◽  
Author(s):  
Alberto Fernández ◽  
Rubén Usamentiaga ◽  
Pedro de Arquer ◽  
Miguel Ángel Fernández ◽  
D. Fernández ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Infrared Thermography ◽  
Large Scale ◽  
Cost Effective ◽  
Electrical Performance ◽  
Performance Measurements ◽  
Robust Detection ◽  
Aerial Vehicles ◽  
Pv Modules

The efficiency and profitability of photovoltaic (PV) plants are highly controlled by their operation and maintenance (O&M) procedures. Today, the effective diagnosis of any possible fault of PV plants remains a technical and economic challenge, especially when dealing with large-scale PV plants. Currently, PV plant monitoring is carried out by either electrical performance measurements or image processing. The first approach presents limited fault detection ability, it is costly and time-consuming, and it is incapable of fast identification of the physical location of the fault. In the second approach, Infrared Thermography (IRT) imaging has been used for the characterization of PV module failures, but their setup and processing are rather complex and an experienced technician is required. The use of Unmanned Aerial Vehicles (UAVs) for IRT imaging of PV plants for health status monitoring of PV modules has been identified as a cost-effective approach that offers 10–-15 fold lower inspection times than conventional techniques. However, previous works have not performed a comprehensive approach in the context of automated UAV inspection using IRT. This work provides a fully automated approach for the: (a) detection, (b) classification, and (c) geopositioning of the thermal defects in the PV modules. The system has been tested on a real PV plant in Spain. The obtained results indicate that an autonomous solution can be implemented for a full characterization of the thermal defects.

Nonlinear inversion of subsidence signatures induced by tunnels detected with surface and remote sensing measurements

2019 ◽  
Vol 38 (7) ◽  
pp. 550-553
Author(s):  
Jorge Parra ◽  
Jonathan Parra ◽  
Marius Necsoiu
Keyword(s):  
Remote Sensing ◽  
State Of The Art ◽  
Cost Effective ◽  
Forward Modeling ◽  
Nonlinear Inversion ◽  
Inversion Algorithm ◽  
Gap Parameter ◽  
Remote Sensing Methods ◽  
Good Agreement

The state of the art in predicting tunnel-induced subsidence settlements is based on empirical and analytical methods. Empirical methods are useful when the equations are implemented with host medium properties where tunnels have been excavated. Analytical solutions can predict tunneling-induced ground movements, with the predictions accounting for tunnel radius and depth as well as ground-loss parameters in soft soils. The drawback is that these methods require human intervention, as each model must be adjusted manually by the interpreter until the model signature fits the observed data. It would take tremendous effort to evaluate displacement anomalies detected by remote sensing methods using such forward-modeling methods. Therefore, we present a method based on an inversion algorithm that automatically inverts subsidence signatures for tunnel radius, depth, Poisson's ratio, and the gap parameter. It is an advancement over conventional methods because it does not require a first guess, and it can invert several subsidence signatures in a matter of minutes. The algorithm, coupled with remote sensing-based displacement maps, is a cost-effective solution in operational characterization of displacement anomalies. We demonstrate that observed and predicted subsidence signatures are in good agreement with existing tunnel data in uniform clay and that the inversion parameters correspond to those predicted with forward modeling alone.

scholarly journals Value of Information (VoI) for the Chloride Content in Reinforced Concrete Bridges

2020 ◽  
Vol 10 (2) ◽  
pp. 567
Author(s):  
Ivan Zambon ◽  
Monica Patricia Santamaria Ariza ◽  
José Campos e Matos ◽  
Alfred Strauss
Keyword(s):  
Reinforced Concrete ◽  
Minimum Cost ◽  
Cost Effective ◽  
Chloride Content ◽  
Concrete Bridges ◽  
Chloride Ingress ◽  
Maximum Cost ◽  
Initiation Phase ◽  
Reinforced Concrete Bridges ◽  
The Cost

The corrosion of reinforcement caused by chloride ingress significantly reduces the length of the service life of reinforced concrete bridges. Therefore, the condition of bridges is periodically inspected by specially trained engineers regarding the possible occurrence of reinforcement corrosion. Their main goal is to ensure that the structure can resist mechanical and environmental loads and offer a satisfactory level of safety and serviceability. In the course of assessment, measuring the chloride content, through which corrosion could be anticipated and prevented, presents a possible alternative to visual inspections and corrosion tests that can only indicate already existing corrosion. It is hard to determine the cost-effectiveness and actual value of chloride content measurements in a simple and straightforward way. Thus, the main aim of the paper was to study the value of newly gained information, which is obtained when a chloride content in reinforced concrete bridges is measured. This value was here analyzed through the pre-posterior analysis of the cost of measurement and repair, taking into account different types of exposure and material properties for a general case. The research focus was set on the initiation phase in which there are no visible damages. A relative comparison of costs is presented, where the cost of possible reactive/proactive repair was compared with the maximum cost of measurement, while the measurement is still cost effective. The analysis showed a high influence of the initial probability of depassivation on the maximum cost of the cost-effective measurement, as well as a nonreciprocal relation of the minimum cost of cost-effective reactive repair with the measurement accuracy.

scholarly journals In Search of a Better Heuristic Algorithm for Simplification of Switching Function - A Challenge to ESPRESSO

2012 ◽  
Vol 3 (1) ◽  
pp. 121-126
Author(s):  
Subhajit Guha ◽  
Uma Mitra ◽  
Pinki Dey ◽  
Samar Sen Sarma
Keyword(s):  
Heuristic Algorithm ◽  
Heuristic Algorithms ◽  
State Of The Art ◽  
Fitness Function ◽  
Minimum Cost ◽  
Cost Effective ◽  
Switching Function ◽  
Combinatorial Explosion ◽  
New Approach ◽  
Intractable Problem

Finding minimum cost switching function is a n intractable problem. The number of prime implicant of a typical switching function is of the order of 3n [5][13]. When fitness function with respect to cost is the only goal, the existing algorithms require faces combinatorial explosion [20]. The allurement of approximation and heuristic algorithms in this area has already generated ESPRESSO algorithm. We find that our algorithm presented here is at least equal or more cost effective than the ESPRESSO algorithm. The paper shows a new approach for attainment of our claim. We hope the algorithm presented here is a new attachment to the existing state of the art technology.

Automated Characterization of Atheromatous Plaque in Intravascular Ultrasound Images Using Neuro Fuzzy Classifier

2012 ◽  
Vol 58 (4) ◽  
pp. 425-431 ◽  
Author(s):  
D. Selvathi ◽  
N. Emimal ◽  
Henry Selvaraj
Keyword(s):  
Intravascular Ultrasound ◽  
Human Error ◽  
Human Life ◽  
Classification Performance ◽  
Atheromatous Plaque ◽  
Ultrasound Images ◽  
Fuzzy Classifier ◽  
Neuro Fuzzy ◽  
Calcified Tissues

Abstract The medical imaging field has grown significantly in recent years and demands high accuracy since it deals with human life. The idea is to reduce human error as much as possible by assisting physicians and radiologists with some automatic techniques. The use of artificial intelligent techniques has shown great potential in this field. Hence, in this paper the neuro fuzzy classifier is applied for the automated characterization of atheromatous plaque to identify the fibrotic, lipidic and calcified tissues in Intravascular Ultrasound images (IVUS) which is designed using sixteen inputs, corresponds to sixteen pixels of instantaneous scanning matrix, one output that tells whether the pixel under consideration is Fibrotic, Lipidic, Calcified or Normal pixel. The classification performance was evaluated in terms of sensitivity, specificity and accuracy and the results confirmed that the proposed system has potential in detecting the respective plaque with the average accuracy of 98.9%.

Monitoring of Petroleum-Related Environmental Contamination Using Fluorescence Fingerprinting

2006 ◽  
Vol 1 (2) ◽  
Author(s):  
P. Literathy ◽  
M. Quinn
Keyword(s):  
Environmental Contamination ◽  
Statistical Evaluation ◽  
Oil Pollution ◽  
Emission Spectra ◽  
Cost Effective ◽  
Petroleum Products ◽  
Danube River ◽  
Aromatic Components ◽  
Analytical Approaches

Petroleum and its refined products are considered the most complex contaminants frequently impacting the environment in significant quantities. They have heterogeneous chemical composition and alterations occur during environmental weathering. No single analytical method exists to characterize the petroleum-related environmental contamination. For monitoring, the analytical approaches include gravimetric, spectrometric and chromatographic methods having significant differences in their selectivity, sensitivity and cost-effectiveness. Recording fluorescence fingerprints of the cyclohexane extracts of the water, suspended solids, sediment or soil samples and applying appropriate statistical evaluation (e.g. by correlating the concatenated emission spectra of the fingerprints of the samples with arbitrary standards (e.g. petroleum products)), provides a powerful, cost-effective analytical tool for characterization of the type of oil pollution and detecting the most harmful aromatic components of the petroleum contaminated matrix. For monitoring purposes, the level of the contamination can be expressed as the equivalent concentration of an appropriate characteristic standard, based on the fluorescence intensities at the relevant characteristic wavelengths. These procedures are demonstrated in the monitoring of petroleum-related pollution in the water and suspended sediment in the Danube river basin

Green Synthesis and Spectroscopic Studies of Ag-rGO Nanocomposites for Highly Selective Mercury (II) Sensing

2018 ◽  
Vol 9 (1) ◽  
pp. 101-108 ◽  
Author(s):  
Shubhangi J. Mane-Gavade ◽  
Sandip R. Sabale ◽  
Xiao-Ying Yu ◽  
Gurunath H. Nikam ◽  
Bhaskar V. Tamhankar
Keyword(s):  
Green Synthesis ◽  
Color Change ◽  
Limit Of Detection ◽  
Aqueous Media ◽  
Suitable Condition ◽  
Cost Effective ◽  
Spectroscopic Studies ◽  
Calibration Plot ◽  
First Time

Introduction: Herein we report the green synthesis and characterization of silverreduced graphene oxide nanocomposites (Ag-rGO) using Acacia nilotica gum for the first time. Experimental: We demonstrate the Hg2+ ions sensing ability of the Ag-rGO nanocomposites form aqueous medium. The developed colorimetric sensor method is simple, fast and selective for the detection of Hg2+ ions in aqueous media in presence of other associated ions. A significant color change was noticed with naked eye upon Hg2+ addition. The color change was not observed for cations including Sr2+, Ni2+, Cd2+, Pb2+, Mg2+, Ca2+, Fe2+, Ba2+ and Mn2+indicating that only Hg2+ shows a strong interaction with Ag-rGO nanocomposites. Under the most suitable condition, the calibration plot (A0-A) against concentration of Hg2+ was linear in the range of 0.1-1.0 ppm with a correlation coefficient (R2) value 0.9998. Results & Conclusion The concentration of Hg2+ was quantitatively determined with the Limit of Detection (LOD) of 0.85 ppm. Also, this method shows excellent selectivity towards Hg2+ over nine other cations tested. Moreover, the method offers a new cost effective, rapid and simple approach for the detection of Hg2+ in water samples.

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