The combination of geomatic approaches and operational modal analysis to improve calibration of finite element models: a case of study in Saint Torcato church (Guimarães, Portugal)

This paper present a set of procedures based on laser scanning, photogrammetry (Structure from Motion) and operational modal analysis in order to obtain accurate numeric models which allows identigying architectural complications that arise in historical buildings. In addition, themethod includes tools that facilitate building-damage monitoring tasks. All of these aimed to obtain robust basis for numerical analysis of the actual behavior and monitoring task. This case study seeks to validate said methodologies, using as an example the case of Saint Torcato Church, located in Guimãres, Portugal

Non-contact vision-based deformation monitoring on bridge structures

Information on deformation is an important metric for bridge condition and performance assessment, e.g. identifying abnormal events, calibrating bridge models and estimating load carrying capacities, etc. However, accurate measurement of bridge deformation, especially for long-span bridges remains as a challenging task. The major aim of this research is to develop practical and cost-effective techniques for accurate deformation monitoring on bridge structures. Vision-based systems are taken as the study focus due to a few reasons: low cost, easy installation, desired sample rates, remote and distributed sensing, etc. This research proposes an custom-developed vision-based system for bridge deformation monitoring. The system supports either consumer-grade or professional cameras and incorporates four advanced video tracking methods to adapt to different test situations. The sensing accuracy is firstly quantified in laboratory conditions. The working performance in field testing is evaluated on one short-span and one long-span bridge examples considering several influential factors i.e. long-range sensing, low-contrast target patterns, pattern changes and lighting changes. Through case studies, some suggestions about tracking method selection are summarised for field testing. Possible limitations of vision-based systems are illustrated as well. To overcome observed limitations of vision-based systems, this research further proposes a mixed system combining cameras with accelerometers for accurate deformation measurement. To integrate displacement with acceleration data autonomously, a novel data fusion method based on Kalman filter and maximum likelihood estimation is proposed. Through field test validation, the method is effective for improving displacement accuracy and widening frequency bandwidth. The mixed system based on data fusion is implemented on field testing of a railway bridge considering undesired test conditions (e.g. low-contrast target patterns and camera shake). Analysis results indicate that the system offers higher accuracy than using a camera alone and is viable for bridge influence line estimation. With considerable accuracy and resolution in time and frequency domains, the potential of vision-based measurement for vibration monitoring is investigated. The proposed vision-based system is applied on a cable-stayed footbridge for deck deformation and cable vibration measurement under pedestrian loading. Analysis results indicate that the measured data enables accurate estimation of modal frequencies and could be used to investigate variations of modal frequencies under varying pedestrian loads. The vision-based system in this application is used for multi-point vibration measurement and provides results comparable to those obtained using an array of accelerometers

Documenting Complexity for the 20TH Century Heritage: the Enriched 3d Models of the Turin Exposition Nervi's Halls Digitization

Abstract. Great attention is increasingly paid to the heritage belonging to the XX century, particularly for the spatial structures made of concrete, that are a significant trait of this modern movement architecture. Since they demand today urgent conservation plans sustaining their deterioration, the multidisciplinary researches should devotes a profound investigations for tailored approaches providing a clear indication of best practices and recommendation for correct 3D documentation, information management and structural assessment and monitoring. In this framework, the Geomatics approaches are advancing the interests toward the multi-scale and multi-sensor digitization and for supporting management of complex information in enriched 3D models. The iconic halls B and C in Torino Esposizioni (Italy), designed by Pier Luigi Nervi, is the case study presented. It was recently awarded by the Getty Keeping it Modern grant. The multi-disciplinary research conducted, still in progress, focuses a particularly into the investigation of the structural analysis and consistency of ferrocement elements of the vaulted system finalized to the structural condition assessment. Here the role of multi-scale and multi-sensor 3D models is investigated, such as the development of a digital twin of the halls as a starting point to create an enriched informative system. The reconstruction of this model particularly considering the large extension and the complexity of the spaces, is addressed to works as a collector of 3D multi-sensor data and information related to the diagnostic investigation on structural health monitoring for the durability of ferrocement elements

Robotic surface exploration with vision and tactile sensing for cracks detection and characterisation

This thesis presents a novel algorithm for crack localisation and detection based on visual and tactile analysis via fibre-optics. A finger-shaped sensor based on fibre-optics is employed for the data acquisition to collect data for the analysis and the experiments. Three pairs of fibre optics are used to measure the sensor's soft part deformation via changes in the reflected light intensity. A fourth pair of optical fibre cables is positioned at the tip of the finger and it is used to sense the proximity to external objects. To detect the possible locations of cracks a camera is used to scan an environment while running an object detection algorithm. Once the crack is detected, a fully-connected graph is created from a skeletonised version of the crack. Minimum spanning tree is then employed for calculating the shortest path to explore the crack which is then used to develop the motion planner for the robotic manipulator. The motion planner divides the crack into multiple nodes which are then explored one by one. Then, the manipulator starts the exploration and performs the tactile data classification to confirm if there is indeed a crack in that location or just a false positive from the vision algorithm. This is repeated until all the nodes of the crack are explored. If a crack is not detected from vision, then it won't be further explored in the tactile step. Because of this, false negative have the biggest weight and recall is the most import metric in this study. I perform experiments to investigate the improvements for the time required during exploration when using visual and tactile modalities together. The experimental studies demonstrate that exploring a fractured surface with a combination of visual and tactile modalities is four times faster than using solely the tactile mode. The accuracy of detection is also improved when the two modalities were combined. Experiments are also performed in order to develop a robust machine learning model to analyse and classify the tactile data acquired during exploration via the fibre-optics sensor. Frequency domain features are explored to investigate the spectrum of the signal. Results show that when training machine learning models and deep learning networks using these features, the resulting models are more robust when tested across different databases, on which they are not trained. Thus, when computer vision techniques may fail because of light conditions or extreme environments, fibre-optics sensors can be employed to analyse the presence of cracks on explored surfaces via machine learning and deep neural network algorithms. Still, when introducing tactile in extreme environments, caution must be used when making contact with possible fragile surfaces which may break because of the friction produced by the tactile sensor. Proximity may be used in this case to calculate the distance between the sensor and the object and to reduce speed when getting closer to the object. In conclusion, the thesis has contributed to advances in crack detection by introducing a multi-modal algorithm that is used to detect cracks in the environment via computer vision and then confirming the presence of a crack via tactile exploration and machine learning classification of the data acquired from a fibre-optic-based sensor. Few methods currently use tactile sensing for crack characterisation and detection and this is the first study which shows the reliability of tactile-based methodologies for crack detection via machine learning analysis. Furthermore, this is the first method which combines both tactile and vision for crack analysis

Investigation of 3DP technology for fabrication of surgical simulation phantoms

The demand for affordable and realistic phantoms for training, in particular for functional endoscopic sinus surgery (FESS), has continuously increased in recent years. Conventional training methods, such as current physical models, virtual simulators and cadavers may have restrictions, including fidelity, accessibility, cost and ethics. In this investigation, the potential of three-dimensional printing for the manufacture of biologically representative simulation materials for surgery training phantoms has been investigated. A characterisation of sinus anatomical elements was performed through CT and micro-CT scanning of a cadaveric sinus portion. In particular, the relevant constituent tissues of each sinus region have been determined. Secondly, feedback force values experienced during surgical cutting have been quantified with an actual surgical instrument, specifically modified for this purpose. Force values from multiple post-mortem subjects and different areas of the paranasal sinuses have been gathered and used as a benchmark for the optimisation of 3D-printing materials. The research has explored the wide range of properties achievable in 3DP through post-processing methods and variation of printing parameters. For this latter element, a machine-vision system has been developed to monitor the 3DP in real time. The combination of different infiltrants allowed the reproduction of force values comparable to those registered from cadaveric human tissue. The internal characteristics of 3D printed samples were shown to influence their fracture behaviour under resection. Realistic appearance under endoscopic conditions has also been confirmed. The utilisation of some of the research has also been demonstrated in another medical (non-surgical) training application. This investigation highlights a number of capabilities, and also limitations, of 3DP for the manufacturing of representative materials for application in surgical training phantoms

The characterisation and simulation of 3D vision sensors for measurement optimisation

The use of 3D Vision is becoming increasingly common in a range of industrial applications including part identification, reverse engineering, quality control and inspection. To facilitate this increased usage, especially in autonomous applications such as free-form assembly and robotic metrology, the capability to deploy a sensor to the optimum pose for a measurement task is essential to reduce cycle times and increase measurement quality. Doing so requires knowledge of the 3D sensor capabilities on a material specific basis, as the optical properties of a surface, object shape, pose and even the measurement itself have severe implications for the data quality. This need is not reflected in the current state of sensor haracterisation standards which commonly utilise optically compliant artefacts and therefore can not inform the user of a 3D sensor the realistic expected performance on non-ideal objects.This thesis presents a method of scoring candidate viewpoints for their ability to perform geometric measurements on an object of arbitrary surface finish. This is achieved by first defining a technology independent, empirical sensor characterisation method which implements a novel variant of the commonly used point density point cloud quality metric, which is normalised to isolate the effect of surface finish on sensor performance, as well as the more conventional assessment of point standard deviation. The characterisation method generates a set of performance maps for a sensor per material which are a function of distance and surface orientation. A sensor simulation incorporates these performance maps to estimate the statistical properties of a point cloud on objects with arbitrary shape and surface finish, providing the sensor has been characterised on the material in question.A framework for scoring measurement specific candidate viewpoints is presented in the context of the geometric inspection of four artefacts with different surface finish but identical geometry. Views are scored on their ability to perform each measurement based on a novel view score metric, which incorporates the expected point density, noise and occlusion of measurement dependent model features. The simulation is able to score the views reliably on all four surface finishes tested, which range from ideal matt white to highly polished aluminium. In 93% of measurements, a set of optimal or nearly optimal views is correctly selected.</div

DOCUMENTING COMPLEXITY FOR THE 20TH CENTURY HERITAGE: THE ENRICHED 3D MODELS OF THE TURIN EXPOSITION NERVI’S HALLS DIGITIZATION

Great attention is increasingly paid to the heritage belonging to the XX century, particularly for the spatial structures made of concrete, that are a significant trait of this modern movement architecture. Since they demand today urgent conservation plans sustaining their deterioration, the multidisciplinary researches should devotes a profound investigations for tailored approaches providing a clear indication of best practices and recommendation for correct 3D documentation, information management and structural assessment and monitoring. In this framework, the Geomatics approaches are advancing the interests toward the multi-scale and multi-sensor digitization and for supporting management of complex information in enriched 3D models. The iconic halls B and C in Torino Esposizioni (Italy), designed by Pier Luigi Nervi, is the case study presented. It was recently awarded by the Getty Keeping it Modern grant. The multi-disciplinary research conducted, still in progress, focuses a particularly into the investigation of the structural analysis and consistency of ferrocement elements of the vaulted system finalized to the structural condition assessment. Here the role of multi-scale and multi-sensor 3D models is investigated, such as the development of a digital twin of the halls as a starting point to create an enriched informative system. The reconstruction of this model particularly considering the large extension and the complexity of the spaces, is addressed to works as a collector of 3D multi-sensor data and information related to the diagnostic investigation on structural health monitoring for the durability of ferrocement elements