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Structural Health Monitoring (SHM) of Civil Structures

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ISBN: 9783038427834 9783038427841 Year: Pages: X, 490 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: General and Civil Engineering
Added to DOAB on : 2018-04-20 14:47:20
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At the current time of writing, the American Society of Civil Engineers (ASCE) has awarded American infrastructure a grade of D+, meaning poor and at risk. Part of the reason for the low grade is due to the rapid deterioration of structural integrity and the inability of most places to safely meet future demands. Deficiencies in these areas may be remediated by advancements in structural health monitoring (SHM) technologies that provide sensing systems to automatically and economically diagnose structural integrity. In a sense, SHM technologies will help pave the way to intelligent structures that are able to detect damage by themselves and even warn occupants of any danger due to impending structural failure. Engineering sensors and developing smart algorithms for SHM often involves the close collaboration of a surprisingly large breadth of specialties. In this book, we have collected a thin but representative slice of the most recent research in SHM, and hope that the reader will gain an inspiring view of today’s research landscape and a notion of what is to come.

Intelligent Sensing Technologies for Nondestructive Evaluation

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ISBN: 9783038428770 9783038428787 Year: Pages: VIII, 272 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: General and Civil Engineering
Added to DOAB on : 2018-05-08 13:27:51
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Nondestructive evaluation (NDE) sensing has progressed significantly in the past few years. In particular, smart sensors play an increasingly important role in structural damage detection. There is growing progress in the performance of strategic sensors, such as piezoelectric sensors, as well as noncontact sensors, such as air-coupled transducers, magnetic flux leakage sensors and pulsed laser ultrasonic propagation applications. The most progress has been made in the development of application software for all technologies. We are now able to enhance damage resolutions and then focus on damage visualization in many applications.This Special Issue highlights advances in the development, testing, and use of damage visualization tools for smart sensor-based nondestructive evaluations. Topics covered include: • New developments in smart sensing-based nondestructive evaluations• Magnetic flux leakage sensors• Laser scanning-based ultrasonic propagation sensors• Piezoelectric sensors• Air-coupled transducers• Damage detection and visualization

Smart Sensors for Structural Health Monitoring

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ISBN: 9783039217588 9783039217595 Year: Pages: 342 DOI: 10.3390/books978-3-03921-759-5 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:16
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Smart sensors are technologies designed to facilitate the monitoring operations. For instance, power consumption can be minimized through on-board processing and smart interrogation algorithms, and state detection enhanced through collaboration between sensor nodes. Applied to structural health monitoring, smart sensors are key enablers of sparse and dense sensor networks capable of monitoring full-scale structures and components. They are also critical in empowering operators with decision making capabilities. The objective of this Special Issue is to generate discussions on the latest advances in research on smart sensing technologies for structural health monitoring applications, with a focus on decision-enabling systems. This Special Issue covers a wide range of related topics such as innovative sensors and sensing technologies for crack, displacement, and sudden event monitoring, sensor optimization, and novel sensor data processing algorithms for damage and defect detection, operational modal analysis, and system identification of a wide variety of structures (bridges, transmission line towers, high-speed trains, masonry light houses, etc.).

Keywords

optical crack growth sensor --- digital sampling moiré --- 2D crack growth --- calibration --- concrete crack --- feature extraction --- mapping construction --- fuzzy classification --- rotary ultrasonic array --- bending stiffness --- damage identification --- environmental noise --- bridge --- test vehicle --- structural impact monitoring --- sensors distribution optimization --- NSGA-II --- energy analysis of wavelet band --- principal component analysis --- transmission tower --- settlement --- wind force --- acceleration --- modal frequencies --- sudden event monitoring --- wireless smart sensors --- demand-based nodes --- event-triggered sensing --- data fusion --- patch antenna --- sensor --- structural health monitoring --- crack identification --- resonant frequency --- damage identification --- sensor optimization --- Virtual Distortion Method (VDM) --- Particle Swarm Optimization (PSO) algorithm --- sensitivity --- structural health monitoring --- piezoelectric wafer active sensors --- active sensing --- passive sensing --- damage detection --- acoustic emission --- uniaxial stress measurement --- structural steel members --- amplitude spectrum --- phase spectrum --- shear-wave birefringence --- acoustoelastic effect --- damage detection --- smartphones --- steel frame --- shaking table tests --- wavelet packet decomposition --- low-velocity impacts --- strain wave --- impactor stiffness --- data processing --- feature selection --- impact identification --- crack --- strain --- distributed dense sensor network --- structural health monitoring --- fibre bundle --- reflective optical sensor --- tip clearance --- turbine --- aero engine --- principal component analysis --- space window --- time window --- damage detection --- length effect --- stress detection --- electromagnetic oscillation --- steel strand --- concrete structures --- SHM --- stretching method --- model updating --- displacement sensor --- helical antenna --- resonant frequency --- perturbation theory --- normal mode --- wheel minor defect --- high-speed train --- online wayside detection --- Bayesian blind source separation --- FBG sensor array

Non-destructive Testing of Materials in Civil Engineering

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ISBN: 9783039216901 9783039216918 Year: Pages: 448 DOI: 10.3390/books978-3-03921-691-8 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:16
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This book was proposed and organized as a means to present recent developments in the field of nondestructive testing of materials in civil engineering. For this reason, the articles highlighted in this editorial relate to different aspects of nondestructive testing of different materials in civil engineering—from building materials to building structures. The current trend in the development of nondestructive testing of materials in civil engineering is mainly concerned with the detection of flaws and defects in concrete elements and structures, and acoustic methods predominate in this field. As in medicine, the trend is towards designing test equipment that allows one to obtain a picture of the inside of the tested element and materials. From this point of view, interesting results with significance for building practices have been obtained

Keywords

non-destructive testing --- masonry structures --- strengthening --- ultrasonic tomography --- adhesion assessment --- autoclaved aerated concrete (AAC) --- compressive strength --- shape and size of specimen --- moisture of AAC --- ultrasonic testing --- gantry crane --- RMF technique --- civil engineering --- fibre-cement boards --- non-destructive testing --- acoustic emission --- degree of degradation --- thermovision --- active thermography --- thermal contrast --- defect detection --- location of inclusions --- non-destructive testing --- materials research --- building partition --- cement-based composites --- fiber cement boards --- durability --- ultrasound measurements --- spun concrete --- micro-computed tomography --- nanoindentation --- deconvolution --- mathematical morphology --- non-destructive evaluation --- structural damage --- natural frequency --- singular value truncation --- multiple feedbacks --- data noise --- NDT methods --- rebar location --- eddy-current method --- GPR method --- concrete --- concrete mix design --- concrete strength prediction --- data mining --- machine learning --- timber structures --- non-destructive methods --- ultrasonic wave --- stress wave --- drilling resistance --- X-ray micro-computed tomography --- waste brick dust --- adsorption --- lead --- cesium --- surface complexation --- precipitation --- solid-state NMR spectroscopy --- Lamb waves --- scanning laser vibrometry --- adhesive joints --- non-destructive testing --- damage detection --- excitation frequency --- nondestructive testing --- thermography --- monitoring of structures --- reinforced concrete chimney --- corrosion processes --- service life of a structure --- viscoelastic parameters --- creep test --- fatigue tests --- asphalt mixtures --- Burgers model --- four point bending beam --- pattern recognition --- acoustic emission --- Structural Health Monitoring --- brittle fracture --- diagnostics --- non-destructive testing --- reinforced concrete grandstand stadium --- vibration analysis --- crowd-induced excitation --- structural tuning --- concrete slabs and floorings --- horizontal casting --- compressive strength --- ultrasonic tests --- fibre-cement boards --- non-destructive testing --- acoustic emission --- artificial neural networks --- SEM --- non-destructive method --- damage --- mercury intrusion porosimetry --- X-ray computed tomography --- acoustic emission AE --- acoustic spectrum --- quasi brittle cement composites --- destruction process --- resistance measurement --- wood moisture sensing --- non-destructive testing --- moisture safety --- cellulose fibre cement boards --- microstructure --- nanoindentation --- SEM-EDS analysis --- temperature --- concrete elements --- concrete strength --- reinforced concrete tanks --- concrete corrosion --- sulphate corrosion --- ultrasound tests --- rebound hammer --- SilverSchmidt --- concrete --- compressive strength --- non-destructive testing --- non-destructive testing --- diagnostic --- acoustic methods --- ultrasound --- building materials --- defects

Learning to Understand Remote Sensing Images

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ISBN: 9783038976844 9783038976851 Year: Volume: 1 Pages: 426 DOI: 10.3390/books978-3-03897-685-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-12-09 11:49:15
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With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

Keywords

hyperspectral image classification --- SELF --- SVMs --- Segment-Tree Filtering --- multi-sensor --- change feature analysis --- object-based --- multispectral images --- heterogeneous domain adaptation --- transfer learning --- multi-view canonical correlation analysis ensemble --- semi-supervised learning --- canonical correlation weighted voting --- ensemble learning --- image classification --- spatial attraction model (SAM) --- subpixel mapping (SPM) --- land cover --- mixed pixel --- spatial distribution --- hard classification --- building damage detection --- Fuzzy-GA decision making system --- machine learning techniques --- optical remotely sensed images --- sensitivity analysis --- texture analysis --- quality assessment --- ratio images --- Synthetic Aperture Radar (SAR) --- speckle --- speckle filters --- ice concentration --- SAR imagery --- convolutional neural network --- urban surface water extraction --- threshold stability --- sub-pixel --- linear spectral unmixing --- Landsat imagery --- image registration --- image fusion --- UAV --- metadata --- visible light and infrared integrated camera --- semantic segmentation --- CNN --- deep learning --- ISPRS --- remote sensing --- gate --- hyperspectral image --- sparse and low-rank graph --- tensor --- dimensionality reduction --- semantic labeling --- convolution neural network --- fully convolutional network --- sea-land segmentation --- ship detection --- hyperspectral image --- target detection --- multi-task learning --- sparse representation --- locality information --- remote sensing image correction --- color matching --- optimal transport --- CNN --- very high resolution images --- segmentation --- multi-scale clustering --- vehicle localization --- vehicle classification --- high resolution --- aerial image --- convolutional neural network (CNN) --- class imbalance --- deep learning --- convolutional neural network (CNN) --- fully convolutional network (FCN) --- classification --- remote sensing --- high resolution --- semantic segmentation --- deep convolutional neural networks --- manifold ranking --- single stream optimization --- high resolution image --- feature extraction --- hypergraph learning --- morphological profiles --- hyperedge weight estimation --- semantic labeling --- convolutional neural networks --- remote sensing --- deep learning --- aerial images --- hyperspectral image --- feature extraction --- dimensionality reduction --- optimized kernel minimum noise fraction (OKMNF) --- hyperspectral remote sensing --- endmember extraction --- multi-objective --- particle swarm optimization --- image alignment --- feature matching --- geostationary satellite remote sensing image --- GSHHG database --- Hough transform --- dictionary learning --- road detection --- Radon transform --- geo-referencing --- multi-sensor image matching --- Siamese neural network --- satellite images --- synthetic aperture radar --- inundation mapping --- flood --- optical sensors --- spatiotemporal context learning --- Modest AdaBoost --- HJ-1A/B CCD --- GF-4 PMS --- hyperspectral image classification --- automatic cluster number determination --- adaptive convolutional kernels --- hyperspectral imagery --- 1-dimensional (1-D) --- Convolutional Neural Network (CNN) --- Support Vector Machine (SVM) --- Random Forests (RF) --- machine learning --- deep learning --- TensorFlow --- multi-seasonal --- regional land cover --- saliency analysis --- remote sensing --- ROI detection --- hyperparameter sparse representation --- dictionary learning --- energy distribution optimizing --- multispectral imagery --- nonlinear classification --- kernel method --- dimensionality expansion --- deep convolutional neural networks --- road segmentation --- conditional random fields --- satellite images --- aerial images --- THEOS --- land cover change --- downscaling --- sub-pixel change detection --- machine learning --- MODIS --- Landsat --- very high resolution (VHR) satellite image --- topic modelling --- object-based image analysis --- image segmentation --- unsupervised classification --- multiscale representation --- GeoEye-1 --- wavelet transform --- fuzzy neural network --- remote sensing --- conservation --- urban heat island --- land surface temperature --- climate change --- land use --- land cover --- Landsat --- remote sensing --- SAR image --- despeckling --- dilated convolution --- skip connection --- residual learning --- scene classification --- saliency detection --- deep salient feature --- anti-noise transfer network --- DSFATN --- infrared image --- image registration --- MSER --- phase congruency --- hashing --- remote sensing image retrieval --- online learning --- hyperspectral image --- compressive sensing --- structured sparsity --- tensor sparse decomposition --- tensor low-rank approximation

Learning to Understand Remote Sensing Images

Author:
ISBN: 9783038976981 9783038976998 Year: Volume: 2 Pages: 376 DOI: 10.3390/books978-3-03897-699-8 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-12-09 11:49:15
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Abstract

With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

Keywords

hyperspectral image classification --- SELF --- SVMs --- Segment-Tree Filtering --- multi-sensor --- change feature analysis --- object-based --- multispectral images --- heterogeneous domain adaptation --- transfer learning --- multi-view canonical correlation analysis ensemble --- semi-supervised learning --- canonical correlation weighted voting --- ensemble learning --- image classification --- spatial attraction model (SAM) --- subpixel mapping (SPM) --- land cover --- mixed pixel --- spatial distribution --- hard classification --- building damage detection --- Fuzzy-GA decision making system --- machine learning techniques --- optical remotely sensed images --- sensitivity analysis --- texture analysis --- quality assessment --- ratio images --- Synthetic Aperture Radar (SAR) --- speckle --- speckle filters --- ice concentration --- SAR imagery --- convolutional neural network --- urban surface water extraction --- threshold stability --- sub-pixel --- linear spectral unmixing --- Landsat imagery --- image registration --- image fusion --- UAV --- metadata --- visible light and infrared integrated camera --- semantic segmentation --- CNN --- deep learning --- ISPRS --- remote sensing --- gate --- hyperspectral image --- sparse and low-rank graph --- tensor --- dimensionality reduction --- semantic labeling --- convolution neural network --- fully convolutional network --- sea-land segmentation --- ship detection --- hyperspectral image --- target detection --- multi-task learning --- sparse representation --- locality information --- remote sensing image correction --- color matching --- optimal transport --- CNN --- very high resolution images --- segmentation --- multi-scale clustering --- vehicle localization --- vehicle classification --- high resolution --- aerial image --- convolutional neural network (CNN) --- class imbalance --- deep learning --- convolutional neural network (CNN) --- fully convolutional network (FCN) --- classification --- remote sensing --- high resolution --- semantic segmentation --- deep convolutional neural networks --- manifold ranking --- single stream optimization --- high resolution image --- feature extraction --- hypergraph learning --- morphological profiles --- hyperedge weight estimation --- semantic labeling --- convolutional neural networks --- remote sensing --- deep learning --- aerial images --- hyperspectral image --- feature extraction --- dimensionality reduction --- optimized kernel minimum noise fraction (OKMNF) --- hyperspectral remote sensing --- endmember extraction --- multi-objective --- particle swarm optimization --- image alignment --- feature matching --- geostationary satellite remote sensing image --- GSHHG database --- Hough transform --- dictionary learning --- road detection --- Radon transform --- geo-referencing --- multi-sensor image matching --- Siamese neural network --- satellite images --- synthetic aperture radar --- inundation mapping --- flood --- optical sensors --- spatiotemporal context learning --- Modest AdaBoost --- HJ-1A/B CCD --- GF-4 PMS --- hyperspectral image classification --- automatic cluster number determination --- adaptive convolutional kernels --- hyperspectral imagery --- 1-dimensional (1-D) --- Convolutional Neural Network (CNN) --- Support Vector Machine (SVM) --- Random Forests (RF) --- machine learning --- deep learning --- TensorFlow --- multi-seasonal --- regional land cover --- saliency analysis --- remote sensing --- ROI detection --- hyperparameter sparse representation --- dictionary learning --- energy distribution optimizing --- multispectral imagery --- nonlinear classification --- kernel method --- dimensionality expansion --- deep convolutional neural networks --- road segmentation --- conditional random fields --- satellite images --- aerial images --- THEOS --- land cover change --- downscaling --- sub-pixel change detection --- machine learning --- MODIS --- Landsat --- very high resolution (VHR) satellite image --- topic modelling --- object-based image analysis --- image segmentation --- unsupervised classification --- multiscale representation --- GeoEye-1 --- wavelet transform --- fuzzy neural network --- remote sensing --- conservation --- urban heat island --- land surface temperature --- climate change --- land use --- land cover --- Landsat --- remote sensing --- SAR image --- despeckling --- dilated convolution --- skip connection --- residual learning --- scene classification --- saliency detection --- deep salient feature --- anti-noise transfer network --- DSFATN --- infrared image --- image registration --- MSER --- phase congruency --- hashing --- remote sensing image retrieval --- online learning --- hyperspectral image --- compressive sensing --- structured sparsity --- tensor sparse decomposition --- tensor low-rank approximation

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