Characterization of components of water supply systems from GPR images and tools of intelligent data analysis

[EN] Over time, due to multiple operational and maintenance activities, the networks of water supply systems (WSSs) undergo interventions, modifications or even are closed. In many cases, these activities are not properly registered. Knowledge of the paths and characteristics (status and age, etc.) of the WSS pipes is obviously necessary for efficient and dynamic management of such systems. This problem is greatly augmented by considering the detection and control of leaks. Access to reliable leakage information is a complex task. In many cases, leaks are detected when the damage is already considerable, which brings high social and economic costs. In this sense, non-destructive methods (e.g., ground penetrating radar - GPR) may be a constructive response to these problems, since they allow, as evidenced in this thesis, to ascertain paths of pipes, identify component characteristics, and detect primordial water leaks. Selection of GPR in this work is justified by its characteristics as non-destructive technique that allows studying both metallic and non-metallic objects. Although the capture of information with GPR is usually successful, such aspects as the capture settings, the large volume of generated information, and the use and interpretation of such information require high level of skill and experience. This dissertation may be seen as a step forward towards the development of tools able to tackle the problem of lack of knowledge on the WSS buried assets. The main objective of this doctoral work is thus to generate tools and assess their feasibility of application to the characterization of components of WSSs from GPR images. In this work we have carried out laboratory tests specifically designed to propose, develop and evaluate methods for the characterization of the WSS buried components. Additionally, we have conducted field tests, which have enabled us to determine the feasibility of implementing such methodologies under uncontrolled conditions. The methodologies developed are based on techniques of intelligent data analysis. The basic principle of this work has involved the processing of data obtained through the GPR to look for useful information about WSS components, with special emphasis on the pipes. After performing numerous activities, one can conclude that, using GPR images, it is feasible to obtain more information than the typical identification of hyperbolae currently performed. In addition, this information can be observed directly, e.g. more simply, using the methodologies proposed in this doctoral work. These methodologies also prove that it is feasible to identify patterns (especially with the preprocessing algorithm termed Agent race) that provide fairly good approximation of the location of leaks in WSSs. Also, in the case of pipes, one can obtain such other characteristics as diameter and material. The main outcomes of this thesis consist in a series of tools we have developed to locate, identify and visualize WSS components from GPR images. Most interestingly, the data are synthesized and reduced so that the characteristics of the different components of the images recorded in GPR are preserved. The ultimate goal is that the developed tools facilitate decision-making in the technical management of WSSs, and that such tools can even be operated by personnel with limited experience in handling non-destructive methodologies, specifically GPR.[ES] Con el paso del tiempo, y debido a múltiples actividades operacionales y de mantenimiento, las redes de los sistemas de abastecimiento de agua (SAAs) sufren intervenciones, modificaciones o incluso, son clausuradas, sin que, en muchos casos, estas actividades sean correctamente registradas. El conocimiento de los trazados y características (estado y edad, entre otros) de las tuberías en los SAAs es obviamente necesario para una gestión eficiente y dinámica de tales sistemas. A esta problemática se suma la detección y el control de las fugas de agua. El acceso a información fiable sobre las fugas es una tarea compleja. En muchos casos, las fugas son detectadas cuando los daños en la red son ya considerables, lo que trae consigo altos costes sociales y económicos. En este sentido, los métodos no destructivos (por ejemplo, ground penetrating radar - GPR), pueden ser una respuesta a estas problemáticas, ya que permiten, como se pone de manifiesto en esta tesis, localizar los trazados de las tuberías, identificar características de los componentes y detectar las fugas de agua cuando aún no son significativas. La selección del GPR, en este trabajo se justifica por sus características como técnica no destructiva, que permite estudiar tanto objetos metálicos como no metálicos. Aunque la captura de información con GPR suele ser exitosa, la configuración de la captura, el gran volumen de información, y el uso y la interpretación de la información requieren de alto nivel de habilidad y experiencia por parte del personal. Esta tesis doctoral se plantea como un avance hacia el desarrollo de herramientas que permitan responder a la problemática del desconocimiento de los activos enterrados de los SAAs. El objetivo principal de este trabajo doctoral es, pues, generar herramientas y evaluar la viabilidad de su aplicación en la caracterización de componentes de un SAA, a partir de imágenes GPR. En este trabajo hemos realizado ensayos de laboratorio específicamente diseñados para plantear, elaborar y evaluar metodologías para la caracterización de los componentes enterrados de los SAAs. Adicionalmente, hemos realizado ensayos de campo, que han permitido determinar la viabilidad de aplicación de tales metodologías bajo condiciones no controladas. Las metodologías elaboradas están basadas en técnicas de análisis inteligentes de datos. El principio básico de este trabajo ha consistido en el tratamiento adecuado de los datos obtenidos mediante el GPR, a fin de buscar información de utilidad para los SAAs respecto a sus componentes, con especial énfasis en las tuberías. Tras la realización de múltiples actividades, se puede concluir que es viable obtener más información de las imágenes de GPR que la que actualmente se obtiene con la típica identificación de hipérbolas. Esta información, además, puede ser observada directamente, de manera más sencilla, mediante las metodologías planteadas en este trabajo doctoral. Con estas metodologías se ha probado que también es viable la identificación de patrones (especialmente el pre-procesado con el algoritmo Agent race) que proporcionan aproximación bastante acertada de la localización de las fugas de agua en los SAAs. También, en el caso de las tuberías, se puede obtener otro tipo de características tales como el diámetro y el material. Como resultado de esta tesis se han desarrollado una serie de herramientas que permiten visualizar, identificar y localizar componentes de los SAAs a partir de imágenes de GPR. El resultado más interesante es que los resultados obtenidos son sintetizados y reducidos de manera que preservan las características de los diferentes componentes registrados en las imágenes de GPR. El objetivo último es que las herramientas desarrolladas faciliten la toma de decisiones en la gestión técnica de los SAAs y que tales herramientas puedan ser operadas incluso por personal con una experiencia limitada en el manejo[CA] Amb el temps, a causa de les múltiples activitats d'operació i manteniment, les xarxes de sistemes d'abastament d'aigua (SAAs) se sotmeten a intervencions, modificacions o fins i tot estan tancades. En molts casos, aquestes activitats no estan degudament registrats. El coneixement dels camins i característiques (estat i edat, etc.) de les canonades d'aigua i sanejament fa evident la necessitat d'una gestió eficient i dinàmica d'aquests sistemes. Aquest problema es veu augmentat en gran mesura tenint en compte la detecció i control de fuites. L'accés a informació fiable sobre les fuites és una tasca complexa. En molts casos, les fugues es detecten quan el dany ja és considerable, el que porta costos socials i econòmics. En aquest sentit, els mètodes no destructius (per exemple, ground penetrating radar - GPR) poden ser una resposta constructiva a aquests problemes, ja que permeten, com s'evidencia en aquesta tesi, per determinar rutes de canonades, identificar les característiques dels components, i detectar les fuites d'aigua quan encara no són significatives. La selecció del GPR en aquest treball es justifica per les seves característiques com a tècnica no destructiva que permet estudiar tant objectes metàl·lics i no metàl·lics. Tot i que la captura d'informació amb GPR sol ser reeixida, aspectes com ara la configuració de captura, el gran volum d'informació que es genera, i l'ús i la interpretació d'aquesta informació requereix alt nivell d'habilitat i experiència. Aquesta tesi pot ser vista com un pas endavant cap al desenvolupament d'eines capaces d'abordar el problema de la manca de coneixement sobre els actius d'aigua i sanejament enterrat. L'objectiu principal d'aquest treball doctoral és, doncs, generar eines i avaluar la seva factibilitat d'aplicació a la caracterització dels components de los SAAs, a partir d'imatges GPR. En aquest treball s'han dut a terme proves de laboratori específicament dissenyats per proposar, desenvolupar i avaluar mètodes per a la caracterització dels components d'aigua i sanejament soterrat. A més, hem dut a terme proves de camp, que ens han permès determinar la viabilitat de la implementació d'aquestes metodologies en condicions no controlades. Les metodologies desenvolupades es basen en tècniques d'anàlisi intel·ligent de dades. El principi bàsic d'aquest treball ha consistit en el tractament de dades obtingudes a través del GPR per buscar informació útil sobre els components d'SAA, amb especial èmfasi en la canonades. Després de realitzar nombroses activitats, es pot concloure que, amb l'ús d'imatges de GPR, és factible obtenir més informació que la identificació típica d'hipèrboles realitzat actualment. A més, aquesta informació pot ser observada directament, per exemple, més simplement, utilitzant les metodologies proposades en aquest treball doctoral. Aquestes metodologies també demostren que és factible per identificar patrons (especialment el pre-processat amb l'algoritme Agent race) que proporcionen bastant bona aproximació de la localització de fuites en SAAs. També, en el cas de tubs, es pot obtenir altres característiques com ara el diàmetre i el material. Els principals resultats d'aquesta tesi consisteixen en una sèrie d'eines que hem desenvolupat per localitzar, identificar i visualitzar els components dels SAAS a partir d'imatges GPR. El resultat més interessant és que els resultats obtinguts són sintetitzats i reduïts de manera que preserven les característiques dels diferents components registrats en les imatges de GPR. L'objectiu final és que les eines desenvolupades faciliten la presa de decisions en la gestió tècnica de SAA, i que tals eines poden fins i tot ser operades per personal amb poca experiència en el maneig de metodologies no destructives, específicament GPR.Ayala Cabrera, D. (2015). Characterization of components of water supply systems from GPR images and tools of intelligent data analysis [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/59235TESISPremios Extraordinarios de tesis doctorale

Deep learning processing and interpretation of ground penetrating radar data using a numerical equivalent of a real GPR transducer

Ground-Penetrating Radar (GPR) is a popular non-destructive electromagnetic (EM) technique that is used in diverse applications across different fields, most commonly geophysics and civil engineering. One of the most common applications of GPR is concrete scanning, where it is used to detect structural elements and support the assessment of its condition. However, in any GPR application, the data have no resemblance to the characteristics of targets of interest and a means of extracting information from the data regarding the targets is required. Interpreting the GPR data, to infer key properties of the subsurface and to locate the targets is a difficult and challenging task and is highly dependent on the processing of the data and the experience of the user. Traditional processing techniques have some drawbacks, which can lead to misinterpretations of the data in addition to the interpretation being subjective to the user. Machine learning (ML) has proven its ability to solve a variety of problems and map complex relationships and in recent years, is becoming an increasingly attractive option for solving GPR and other EM problems regarding processing and interpretation. Numerical modelling has been extensively used to understand the EM wave propagation and assist in the interpretation of GPR responses. If ML is combined with numerical modelling, efficient solutions to GPR problems can be acquired. This research focuses on developing a numerical equivalent of a commercial GPR transducer and utilising this model to produce realistic synthetic training data sets for deep learning applications. The numerical model is based on the high-frequency 2000 MHz "palm" antenna from Geophysical Survey Systems, Inc. (GSSI). This GPR system is mainly used for concrete scanning, where the targets are located close to the surface. Unknown antenna parameters were found using global optimisation by minimising the mismatch between synthetic and real responses. A very good match was achieved, demonstrating that the model can accurately replicate the behaviour of the real antenna which was further validated using a number of laboratory experiments. Real data were acquired using the GSSI transducer over a sandbox and reinforced concrete slabs and the same scenarios were replicated in the simulations using the antenna model, showing excellent agreement. The developed antenna model was used to generate synthetic data, which are similar to the true data, for two deep learning applications, trained entirely using synthetic data. The first deep learning application suggested in the present thesis is background response and properties prediction. Two coupled neural networks are trained to predict the background response given as input total GPR responses, perform background removal and subsequently use the predicted background response to predict its dielectric properties. The suggested scheme not only performs the background removal processing step, but also enables the velocity calculation of the EM wave propagating in a medium using the predicted permittivity value. The ML algorithm is evaluated using a number of synthetic and measured data demonstrating its efficiency and higher accuracy compared to traditional methods. Predicting a permittivity value per A-scan included in a B-scan results in a permittivity distribution, which is used along with background removal to perform reverse-time migration (RTM). The proposed RTM scheme proved to be superior when compared with the commonly used RTM schemes. The second application was a deep learning-based forward solver, which is used as part of a full-waveform inversion (FWI) framework. A neural network is trained to predict entire B-scans given certain model parameters as input for reinforced concrete slab scenarios. The network makes predictions in real time, reducing by orders of magnitude the computational time of FWI, which is usually coupled with an FDTD forward solver. Therefore, making FWI applicable to commercial computers without the need of high-performance computing (HPC). The results clearly illustrate that ML schemes can be implemented to solve GPR problems and highlight the importance of having a digital representation of a real transducer in the simulations

Big Hole (41TV2161): Two Stratigraphically Isolated Middle Holocene Components in Travis County, Texas Volume I

During April and May 2006, an archeological team from the Cultural Resources Section of the Planning, Permitting and Licensing Practice of TRC Environmental Corporation’s (TRC) Austin office conducted geoarcheological documentation and data recovery excavations at prehistoric site 41TV2161 (CSJ: 0440-06-006). Investigations were restricted to a 70 centimeter (cm) thick target zone between ca. 220 and 290 cm below surface (bs) on the western side of site 41TV2161 – the Big Hole site in eastern Travis County, Texas. This cultural investigation was necessary under the requirements of Section 106 of the National Historic Preservation Act (NHPA), the implementing regulations of 36CRF Part 800 and the Antiquities Code of Texas (Texas Natural Resource Code, Title 9, Chapter 191 as amended) to recover a sample of the significant cultural materials prior to destruction by planned construction of State Highway 130 (SH 130). The latter by a private construction firm – Lone Star Infrastructure. This necessary data recovery was for Texas Department of Transportation (TxDOT), Environmental (ENV) Affairs Division under a Scientific Services Contract No. 577XXSA003 (Work Authorization No. 57701SA003). Over the years since the original award, multiple work authorizations between TxDOT and TRC were implemented and completed towards specific aspects of the analyses and reporting. The final analyses and report were conducted under contract 57-3XXSA004 (Work Authorization 57-311SA004). All work was under Texas Antiquities Committee Permit No. 4064 issued by the Texas Historical Commission (THC) to J. Michael Quigg. Initially, an archeological crew from Hicks & Company encountered site 41TV2161 during an intensive cultural resource inventory conducted south of Pearce Lane along the planned construction zone of SH 130 in the fall of 2005. Following the initial site discovery, archeologists expanded their investigations to the west across the SH 130 right-of-way, and completed excavation of 10 backhoe trenches, 13 shovel tests, and 11 test units at site 41TV2161. The investigations encountered at least seven buried cultural features and 1,034 artifacts, some in relatively good context. The survey and testing report to TxDOT presented their findings and recommendations (Campbell et al. 2006). The ENV Affairs Division of TxDOT and the THC reviewed the initial findings and recommendations, and determined site 41TV2161 was eligible for listing on the National Register of Historic Places and as State Antiquities Landmark as the proposed roadway development was to directly impact this important site and further excavations were required. Subsequently, TRC archeologists led by Paul Matchen (Project Archeologist) and J. Michael Quigg (Principal Investigator) initiated data recovery excavations through the mechanical-removal of between 220 and 250 cm of sediment from a 30-by-40 meter (m) block area (roughly 3,000 m3). This was conducted to allow hand-excavations to start just above the deeply buried, roughly 70 cm thick targeted zone of cultural material. Mechanical stripping by Lone Star Infrastructure staff created a large hole with an irregular bottom that varied between 220 and 260 cmbs. To locate specific areas to initiate hand-excavations within the mechanically stripped area, a geophysical survey that employed ground penetrating radar (GPR) was conducted by Tiffany Osburn then with Geo-Marine in Plano, Texas. Over a dozen electronic anomalies were detected through the GPR investigation. Following processing, data filtering, and assessment, Osburn identified and ranked the anomalies for investigation. The highest ranked anomalies (1 through 8) were thought to have the greatest potential to represent cultural features. Anomalies 1 through 6 were selected and targeted through hand-excavations of 1-by-1 m units that formed continuous excavation blocks of various sizes. Blocks were designated A, B, C, D, E, and F. The type, nature, quantity, and context of encountered cultural materials in each block led the direction and expansion of each excavation block as needed. In total, TRC archeologists hand-excavated 38.5 m3 (150 m2) from a vertically narrow target zone within this deep, multicomponent and stratified prehistoric site. Hand-excavation in the two largest Blocks, B and D (51 m2 and 62 m2 respectively), revealed two vertically separate cultural components between roughly 220 and 290 cmbs. The younger component was restricted to Block B and yielded a Bell/Andice point and point base, plus a complete Big Sandy point. These points were associated with at least eight small burned rock features, one cluster of ground stone tools, limited quantities of lithic debitage, few formal chipped and ground stone tools, and a rare vertebrate faunal assemblage. Roughly 20 to 25 cm below the Bell/Andice component in Block B and across Block D was a component identified by a single corner-notched Martindale dart point. This point was associated with a scattered burned rocks, three charcoal stained hearth features, scattered animal, bird, and fish bones, mussel shells, and less than a dozen formal chipped and ground stone tools. Both identified components contained cultural materials in good stratigraphic context with high spatial integrity. Significant, both were radiocarbon dated by multiple charcoal samples to a narrow 200-year period between 5250 and 5450 B.P. during the middle Holocene. With exception of the well-preserved faunal assemblages, perishable materials were poorly preserved in the moist silty clay loam. Charcoal lacked structure and was reduced to dark stains. Microfossils (e.g., phytoliths and starch gains) were present, although in very limited numbers and deteriorated conditions. The four much smaller Blocks (A, C, E, and F) yielded various quantities of cultural material and features, but these blocks also lacked sufficient charcoal dates and diagnostic artifacts Those artifacts and samples were left unassigned and analyzed separately from the Bell/Andice and Martindale components. The two well-defined components in Blocks B and D are the focus of this technical report. The components provide very significant data towards understanding rare and poorly understood hunter-gatherer populations during late stages of the Altithermal climate period. This final report builds upon the interim report submitted to TxDOT (Quigg et al. 2007) that briefly described the methods, excavations, preliminary findings, initial results from six feasibility studies, and proposed an initial research design for data analyses. Context and integrity of the cultural materials in the two identified components was excellent. This rare circumstance combined with detailed artifact analyses, solid documentation of their ages through multiple radiocarbon dates, and multidisciplinary approach to analyses, allowed significant insights and contributions concerning the two populations involved. Results provide a greater understanding of human behaviors during a rarely identified time in Texas Prehistory. The cultural materials and various collected samples were temporarily curated at TRC’s Austin laboratory. Following completion of analyses and acceptance of this final report, the artifacts, paper records, photographs, and electronic database were permanently curated at the Center for Archaeological Studies (CAS) at Texas State University in San Marcos

Remote Sensing and Geosciences for Archaeology

This book collects more than 20 papers, written by renowned experts and scientists from across the globe, that showcase the state-of-the-art and forefront research in archaeological remote sensing and the use of geoscientific techniques to investigate archaeological records and cultural heritage. Very high resolution satellite images from optical and radar space-borne sensors, airborne multi-spectral images, ground penetrating radar, terrestrial laser scanning, 3D modelling, Geographyc Information Systems (GIS) are among the techniques used in the archaeological studies published in this book. The reader can learn how to use these instruments and sensors, also in combination, to investigate cultural landscapes, discover new sites, reconstruct paleo-landscapes, augment the knowledge of monuments, and assess the condition of heritage at risk. Case studies scattered across Europe, Asia and America are presented: from the World UNESCO World Heritage Site of Lines and Geoglyphs of Nasca and Palpa to heritage under threat in the Middle East and North Africa, from coastal heritage in the intertidal flats of the German North Sea to Early and Neolithic settlements in Thessaly. Beginners will learn robust research methodologies and take inspiration; mature scholars will for sure derive inputs for new research and applications

Innovative Methods and Materials in Structural Health Monitoring of Civil Infrastructures

In the past, when elements in sructures were composed of perishable materials, such as wood, the maintenance of houses, bridges, etc., was considered of vital importance for their safe use and to preserve their efficiency. With the advent of materials such as reinforced concrete and steel, given their relatively long useful life, periodic and constant maintenance has often been considered a secondary concern. When it was realized that even for structures fabricated with these materials that the useful life has an end and that it was being approached, planning maintenance became an important and non-negligible aspect. Thus, the concept of structural health monitoring (SHM) was introduced, designed, and implemented as a multidisciplinary method. Computational mechanics, static and dynamic analysis of structures, electronics, sensors, and, recently, the Internet of Things (IoT) and artificial intelligence (AI) are required, but it is also important to consider new materials, especially those with intrinsic self-diagnosis characteristics, and to use measurement and survey methods typical of modern geomatics, such as satellite surveys and highly sophisticated laser tools

Data Recovery Investigations at the Tank Destroyer Site (41CV1378) at Fort Hood, Coryell County, Texas

Data recovery investigations at the Tank Destroyer site (41CV1378) were conducted in August 2007 for the Texas Department of Transportation (TxDOT). This work was required because of potential impacts to the site from TxDOT’s planned improvements of Tank Destroyer Boulevard and State Highway 9. The investigations focused on a burned rock mound (Feature 1), one-half of which has been destroyed by an adjacent tank trail. The mound contained two internal features: an off-centered earth oven and a small cluster of Rabdotus sp. shells. With the exception of the location of its earth oven, the mound at the Tank Destroyer is typical of a classic central Texas domed mound, though slightly flattened by postdepositional processes. In all, an area of 30.5 m2 and volume of 11.8 m3 of cultural deposits were hand excavated, and an additional ca. 17.3 m2 was mechanically stripped. The mound excavations yielded 5,570.5 kg of burned rocks. Artifacts recovered from mound and nonmound contexts consist of 129 chipped stone tools, 9 cores and core fragments, 4,466 pieces of unmodified debitage, 1 ground stone tool, 2 unmodified bone fragments, 1,415 Rabdotus sp. shells, and 40 historic artifacts. In addition, 413 pieces of microdebitage and 251 Rabdotus sp. shells were recovered from flotation and soil column samples taken from the mound. There was virtually no preservation of vertebrate faunal remains and poor preservation of botanical remains. No economic plants (i.e., food resources) were recovered despite the collection and processing of flotation samples. Sixteen radiocarbon assays on charred wood and Rabdotus sp. shells date the site occupation to 1500 b.c. through a.d. 1650. The date range for the diagnostic projectile points recovered from the site (200 b.c. to a.d. 1200) fits nicely within the range of radiocarbon dates. As a group, the radiocarbon dates and the projectile points suggest that the most intensive period of site use occurred intermittently between 1000 b.c. and a.d. 1200. Like most burned rock mounds, the mound at the Tank Destroyer site consisted of a jumbled mass of burned rocks that episodically accreted around an earth oven. These processes and repeated use over centuries limit our ability to recognize distinct components for analysis. Given these limitations, our analysis took a different approach. While it includes traditional analyses of the lithic, burned rock, and snail assemblages, it also examines social identity during the Late Archaic period in central Texas and the relationships between burned rock mounds and middens and environmental variables through a landscape analysis