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    Corrosion diagnosis of reinforced concrete structures using autonomous robotic inspection systems and artificial intelligence

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    The infrastructure in industrialized countries, which relies heavily on reinforced con- crete (RC), is aging. Over time, corrosion of the steel reinforcement, the most common degradation mechanism, becomes more likely and may cause structural and prema- ture damage to the structure. In order to ensure that the large stock of infrastructure is managed safely, economically, and sustainably, engineers require more accurate diag- noses of the prevailing condition. Currently, routine inspections of RC structures are frequently limited to their simplest form, a visual inspection, which undoubtedly has severe drawbacks in terms of early corrosion detection. Non-destructive testing (NDT) methods could provide a more accurate diagnosis of the condition of structures, but the implementation in engineering practice on a routine basis is hindered by high costs and laborious measurements, especially at locations that are difficult to access. In addi- tion, the lack of established protocols to analyze inspection data is another challenge, particularly when data from different types of NDT methods are combined. The use of artificial intelligence (AI) can help overcome challenges with regards to inspection data analysis, thereby, improving efficiency, detection accuracy, and reliability through data fusion and removing subjective biases. To fully automate the inspection process, advances in robotic systems should be integrated into the inspection process together with sensors capable of self-assessing their functionality to collect data, especially with NDT methods. This thesis highlights different opportunities to integrate developments in sensor technology tailored for autonomous inspections with advanced robotic systems. Data analysis methods based on AI methods and geostatistical analysis were developed to improve several aspects of condition assessment and prediction of the (remaining) service life compared to the current methodology. Two new sensors were developed for the non-destructive contact-based condition as- sessment of RC structures. These sensors were specifically designed for use on flying robots and tailored for autonomous data acquisition. Each sensor allows for the com- bined measurement of two essential parameters in the condition assessment: the half-cell potentials (HCP) of the reinforcement and the concrete resistivity. One of the sensors is in the form of a point sensor that allows for the continuous moistening of the sponge (to ensure an electrolytical connection) and self-assessment of its functionality. The other comprises lightweight sensors in the shape of wheels to enable autonomous movement and increased data acquisition speed. Laboratory tests with both sensors were validated with data acquired manually. To examine the usability of the point sensor on-site, the point sensor was mounted on the flying robot and successfully tested on a bridge under realistic conditions. The results demonstrate the potential of the flying corrosion inspec- tion robot to autonomously collect data using multiple NDT methods on RC structures, possibly reducing inspection costs in the future. A novel technology for the non-destructive inspection of buried RC structures was developed, which may in the future evaluate the corrosion state of steel reinforcement of cantilever retaining walls, especially in structurally critical areas of the working joint. The measurement system comprised of a probe designed to reliably establish contact with the soil to take accurate HCP measurements without the immediate supervision of a human operator. This probe was combined with steered horizontal drilling technology to move the probe through the underground and incrementally place it close to the buried RC surface. In contrast to existing methods that are often destructive and with limited local information, the proposed solution overcomes spatial limitations and provides a more consistent and trustworthy corrosion inspection tool. Successful field tests support the promising approach for this novel NDT method for the corrosion assessment of cantilever retaining walls. This thesis presents new methods to improve the analysis of inspection data to enhance the diagnosis of structures by using geostatistical methods and AI. A geostatistical work- flow was proposed to investigate the effects of spatial variability on the interpretation of HCP mapping data during the condition assessment. The workflow distinguishes between long-range and short-range variability and mathematically expresses them as a trend and residuals. The results of this proposed workflow could help optimize the grid spacing or contribute to modeling deterioration processes using random fields. AI meth- ods were used to analyze a large dataset composed of inspection data from several NDT methods to locate the corroding reinforcement, to detect damage in surface images, and to assess hydrophobic treatments using active reflectance spectroscopy (ARS). Although the detection accuracy of neural networks was found to be high in training datasets to detect surface damage, it needs to be improved to support or partially replace visual inspection in engineering practice. The novel data analysis methods developed to locate corroding reinforcement showed better detection accuracy than existing approaches, making the analysis more reliable and less dependent on the engineers’ experience. ARS, combined with the developed data analysis methods, has the potential to detect hydrophobic surface treatments on RC surfaces on-site, making it a promising NDT method for the future. In addition to improving the assessment of the present condition of the structure, this thesis presents a method to strengthen the prediction of the service life of concrete structures using AI-based crack detection on surface images for structural parts exposed to chlorides. This method might be used after regular visual inspections or extended to be fully probabilistic. NDT methods offer a more accurate diagnosis, but are currently hindered by high costs, laborious measurements, and limited accessibility. This work presented multiple en- hancements to the current inspection methodology of RC structures. The individual parts of this work aimed to enable the automation of condition assessments, from capturing data with robotic systems and novel sensors tailored for autonomous measurements to data analysis with AI methods and forecasting the condition over time. These develop- ments have the potential to greatly improve the diagnosis of structures and ultimately reduce the negative impact of repairs on the budgets of infrastructure owners, the end users, and the environment

    Regional Recovery Modeling and Postdisaster Model Updating: The Case of the 2010 Kraljevo Earthquake in Serbia

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    Modeling disaster consequences and postdisaster recovery is a key enabler of holistic community disaster resilience planning and implementation. An important step toward the application of such models in practice consists in ensuring that model parameters can be reliably estimated, building trust in model outputs, as well as reducing output uncertainty. This study aims to tackle these issues first by constructing a regional housing recovery model and validating its results for a real event, the 2010 Kraljevo, Serbia earthquake, and second, by presenting how regional recovery models can be updated following a disaster using early-arriving damage inspection data to reduce output uncertainty. The model outputs are updated postevent using 600 building damage assessment reports, reducing the uncertainty in recovery time predictions. The results confirm the practical applicability of the proposed regional recovery model and postevent updating, while identifying its main shortcomings, such as the lack of consideration for the weather conditions affecting the recovery process and the need for better methods to estimate the communitys ability to mobilize its recovery resources.ISSN:0733-9445ISSN:1943-541

    Deep ensemble geophysics-informed neural networks for the prediction of celestial pole offsets

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    Celestial Pole Offsets (CPO), denoted by dX and dY, describe the differences in the observed position of the pole in the celestial frame with respect to a certain precession-nutation model. Precession and nutation components are part of the transformation matrix between terrestrial and celestial systems. Therefore, various applications in geodetic science such as high-precision spacecraft navigation require information regrading precession and nutation. For this purpose, CPO can be added to the precession-nutation model to precisely describe the motion of the celestial pole. However, as Very Long Baseline Interferometry (VLBI)—currently the only technique providing CPO—requires long data processing times resulting in several weeks of latency, predictions of CPO become necessary. Here we present a new methodology named Deep Ensemble Geophysics-Informed Neural Networks (DEGINNs) to provide accurate CPO predictions. The methodology has three main elements: (1) deep ensemble learning to provide the prediction uncertainty; (2) broad-band Liouville equation as a geophysical constraint connecting the rotational dynamics of CPO to the atmospheric and oceanic Effective Angular Momentum (EAM) functions and (3) coupled oscillatory recurrent neural networks to model the sequential characteristics of CPO time-series, also capable of handling irregularly sampled time-series. To test the methodology, we use the newest version of the final CPO time-series of International Earth Rotation and Reference Systems Service (IERS), namely IERS 20 C04. We focus on a forecasting horizon of 90 days, the practical forecasting horizon needed in space-geodetic applications. Furthermore, for validation purposes we generate an independent global VLBI solution for CPO since 1984 up to the end of 2022 and analyse the series. We draw the following conclusions. First, the prediction performance of DEGINNs demonstrates up to 25 and 33 percent improvement, respectively, for dX and dY, with respect to the rapid data provided by IERS. Secondly, predictions made with the help of EAM are more accurate compared to those without EAM, thus providing a clue to the role of atmosphere and ocean on the excitation of CPO. Finally, free core nutation period shows temporal variations with a dominant periodicity of around one year, partially excited by EAM.ISSN:0956-540XISSN:1365-246

    The Cauchy–Dirichlet problem for the fast diffusion equation on bounded domains

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    The Fast Diffusion Equation (FDE) uₜ = ∆uᵐ, with m ∈ (0,1), is an important model for singular nonlinear (density dependent) diffusive phenomena. Here, we focus on the Cauchy–Dirichlet problem posed on smooth bounded Euclidean domains. In addition to its physical relevance, there are many aspects that make this equation particularly interesting from the pure mathematical perspective. For instance: mass is lost and solutions may extinguish in finite time, merely integrable data can produce unbounded solutions, classical forms of Harnack inequalities (and other regularity estimates) fail to be true, etc. In this paper, we first provide a survey (enriched with an extensive bibliography) focussing on the more recent results about existence, uniqueness, boundedness and positivity (i.e., Harnack inequalities, both local and global), and higher regularity estimates (also up to the boundary and possibly up to the extinction time). We then prove new global (in space and time) Harnack estimates in the subcritical regime. In the last section, we devote a special attention to the asymptotic behaviour, from the first pioneering results to the latest sharp results, and we present some new asymptotic results in the subcritical case.ISSN:0362-546XISSN:1873-521

    Strengthening Hadwiger's conjecture for 4- and 5-chromatic graphs

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    Hadwiger's famous coloring conjecture states that every t-chromatic graph contains a Kt-minor. Holroyd [11] conjectured the following strengthening of Hadwiger's conjecture: If G is a t-chromatic graph and S⊆V(G) takes all colors in every t-coloring of G, then G contains a Kt-minor rooted at S. We prove this conjecture in the first open case of t=4. Notably, our result also directly implies a stronger version of Hadwiger's conjecture for 5-chromatic graphs as follows: Every 5-chromatic graph contains a K5-minor with a singleton branch-set. In fact, in a 5-vertex-critical graph we may specify the singleton branch-set to be any vertex of the graph.ISSN:0095-895

    Santa Claus meets Makespan and Matroids: Algorithms and Reductions

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    In this paper we study the relation of two fundamental problems in scheduling and fair allocation: makespan minimization on unrelated parallel machines and max-min fair allocation, also known as the Santa Claus problem. For both of these problems the best approximation factor is a notorious open question; more precisely, whether there is a better-than-2 approximation for the former problem and whether there is a constant approximation for the latter. While the two problems are intuitively related and history has shown that techniques can often be transferred between them, no formal reductions are known. We first show that an affirmative answer to the open question for makespan minimization implies the same for the Santa Claus problem by reducing the latter problem to the former. We also prove that for problem instances with only two input values both questions are equivalent. We then move to a special case called ``restricted assignment'', which is well studied in both problems. Although our reductions do not maintain the characteristics of this special case, we give a reduction in a slight generalization, where the jobs or resources are assigned to multiple machines or players subject to a matroid constraint and in addition we have only two values. This draws a similar picture as before: equivalence for two values and the general case of Santa Claus can only be easier than makespan minimization. To complete the picture, we give an algorithm for our new matroid variant of the Santa Claus problem using a non-trivial extension of the local search method from restricted assignment. Thereby we unify, generalize, and improve several previous results. We believe that this matroid generalization may be of independent interest and provide several sample applications. As corollaries, we obtain a polynomial-time (2−1/nǫ)-approximation for two-value makespanminimization for every ǫ > 0, improving on the previous (2 − 1/m) approximation, and a polynomial-time (1.75 + ǫ)-approximation for makespan minimization in the restricted assignment case with two values, improving the previous best rate of 1 + 2/√ 5 + ǫ ≈ 1.8945

    Magnetic fluid film enables almost complete drag reduction across laminar and turbulent flow regimes

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    In the race to curb energy and oil consumption, zeroing of wall frictional forces is highly desirable. The turbulent skin friction drag at the solid/liquid interface is responsible for substantial energy losses when conveying liquids through hydraulic networks, contributing approximately 10% to the global electric energy consumption. Despite extensive research, efficient drag reduction strategies effectively applicable in different flow regimes are still unavailable. Here, we use a wall-attached magnetic fluid film to achieve a wall drag reduction of up to 90% in channel flow. Using optical measurements supported by modelling, we find that the strong damping of wall friction emerges from the co-existence of slip and waviness at the coating interface, and the latter is a key factor to obtain almost complete wall drag reduction across laminar and turbulent flow regimes. Our magnetic fluid film is promising and ready to be applied in energy-saving and antifouling strategies in fluid transport and medical devices.ISSN:2399-365

    Free boundary partial regularity in the thin obstacle problem

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    In vitro investigation of the blood flow downstream of a 3D-printed aortic valve

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    The hemodynamics in the aorta as well as the durability of aortic valve prostheses vary greatly between different types of devices. Although placement and sizing of surgical aortic valve prostheses are excellent, the valve geometry of common devices cannot be customized to fit the patient’s anatomy perfectly. Similarly, transcatheter aortic valve implantation (TAVI) devices are not customizable and may be orientated unfavorably during implantation. Imperfect fit of an aortic valve prosthesis may result in suboptimal performance and in some cases the need for additional surgery. Leveraging the advent of precision, multi-material 3D-printing, a bioinspired silicone aortic valve was developed. The manufacturing technique makes it fully customizable and significantly cheaper to develop and produce than common prostheses. In this study, we assess the hemodynamic performance of such a 3D-printed aortic valve and compare it to two TAVI devices as well as to a severely stenosed valve. We investigate the blood flow distal to the valve in an anatomically accurate, compliant aorta model via three-dimensional particle tracking velocimetry measurements. Our results demonstrate that the 3D-printed aortic valve induces flow patterns and topology compatible with the TAVI valves and showing similarity to healthy aortic blood flow. Compared to the stenosis, the 3D-printed aortic valve reduces turbulent kinetic energy levels and irreversible energy losses by over 75%, reaching values compatible with healthy subjects and conventional TAVIs. Our study substantiates that the 3D-printed heart valve displays a hemodynamic performance similar to established devices and underscores its potential for driving innovation towards patient specific valve prostheses.ISSN:2045-232


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