Crack Detection in Single- and Multi-Light Images of Painted Surfaces using Convolutional Neural Networks

Cracks represent an imminent danger for painted surfaces that needs to be alerted before degenerating into more severe aging effects, such as color loss. Automatic detection of cracks from painted surfaces' images would be therefore extremely useful for art conservators; however, classical image processing solutions are not effective to detect them, distinguish them from other lines or surface characteristics. A possible solution to improve the quality of crack detection exploits Multi-Light Image Collections (MLIC), that are often acquired in the Cultural Heritage domain thanks to the diffusion of the Reflectance Transformation Imaging (RTI) technique, allowing a low cost and rich digitization of artworks' surfaces. In this paper, we propose a pipeline for the detection of crack on egg-tempera paintings from multi-light image acquisitions and that can be used as well on single images. The method is based on single or multi-light edge detection and on a custom Convolutional Neural Network able to classify image patches around edge points as crack or non-crack, trained on RTI data. The pipeline is able to classify regions with cracks with good accuracy when applied on MLIC. Used on single images, it can give still reasonable results. The analysis of the performances for different lighting directions also reveals optimal lighting directions

D5.1 SHM digital twin requirements for residential, industrial buildings and bridges

This deliverable presents a report of the needs for structural control on buildings (initial imperfections, deflections at service, stability, rheology) and on bridges (vibrations, modal shapes, deflections, stresses) based on state-of-the-art image-based and sensor-based techniques. To this end, the deliverable identifies and describes strategies that encompass state-of-the-art instrumentation and control for infrastructures (SHM technologies).Objectius de Desenvolupament Sostenible::8 - Treball Decent i Creixement EconòmicObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPreprin

Development of Mining Sector Applications for Emerging Remote Sensing and Deep Learning Technologies

This thesis uses neural networks and deep learning to address practical, real-world problems in the mining sector. The main focus is on developing novel applications in the area of object detection from remotely sensed data. This area has many potential mining applications and is an important part of moving towards data driven strategic decision making across the mining sector. The scientific contributions of this research are twofold; firstly, each of the three case studies demonstrate new applications which couple remote sensing and neural network based technologies for improved data driven decision making. Secondly, the thesis presents a framework to guide implementation of these technologies in the mining sector, providing a guide for researchers and professionals undertaking further studies of this type. The first case study builds a fully connected neural network method to locate supporting rock bolts from 3D laser scan data. This method combines input features from the remote sensing and mobile robotics research communities, generating accuracy scores up to 22% higher than those found using either feature set in isolation. The neural network approach also is compared to the widely used random forest classifier and is shown to outperform this classifier on the test datasets. Additionally, the algorithms’ performance is enhanced by adding a confusion class to the training data and by grouping the output predictions using density based spatial clustering. The method is tested on two datasets, gathered using different laser scanners, in different types of underground mines which have different rock bolting patterns. In both cases the method is found to be highly capable of detecting the rock bolts with recall scores of 0.87-0.96. The second case study investigates modern deep learning for LiDAR data. Here, multiple transfer learning strategies and LiDAR data representations are examined for the task of identifying historic mining remains. A transfer learning approach based on a Lunar crater detection model is used, due to the task similarities between both the underlying data structures and the geometries of the objects to be detected. The relationship between dataset resolution and detection accuracy is also examined, with the results showing that the approach is capable of detecting pits and shafts to a high degree of accuracy with precision and recall scores between 0.80-0.92, provided the input data is of sufficient quality and resolution. Alongside resolution, different LiDAR data representations are explored, showing that the precision-recall balance varies depending on the input LiDAR data representation. The third case study creates a deep convolutional neural network model to detect artisanal scale mining from multispectral satellite data. This model is trained from initialisation without transfer learning and demonstrates that accurate multispectral models can be built from a smaller training dataset when appropriate design and data augmentation strategies are adopted. Alongside the deep learning model, novel mosaicing algorithms are developed both to improve cloud cover penetration and to decrease noise in the final prediction maps. When applied to the study area, the results from this model provide valuable information about the expansion, migration and forest encroachment of artisanal scale mining in southwestern Ghana over the last four years. Finally, this thesis presents an implementation framework for these neural network based object detection models, to generalise the findings from this research to new mining sector deep learning tasks. This framework can be used to identify applications which would benefit from neural network approaches; to build the models; and to apply these algorithms in a real world environment. The case study chapters confirm that the neural network models are capable of interpreting remotely sensed data to a high degree of accuracy on real world mining problems, while the framework guides the development of new models to solve a wide range of related challenges

Symmetry in Structural Health Monitoring

In this Special Issue on symmetry, we mainly discuss the application of symmetry in various structural health monitoring. For example, considering the health monitoring of a known structure, by obtaining the static or dynamic response of the structure, using different signal processing methods, including some advanced filtering methods, to remove the influence of environmental noise, and extract structural feature parameters to determine the safety of the structure. These damage diagnosis methods can also be effectively applied to various types of infrastructure and mechanical equipment. For this reason, the vibration control of various structures and the knowledge of random structure dynamics should be considered, which will promote the rapid development of the structural health monitoring. Among them, signal extraction and evaluation methods are also worthy of study. The improvement of signal acquisition instruments and acquisition methods improves the accuracy of data. A good evaluation method will help to correctly understand the performance with different types of infrastructure and mechanical equipment

Digital workflows for the management of existing structures in the pre- and post-earthquake phases: BIM, CDE, drones, laser-scanning and AI

La metodologia BIM, sviluppata in America negli anni '70, ha rivoluzionato l'industria delle costruzioni introducendo i principi di innovazione e digitalizzazione per la gestione dei progetti, in un settore settore produttivo troppo legato a logiche tradizionali. I numerosi processi digitali che sono stati sviluppati da allora hanno riguardato in gran parte la progettazione di nuovi edifici, e sono principalmente legati alla disciplina del construction management. Alcune prime sperimentazioni condotte nel tempo hanno mostrato come l'estensione di questa metodologia agli edifici esistenti comporti molte difficoltà. In questo panorama, il lavoro di tesi si concentra sulla gestione delle strutture nella fase pre e post-sisma con l'obiettivo di sviluppare processi digitali basati sull'uso di tecnologie innovative applicate sia agli edifici ordinari che a quelli storici. Il primo workflow sviluppato, relativo alla fase pre-sisma, è stato denominato scan-to-FEM, ed è finalizzato a particolarizzare il classico processo scan-to-BIM nel campo dell'ingegneria strutturale, analizzando così tutti i passaggi dal rilievo dell'edificio con le tecniche digitali di fotogrammetria e laser-scanning fino all'analisi strutturale e alla valutazione della sicurezza nei confronti delle azioni sismiche. I processi di gestione delle strutture post-sisma sono invece incentrati sulla stima della sicurezza della struttura e sulla definizione delle strategie di intervento, e si basano sull'analisi delle caratteristiche intrinseche della struttura e dei danni indotti dagli eventi sismici. L'intero processo di valutazione del livello operativo di un edificio è stato quindi rivisto alla luce delle moderne tecnologie digitali. Nel dettaglio, sono state sviluppate Reti Neurali Convoluzionali (CNN) per la crack detection, e l'estrazione delle informazioni numeriche associate alle lesioni, gestite poi grazie ai modelli BIM. I quadri fessurativi sono stati digitalizzati grazie allìintroduzione un nuovo oggetto BIM "lesione" (attualmente non codificato nello standard IFC), al quale è stato aggiunto un set di parametri in parte valutati con le CNN ed in parte qualitativi. Durante lo sviluppo di questi processi, sono stati sviluppati nuovi strumenti adhoc per la gestione degli edifici esistenti. In particolare, sono state definite specifiche per lo sviluppo di schede tecniche digitali dei danni, e per la creazione del nuovo oggetto BIM "lesione". I processi di gestione degli edifici danneggiati, grazie agli sviluppi tecnologici realizzati, sono stati applicati per la digitalizzazione dell'edificio storico della chiesa di San Pietro in Vinculis danneggiato a seguito di eventi sismici, grazie ai quali sono stati sperimentati i massimi benefici in termini di riduzione di tempo e risparmio di risorse

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways