Automatic Fracture Characterization Using Tactile and Proximity Optical Sensing

This paper demonstrates how tactile and proximity sensing can be used to perform automatic mechanical fractures detection (surface cracks). For this purpose, a custom-designed integrated tactile and proximity sensor has been implemented. With the help of fiber optics, the sensor measures the deformation of its body, when interacting with the physical environment, and the distance to the environment's objects. This sensor slides across different surfaces and records data which are then analyzed to detect and classify fractures and other mechanical features. The proposed method implements machine learning techniques (handcrafted features, and state of the art classification algorithms). An average crack detection accuracy of ~94% and width classification accuracy of ~80% is achieved. Kruskal-Wallis results (p < 0.001) indicate statistically significant differences among results obtained when analysing only integrated deformation measurements, only proximity measurements and both deformation and proximity data. A real-time classification method has been implemented for online classification of explored surfaces. In contrast to previous techniques, which mainly rely on visual modality, the proposed approach based on optical fibers might be more suitable for operation in extreme environments (such as nuclear facilities) where radiation may damage electronic components of commonly employed sensing devices, such as standard force sensors based on strain gauges and video cameras

Dead End Body Component Inspections With Convolutional Neural Networks Using UAS Imagery

This work presents a novel system utilizing previously developed convolutional neural network (CNN) architectures to aid in automating maintenance inspections of the dead-end body component (DEBC) from high-tension power lines. To maximize resolution of inspection images gathered via unmanned aerial systems (UAS), two different CNNs were developed. One to detect and crop the DEBC from an image. The second to classify the likelihood the component in question contains a crack. The DEBC detection CNN utilized a Python implementation of Faster R-CNN fine-tuned for three classes via 270 inspection photos collected during UAS inspection, alongside 111 images from provided simulated imagery. The data was augmented to develop 2,707 training images. The detection was tested with 111 UAS inspections images. The resulting CNN was capable of 97.8% accuracy in detecting and cropping DEBC welds. To train the classification CNN if the DEBC weld region cropped from the DEBC detection CNN was cracked, 1,149 manually cropped images from both the simulated images, as well images collected of components previously replaced both inside and outside a warehouse, were augmented to provide a training set of 4,632 images. The crack detection network was developed using the VGG16 model implemented with the Caffe framework. Training and testing of the crack detection CNNs performance was accomplished using a random 5-fold cross validation strategy resulting in an overall 98.8% accuracy. Testing the combined object detection and crack classification networks on the same 5-fold cross validation test images resulted in an average accuracy of 73.79%

Pavement Defect Classification and Localization Using Hybrid Weakly Supervised and Supervised Deep Learning and GIS

Automated detection of road defects has historically been challenging for the pavement management industry. As a result, new methods have been developed over the past few years to handle this issue. Most of these methods relied on supervised machine learning techniques, such as object detection and segmentation methods, which need a large, annotated image dataset to train their models. However, annotating pavement defects is difficult and time-consuming due to their ununiformed and complex shapes. To address this challenge, a hybrid pavement defect classification and localization framework using weakly supervised and supervised deep learning methods is proposed in this thesis. This framework has two steps: (1) A robust hierarchical two-level classifier that classifies the defects in images, and (2) A method for defect localization combining weakly supervised and supervised techniques. In the localization method, first, defects are primarily localized using a weakly supervised method (i.e. Class Activation Mapping (CAM)). Next, based on the results of the first classifiers, the defects are segmented from the localized patches obtained in the previous step. The feature maps extracted from the CAM method are used to train a segmentation network once (i.e. U-Net or Mask R-CNN) to localize and segment the defects in the images. Thus, the proposed framework combines the advantages of weakly supervised and supervised methods. The supervised modules in the framework are trained once and can be used for any new data without the need to train. In other words, to use our framework on new dataset only the classifiers should be fine-tuned. Furthermore, the proposed framework introduced an innovative method designed to calculate the maximum crack width in pixels within linear segmented defect patches, derived from the localization module of the proposed framework. This method is particularly advantageous as it provides critical information that can be further employed in the calculation of the Pavement Condition Index (PCI). Additionally, the proposed method benefits from an asset management inspection system based on Geographic Information System (GIS) technology to prepare the dataset used in the training and testing. Thus, this advanced system serves a dual role within our framework. Firstly, it assists in the assembly and preparation of the dataset used in the model training process, providing a geographically organized collection of images and related data. Secondly, it plays a crucial role in the testing phase, offering a spatially accurate platform for evaluating the effectiveness of the model in real-world scenarios. A dataset from Georgia State in the USA was used in the case study. The proposed framework obtained high precision of 97%, 88%, 92% and 97% for localizing the alligator, block, longitudinal and transverse cracks, respectively. Considering all factors, such as annotation cost, and performance on the test dataset, the proposed localization method outperforms the supervised localization methods, such as instance segmentation and object detection for localizing road pavement defect

Detection of land cover changes in El Rawashda forest, Sudan: A systematic comparison: Detection of land cover changes in El Rawashda forest, Sudan: A systematic comparison

The primary objective of this research was to evaluate the potential for monitoring forest change using Landsat ETM and Aster data. This was accomplished by performing eight change detection algorithms: pixel post-classification comparison (PCC), image differencing Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Transformed Difference Vegetation Index (TDVI), principal component analysis (PCA), multivariate alteration detection (MAD), change vector analysis (CVA) and tasseled cap analysis (TCA). Methods, Post-Classification Comparison and vegetation indices are straightforward techniques and easy to apply. In this study the simplified classification with only 4 forest classes namely close forest, open forest, bare land and grass land was used The overall classification accuracy obtained were 88.4%, 91.9% and 92.1% for the years 2000, 2003 and 2006 respectively. The Tasseled Cap green layer (GTC) composite of the three images was proposed to detect the change in vegetation of the study area. We found that the RBG-TCG worked better than RGBNDVI. For instance, the RBG-TCG detected some areas of changes that RGB-NDVI failed to detect them, moreover RBG-TCG displayed different changed areas with more strong colours. Change vector analysis (CVA) based on Tasseled Cap transformation (TCT) was also applied for detecting and characterizing land cover change. The results support the CVA approach to change detection. The calculated date to date change vectors contained useful information, both in their magnitude and their direction. A powerful tool for time series analysis is the principal components analysis (PCA). This method was tested for change detection in the study area by two ways: Multitemporal PCA and Selective PCA. Both methods found to offer the potential for monitoring forest change detection. A recently proposed approach, the multivariate alteration detection (MAD), in combination with a posterior maximum autocorrelation factor transformation (MAF) was used to demonstrate visualization of vegetation changes in the study area. The MAD transformation provides a way of combining different data types that found to be useful in change detection. Accuracy assessment is an important final step addressed in the study to evaluate the different change detection techniques. A quantitative accuracy assessment at level of change/no change pixels was performed to determine the threshold value with the highest accuracy. Among the various accuracy assessment methods presented the highest accuracy was obtained using the post-classification comparison based on supervised classification of each two time periods (2000 -2003 and 2003-2006), which were 90.6% and 87% consequently

Detection of land cover changes in El Rawashda forest, Sudan: A systematic comparison: Detection of land cover changes in El Rawashda forest, Sudan: A systematic comparison

The primary objective of this research was to evaluate the potential for monitoring forest change using Landsat ETM and Aster data. This was accomplished by performing eight change detection algorithms: pixel post-classification comparison (PCC), image differencing Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Transformed Difference Vegetation Index (TDVI), principal component analysis (PCA), multivariate alteration detection (MAD), change vector analysis (CVA) and tasseled cap analysis (TCA). Methods, Post-Classification Comparison and vegetation indices are straightforward techniques and easy to apply. In this study the simplified classification with only 4 forest classes namely close forest, open forest, bare land and grass land was used The overall classification accuracy obtained were 88.4%, 91.9% and 92.1% for the years 2000, 2003 and 2006 respectively. The Tasseled Cap green layer (GTC) composite of the three images was proposed to detect the change in vegetation of the study area. We found that the RBG-TCG worked better than RGBNDVI. For instance, the RBG-TCG detected some areas of changes that RGB-NDVI failed to detect them, moreover RBG-TCG displayed different changed areas with more strong colours. Change vector analysis (CVA) based on Tasseled Cap transformation (TCT) was also applied for detecting and characterizing land cover change. The results support the CVA approach to change detection. The calculated date to date change vectors contained useful information, both in their magnitude and their direction. A powerful tool for time series analysis is the principal components analysis (PCA). This method was tested for change detection in the study area by two ways: Multitemporal PCA and Selective PCA. Both methods found to offer the potential for monitoring forest change detection. A recently proposed approach, the multivariate alteration detection (MAD), in combination with a posterior maximum autocorrelation factor transformation (MAF) was used to demonstrate visualization of vegetation changes in the study area. The MAD transformation provides a way of combining different data types that found to be useful in change detection. Accuracy assessment is an important final step addressed in the study to evaluate the different change detection techniques. A quantitative accuracy assessment at level of change/no change pixels was performed to determine the threshold value with the highest accuracy. Among the various accuracy assessment methods presented the highest accuracy was obtained using the post-classification comparison based on supervised classification of each two time periods (2000 -2003 and 2003-2006), which were 90.6% and 87% consequently

The value of the geological record in determining rates and drivers of coastal lagoon shoreline development.

This research investigated the feasibility of using the geological record to determine rates and drivers of morphological change in coastal lagoons. Substrate elevation in these environments is of primary importance for survival of wetland habitats, the effectiveness of drainage and flood mitigation functions delivered by those habitats, and the success of potential carbon sequestration programs. Investigating rates and trajectories of lagoon evolution will become more important given the effects of accelerating sea-level rise and human interventions, direct and indirect, on all coastal depositional environments. Elevation change on coastal lagoon shorelines is the net result of numerous sediment accretion and erosion cycles that are subject to considerable uncertainty. Numerous hydrological, biological, geological and anthropogenic processes interact over a range of timescales, and are subject to complex relationships and non-linear feedbacks. To successfully reproduce and predict long-term shoreline change with numerical models, the net effect of these processes must be captured and attributed to appropriate functions and parameter values. Shoreline processes are typically measured in-situ, and measurements would need to span several decades in order to reach an adequate level of confidence about the representativeness of the results. This is particularly true in regions subject to inter-decadal climate variability, such as the El Niño Southern Oscillation in southeast Australia. Even with a sufficiently long-term empirical dataset, the lasting effect of sediment accumulation for elevation change depends strongly on sub-surface processes (root production, decomposition, compaction and soil water content), which take place over still longer timescales and require sub-surface investigation. Reliance on the depositional history captured in the geological record would improve confidence in longer-term rates of morphological change. It would reduce the time and effort required from years (at least) of field measurements to a few months of laboratory work. The effectiveness of the geological record for model parameterisation and calibration, however, depends on the potential to infer drivers of elevation change as well as rates. For this research, soil samples up to 1.8 m depth were obtained in cross-shore core transects from prograded shorelines in three NSW coastal lagoons: Wooloweyah Lagoon near Yamba; Lake Innes near Port Macquarie; and Neranie Bay within Myall Lake. The three lagoons and the segments of shoreline sampled were selected to be as low-energy as possible by avoiding the effects of fluvial and tidal processes that could render intractable shoreline processes with already complex interactions. Each core sample was split and scanned for high-resolution optical images and down-core profiles of magnetic susceptibility, and geochemistry. These datasets enabled the identification and correlation of depositional units between cores and along cross-shore profiles, and thus high-level analysis of shorelines stratigraphy. From each site or transect at least one representative core was selected for detailed investigation, sub-sampled at 10 mm resolution and analysed for grain size, moisture content, density, organic content, and isotopic activity of 210Pb, 137Cs and 14C which provided the approximate timing of deposition for each sub-sample. Mass accumulation rates (g/cm2/yr) and vertical accretion rates (mm/yr) were calculated for correlation with physical sediment properties. At one site, Neranie Bay, this detailed level of analysis was performed for three cores, covering most of the cross-shore transect. Accretion rates calculated for approximately the last 100 years from 210Pb analysis averaged less than 2 mm/yr, consistent with figures reported for similar environments elsewhere in southeast Australia, and at the lower end of the spectrum for internationally reported rates. Preceding the timing of European settlement, accretion rates at the three sites were considerably lower. Recent rates of sediment mass accumulation mostly ranged from 0.02-0.2 g/cm2/yr, but this figure is rarely reported elsewhere and is therefore difficult to compare. Accretion and mass accumulation rates reduced rapidly down-core in the upper few centimetres of each sample, suggesting a significant role for organic matter decomposition for at least several decades following initial deposition. Changes in moisture content and bulk density were observed over similar depths. This research highlights the importance of analysing soil samples to sufficient depth and ensuring sub-surface processes have ceased to have significant impacts on down-core changes before making interpretations about trends over time. A controlling influence of organic content over vertical accretion (and therefore elevation change) was found for the three sites investigated. This control was independent of the inorganic sediment input, which was often higher (by mass) than the organic input. At Neranie Bay, cross-shore trends in organic content were evident. Organic matter input at the surface of the soil sample was greatest when the sample was taken from a higher elevation with less frequent inundation (i.e. short hydroperiod). The proportion of organic matter retained in the soil profile, however, was lowest where hydroperiod was shortest. On balance, organic matter makes the greatest contribution to elevation change when hydroperiod is longest. It could not be determined whether this was caused by higher rates of sub-surface decomposition with short hydroperiod, or high rates of below-ground productivity with long hydroperiod (or both). Either way the results are counter-intuitive and could not be determined without reliance on the geological record. The cross-shore trend that was established from this research is of vital importance. The relationship between hydroperiod and organically driven elevation change results in self-regulating, negative feedback and therefore greater resilience to increases in hydroperiod when the relationship is as reported here. When the reverse relationship is found, however, resilience to increased hydroperiod, and therefore sea level rise, would be compromised because inundation would continually decrease the ability of organic sedimentation to drive accretion, potentially resulting in habitat loss and exposing the shoreline to the risk of erosion. Previous studies suggest that this cross-shore relationship varies on a site-by-site basis. Determining the direction of the relationship with field measurements would take years and still be subject to much higher uncertainty than the methods employed here. This research has shown that the geological record is not only a feasible source of information about accretion rates and drivers, but also a preferable one. Provided further research can succeed in linking sub-surface retention of organic matter to contemporary primary production at the surface, the geological record will provide a more efficient and effective method of designing and calibrating much-needed predictive models to explore scenarios of shoreline development and wetland survival under changing conditions. Further research should also target a range of geologic and climatic settings to differentiate between drivers that can be generalised across all sites and those that vary on a site-by-site basis

New Perspectives in the Definition/Evaluation of Seismic Hazard through Analysis of the Environmental Effects Induced by Earthquakes

The devastating effects caused by the recent catastrophic earthquakes that took place all over the world from Japan, New Zealand, to Chile, as well as those occurring in the Mediterranean basin, have once again shown that ground motion, although a serious source of direct damage, is not the only parameter to be considered, with most damage being the result of coseismic geological effects that are directly connected to the earthquake source or caused by ground shaking. The primary environmental effects induced by earthquakes as well as the secondary effects (sensu Environmental Seismic Intensity - ESI 2007 scale) must be considered for a more correct and complete evaluation of seismic hazards, at both regional and local scales. This Special Issue aims to collect all contributions that, using different methodologies, integrate new data produced with multi-disciplinary and innovative methods. These methodologies are essential for the identification and characterization of seismically active areas, and for the development of new hazard models, obtained using different survey techniques. The topic attracted a lot of interest, 19 peer-reviewed articles were collected; moreover, different areas of the world have been analyzed through these methodologies: Italy, USA, Spain, Australia, Ecuador, Guatemala, South Korea, Kyrgyzstan, Mongolia, Russia, China, Japan, and Nepal