Scour is a major cause of bridge collapse worldwide. Conventional techniques directly monitor the depth of soil near foundations. However, when scouring occurs, the holes can be filled with sediment, and so the scour cannot be detected by these methods. This paper presents an indirect, consequential method for an early and robust detection of bridge pier scour. Scour is detected using vibration analysis, by monitoring the dynamic response of the structure. The principle of the method is therefore to identify small variations in foundation stiffness from small variations in measured frequencies. The bridge and its piers are modeled by beams, and the foundations by springs, to obtain an expression for the sensitivity of frequencies to foundation stiffnesses. This expression is the result of a first-order perturbation calculation. Frequency variations are therefore expressed as linear combinations of the stiffness variations of the multiple foundations. This approach is tested on a digital model of 8-span bridge over the Loire River on the A85 motorway. The modelling assumptions of the bridge were validated using available ambient vibration measurements. Several scour scenarios were simulated by reducing the initial values of foundation stiffnesses. For the type under consideration in this paper, it was found out that the frequencies of the first out-of-plane modes of the bridge are sensitive to scour, depend little on the detailed functioning of the support. This method is effective because it is based on a robust inversion algorithm that prevents an amplification of the uncertainties in the variation of the measured frequencies. Foundation stiffness variations of 5% can be detected when the relative variation in useful frequencies is of the order of 1%. This level of variation is compatible with the uncertainty associated with the measurement chain and the operational modal analysis performed by professional software.

In recent years, fiber optic sensors are widely used in structural health monitoring due to their highspatial resolution distributed measurements. However, strain transfer through protective coatings of the sensors can distort measurements, especially in high strain gradients. To correct this, mathematical models of strain transfer have been developed and validated through numerical simulations and experiments. Recovering the actual strain profile involves inverting these models, particularly a 1D second-order differential equation derived from a 3D model. This equation, subject to boundary conditions, must be solved repeatedly for efficient numerical inversion. Given the high sampling frequencies of fiber optic sensors, fast algorithms are crucial for real-time applications such as vibration analysis. This paper proposes an efficient method to solve the 1D strain transfer equation and its inverse problem. By enabling rapid post-processing of strain measurements, this approach aims to enhance the application of fiber optic sensors in vibration analysis and structural health monitoring.

The scour is one of the main reasons for bridge collapse. Soil removal around bridge foundations alters their embedment conditions and affects the stability of the structure. Therefore, scour detection is crucial to ensure the safety of bridges and prevent their failures. This paper presents a vibration-based analysis method for detecting scour in its early stages, using frequency values as a scour indicator. Scour is represented by the degradation of foundation stiffness. A robust expression is developed to calculate the variation of frequency as a function of stiffness variations. Different scour scenarios are numerically simulated on a steel-concrete bridge model, revealing that frequencies related to horizontally displaced modes are sensitive to scour, while the deck bending modes are insensitive. The SVD method used to solve the inverse problem has proven to be robust in estimating stiffness reduction based on frequency changes and identifying the location of scour.

A new identification technique is proposed to evaluate the tension of a cable using a static inverse approach that couples a universal cable model with displacement sensors, strain gauges and added masses that should preserve operational affordability. An inverse problem is formulated as the minimization of a data misfit functional based on the differences in terms of vertical displacements and axial strains between two equilibrium configurations of the cable, namely, one loaded and the other free. The inverse problem formulation echoes the parametric study of a non-conventional functional suggesting a way to identify the cable parameters, namely, its length, its axial stiffness, and its mass per unit length. The computational resolution of the inverse problem is implemented as a two-step identification procedure. First, the axial stiffness and mass per unit length are kept constant and the length of the cable is approximately found via a simple line search algorithm using finite differences to estimate the functional derivatives. Second, the other physical parameters are assessed using an adjoint method for which the direct problem, the adjoint problem, and the parameter sensitivities are defined as derivatives of a Lagrangian functional with respect to dual variables, primal variables, and parameters, respectively. Due to the ill-conditioning of the problem, the proposed method does not enable an exact parameter identification but yields a good tension assessment. An experimental test campaign conducted on a multilayered 21-m long stranded cable subject to several tension levels confirms the relevance of the proposed inverse method. A field test campaign of the method on three 120-m long cables of Bonny-sur-Loire (France) suspension bridge is also presented. It proves the reliability and affordability of the overall tension identification process.

The challenges of understanding the impacts of air pollution require detailed information on the state of air quality. While many modeling approaches attempt to treat this problem, physically-based deterministic methods are often overlooked due to their costly computational requirements and complicated implementation. In this work we extend a non-intrusive Reduced Basis Data Assimilation method (known as PBDW state estimation) to large pollutant dispersion case studies relying on equations involved in chemical transport models for air quality modeling. This, with the goal of rendering methods based on parameterized partial differential equations (PDE) feasible in air quality modeling applications requiring quasi-real-time approximation and correction of model error in imperfect models. Reduced basis methods (RBM) aim to compute a cheap and accurate approximation of a physical state using approximation spaces made of a suitable sample of solutions to the model. One of the keys of these techniques is the decomposition of the computational work into an expensive one-time offline stage and a low-cost parameter-dependent online stage. Traditional RBMs require modifying the assembly routines of the computational code, an intrusive procedure which may be impossible in cases of operational model codes. We propose a less intrusive reduced order method using data assimilation for measured pollution concentrations, adapted for consideration of the scale and specific application to exterior pollutant dispersion as can be found in urban air quality studies. Common statistical techniques of data assimilation in use in these applications require large historical data sets, or time-consuming iterative methods. The method proposed here avoids both disadvantages. In the case studies presented in this work, the method allows to correct for unmodeled physics and treat cases of unknown parameter values, all while significantly reducing online computational time. (C) 2019 Elsevier Inc. All rights reserved.

As the population increases, cities must constantly reassess their urban planning. However, this must be done in such a way to preserve the quality of life of its inhabitants. Energy saving, sustainable water and air quality are some of the important challenges associated with growing cities. In this context, the monitoring of the different urban flows (pollution, heat) is very important. For instance data assimilation approach can be used in monitoring. These methods incorporate available measurement data and mathematical model to provide improved approximations of the physical state. The effectiveness of modeling and simulation tools is essential. Advanced physically based models could provide spatially rich small-scale solution, however the use of such models is challenging due to explosive computational times in real-world applications. Beyond computational costs, physical models are often constrained by available knowledge on the physical system. To overcome these difficulties, we resort the Parameterized-Background Data-Weak (PBDW) method introduced in [1]. The PBDW formulation combines a set of solutions (a reduced basis [2]) from the physically based model, and the experimental observations, in order to provide a real-time and in-situ state estimate. The reduced basis is used to diminish the cost of using a high-resolution model by exploiting the parametric structure of the governing equations. In addition, variational data-assimilation techniques are used to correct the model error. In this work we extend the PBDW method to the monitoring of urban flows as an important use case but also as an example of the very generic approach that proves well suited to Online monitoring over large scales. In case studies presented here, the method allows to correct for unmodeled physics and treat cases of unknown parameter values, all while significantly reducing online computational time. REFERENCES [1] Y. Maday, A.T Patera, J.D. Penn and M. Yano, “A parameterized-background data-weak approach to variational data assimilation: formulation, analysis, and application to acoustics”, Int. J. Numer. Meth. Engng (2014).

In this paper are reported the results of the application of several damage identification algorithms to a structure tested on a shaking table in the realm of the International Benchmark SMART 2011. The structure, a scaled torsionally asymmetric three story trapezoidal reinforced concrete mock up, was designed according to the French nuclear regulation to represent a part of a typical nuclear electrical building. Shaking table tests were carried out to investigate the non linear seismic response of the specimen under strong ground motions. Before and after the shaking table tests, hammer tests were performed to retrieve the dynamic parameters of the structure from the measured Frequency Response Functions. The latter data have been used in this paper to apply and compare several damage identification algorithms based on modal parameters (frequencies, modal shapes and curvatures) and on Frequency Response Functions. Results given by different methods are reported in the paper together with a discussion about relevant pros and cons.

This work proposes a new cable tension identification technique based on a static inverse method that, by coupling a universal cable model with displacement and strain sensors data, exploits the differences between the original cable equilibrium problem and that of the cable loaded by a suitable added mass. The formulated inverse problem thus defines a data misfit functional based on the differences in terms of transverse displacements and elongations between the two equilibrium configurations. The inverse problem is implemented in a two-step identification procedure. First, the axial stiffness and mass per unit length are kept constant and the length of the cable is approximately found via a simple line search algorithm using finite differences to estimate the functional derivatives. Second, the other physical parameters are assessed using an adjoint method for which the direct problem, the adjoint problem and the parameters sensitivities are found as derivatives of a Lagrangian functional with respect to dual variables, primary variables, and parameters, respectively. Due to the ill-conditioning nature of the problem, the proposed method does not allow an exact parameter identification but it does lead to an acceptable tension assessment. An experimental test campaign conducted on a multilayered stranded cable 21 m long and 22 mm in diameter subject to several tension levels confirms the relevance and operational feasibility of the proposed inverse method.

By combining a physical model and sensor outputs in an inverse transport-diffusion-reaction strategy, an accurate concentration cartography may be obtained. The paper addresses the influence of discretization errors, flow uncertainties, and measurement noise on the concentration field reconstruction process. We consider a key element of a drinking water network, i.e., a pipe junction, where Reynolds and Peclet numbers are approximately 2000 and 1000, respectively. We show that a 10% error between the reference concentration field and the reconstructed concentration field may be obtained using a coarse discretization. Nevertheless, to keep the error below 10%, a fine concentration discretization is required. We also detail the influence of the flow approximation on the concentration reconstruction process. The flow modeling error obtained when the exact Navier–Stokes flow is approximated by a Stokes flow may lead to a 40% error in the reconstructed concentration. However, if the flow field is obtained from the full set of Navier–Stokes equations, we show that the error may be less than 5%. Then, we observe that the quality of the reconstructed concentration field obtained with the proposed inverse technique is not deteriorated when sensor outputs have a normal distribution noise variance of few percents. Finally, a good engineering practice would be to stop the reconstruction process according to an extended discrepancy principle including modeling and measurement errors. As shown in the paper, the quality of the reconstructed field declines after reaching the threshold of the modeling error.

With increased pollutant emissions and exposure due to mass urbanization worldwide, air quality measurement campaigns and epidemiology studies on air pollution and health effects have become increasingly common to estimate individual exposures and evaluate their association to various illnesses. As air pollution concentrations are known to be highly heterogeneous, sophisticated physically based air quality models (AQMs), in particular models based on Computational Fluid Dynamics, can provide spatially rich approximations and enable to better estimate individual exposure. In this work we investigate reduced basis (RB) methods [1] to diminish the resolution cost of advanced AQMs developed for concentration evaluation at urban scales. These models depend on varying parameters including meteorological conditions and pollutant emissions, often unknown at the micro scale. RB methods use approximation spaces made of suitable samples of solutions of AQMs governed by parameterized partial differential equations (PDEs), to rapidly construct accurate and computationally efficient approximations. A key to this technique is decomposing computational work into an offline and online stage. The RB functions used to build approximation spaces and all expensive parameter-independent terms, are computed 'offline' once and stored, whereas inexpensive parameter-dependent quantities are evaluated 'online ' for each new value of the parameters. However, the decomposition of the matrices into offline-online pieces requires modifying the calculation code, an intrusive procedure, which in some situations is impractical. In this work, we extend the Parameterized-Background Data-Weak (PBDW) method introduced in [2] to physically based AQMs. We will generate a sample of solutions from physical AQMs with varying meteorological conditions and pollution emissions to build the RB approximation space and combine it with experimental observations, using the method in [3], to improve pollutant concentration estimations, with the goal of collaboration with an epidemiology exposure assessment team at the University of California-Berkeley. The goal is to rapidly estimate 'online' pollutant concentration(s) around an area of interest at micro scale, using available AQMs in a non-intrusive and computationally efficient manner. REFERENCES[1] Prud'homme, C., Rovas, D. V., Veroy, K., Machiels, L., Maday, Y., Patera, A. T., & Turinici, G. (2002). 'Reliable real-time solution of parametrized partial differential equations: Reduced-basis output bound methods'. Journal of Fluids Engineering, 124(1), 70-80 [2] Y. Maday, A.T Patera, J.D. Penn and M. Yano, 'A parameterized-background data-weak approach to variational data assimilation: formulation, analysis, and application to acoustics', Int. J. Numer. Meth. Engng (2014).[3] Yvon Maday and Olga Mula. A generalized empirical interpolation method: application of reduced basis techniques to data assimilation. In Analysis and numerics of partial differential equations, pages 221-235. Springer, 2013.

The paper deals with the results of an experimental campaign carried out on a post tensioned concrete beam with the aim of investigating the possibility to detect early warning signs of deterioration based on static and/or dynamic tests. The beam was tested in several configurations aimed to reproduce 5 different phases of the 'life' of the beam: in the original undamaged state, under increasing loss of tension in the post tensioning cables, during and after the formation of cracks at mid span, after a strengthening intervention carried out by means of a second tension cable, during and after the formation of further cracks on the strengthened beam. Responses of the beam were measured by an extensive set of instruments consisting of accelerometers, inclinometers, displacement transducers, strain gauges and optical fibers. In this paper data from accelerometers and displacement transducers have been exploited. The paper presents the test program and the dynamic characterization of the beam in the different damage scenarios in terms of the first modal frequency, identified from dynamic tests and of the bending stiffness monitored during static tests. (C) 2016 Elsevier Ltd. All rights reserved.

Electricity consumption in urban railway stations accounts for almost one third of the total energy consumption of a subway network of a city like Paris. The overall system's efficiency can be optimized by taking advantage of available sources of energy such as regenerative braking of trains or local renewable energy resources. This can be achieved by handling the intermittent nature of the various sources and consumption points and by redesigning the station energy grid in a global approach. Microgrids have been an actively researched subject since a few years with the growing interests in smart electricity networks as a mean to decentralize the global power supply facilities. We present hereby a methodology for the optimal management of a microgrid connecting regenerative braking energy sources, eventual distributed energy resources, heating, ventilation, air conditioning (HVAC) systems, specific electricity consumptions and electricity storage systems (ESS) for energy management in subway stations. The overall energy cost is minimized adjusting in real-time electricity demand and availability using demand-response strategies while ensuring an optimal thermal comfort and a safe indoor air quality environment. (C) 2016 The Authors. Published by Elsevier B.V.

Two additive thermal sources are generally not simultaneously distinguishable from the only observation of their effect on the heat balance. However, there are cases where information about the variation regularity of these sources is known. This is typically the case of convective internal gains in the building, for which the use scenarios create discontinuous inputs while heat gains relating to the air leakage are regular in time. In the present paper, we introduce a method aiming to distinguish heat sources using this a priori knowledge about their dynamics. We provide numerical and experimental evidence that the method succeeds in separating/distinguishing these kind of sources. This method could be applied to the identification of the occupancy rate for measurement and verification (M&V) plans or smart home systems such as learning thermostats.

Air quality measurement campaigns and epidemiology studies on air pollution and children's health have been ongoing in California for over a decade, to estimate individual exposures and evaluate it to various type of illness. As air pollution concentrations are known to be highly heterogeneous, sophisticated physically based AQMs, in particular CFD-based models, can provide spatially rich approximations and enable to better estimate individual exposure. In this paper we investigate reduced basis (RB) methods [1] to diminish the resolution cost of these advanced AQMs developed for California. These models depend on varying parameters including meteorological conditions and pollutant emissions, often unknown at the micro scale. RB methods use approximation spaces made of suitable samples of solutions of AQMs governed by parameterized partial differential equations (PDEs), to rapidly construct accurate and computationally efficient approximations. A key to this technique is decomposing computational work into an offline and online stage. The RB functions used to build approximation spaces and all expensive parameter-independent terms, are computed 'offline' once and stored, whereas inexpensive parameter-dependent quantities are evaluated 'online ' for each new value of the parameters. However, the decomposition of the matrices into offline-online pieces requires modifying the calculation code, an intrusive procedure, which in some situations is impractical. In this work, we extend the non-intrusive RB and data assimilation methods introduced in [2-3] to physically based AQMs. We will generate a sample of solutions from physical AQMs with varying meteorological conditions and pollution emission to build the RB approximation space and combine it with the experimental observations provided by the epidemiology exposure assessment team at UC-Berkeley. The goal is to rapidly estimate 'online' pollutant concentration(s) around an area of interest at micro scale. References : [1] Prud'homme, C., Rovas, D. V., Veroy, K., Machiels, L., Maday, Y., Patera, A. T., & Turinici, G. (2002). 'Reliable real-time solution of parametrized partial differential equations: Reduced-basis output bound methods'. Journal of Fluids Engineering, 124(1), 70-80. [2] R. Chakir, P. Joly, Y. Maday and P. Parnaudeau, 'A Non-intrusive reduced basis method: application to computational fluid dynamics', 2nd ECCOMAS Young Investigators Conference (YIC 2013), Sep 2013, Bordeaux, France, . [3] Y. Maday, A.T Patera, J.D. Penn and M. Yano, 'A parameterized-background data-weak approach to variational data assimilation: formulation, analysis, and application to acoustics', Int. J. Numer. Meth. Engng (2014).

Accurate building performance assessment is necessary for the design of efficient energy retrofit operations and to foster the development of energy performance contracts. An important barrier however is that simulation tools fail to accurately predict the actual energy consumption. We present a methodology combining thermal sensor output and inverse algorithms to determine the key parameters of a multizone thermal model. The method yields calibrated thermal models that are among the most detailed ones in the literature dealing with building thermal identification. We evaluate the accuracy of the resulting thermal model through the computation of the energy consumption and the reconstruction of the main energy flux. Our method enables one to reduce standard uncertainties in the thermal state and in the quantities of interest by more than 1 order of magnitude. (C) 2016 Elsevier B.V. All rights reserved.

Nowadays, many projects have been conducted in order to reduce CO2 emissions, with the objective of reducing energy consumption. In the context of the urban railway area, the energy consumption is huge. It is respectively split into 70% for the traction and 30% for station consumers. Many works have already been carried out on traction systems, but very few of them were oriented towards the station energy problematic. This paper describes the project led by "Efficacity" Institute which concerns the use of the braking energy to manage and optimize the railway station energy consumption.Efficacity investigates energetic concepts in order to store the braking energy of the trains with a stationary electrical saving system, and to reutilize it for the power supply of electric and thermal consumers or actuators in a railway station thanks to a microgrid. The idea is to store train braking energy in hybrid storage system (composed of batteries and super-capacitors cells) and to restate it judiciously at different moments of the day (during peak or low energy consumption hours) to various kind of station loads. (C) 2016 The Authors. Published by Elsevier B.V.

During previous works, square pulsed thermography was used to carry out non destructive testing of bonding quality of CFRP glued on civil engineering structures during reinforcement operations [1,2]. The use of such wave form excitation was motivated by on-site requirements, but also by measurements duration, number of composite layers to test, depth of possible faulting areas versus temperature elevation allowed at composite level according to inner heat diffusion. Nevertheless, square pulsed excitation implies to choose an adapted heat duration. This duration is directly linked to the reliability of the parameter estimator [3]. In fact, after a certain duration the standard deviation of the estimation procedure stagnates. According to these observations, an indicator able to predict the sufficient heating time when the reliability of the parameter estimator reached an asymptotic evolution behavior was studied. Based on the absolute thermal contrast, the proposed indicator Iph is defined with the maximum thermal contrast ∆Tmax and the time delay (tph) between the heating time tc and the appearance of the maximum contrast, as shown in figure 1 (left). A typical evolution of the Iph indicator is proposed in figure 1 (right). This indicator allows to take into acount the detectability as well as the induced flaw temporal effect on the thermal contrast shape evolution. It has been observed that the maximum of Iph is connected with the sufficient heating time when the standard deviation of the estimation procedure tends to be minimized. This paper will present the establishment of this indicator for optimal square heating time and present an analysis of results obtained with numerical simulations and laboratory experiments. Figure 1 Absolute thermal contrast characteristics (left) and indicator Iph evolution with heating time duration References [1] A. Criniere, J. Dumoulin, C. Ibarra-Castanedo and X. Maldague , Inverse model for defect characterization of externally glued CFRP on reinforced concrete structures: Comparative study of square pulsed and pulsed thermography ,] L-D. Theroux, J. Dumoulin and X. Maldague , Square heating applied to shearography and active infrared thermography measurements coupling: from feasibility test in laboratory to numerical study of pultruded CFRP plates glued on concrete specimen ,Strain journal, Wiley editor, 2014. doi:10.1111/str.12086. [3] V. Feuillet, L. Ibos, M. Fois, J. Dumoulin, Y. Candau, Defect detection and characterization in composite materials using Square Pulse Thermography coupled with Singular Value Decomposition analysis and thermal quadrupole modeling , NDTu0026E International, Volume 51, Octobre 2012, pp 58–67, Elsevier, http://dx.

Our cities, from megalopolis to rural commune, are systems of an extraordinary technological and human complexity. Their balance is threatened by the growing population and rarefaction of resources. Massive urbanization endanges the environment, while global climate change, through natural hazards generated (climatic, hydrological and geological), threats people and goods. Connect the city, that is to say, design and spread systems able to route, between multiple actors, a very large amount of heterogeneous information natures and analyzed for various purposes, is at the heart of the hopes to make our cities more sustainable: climate-resilient, energy efficient and actresses of the energy transition, attractive to individuals and companies, health and environment friendly. If multiple players are already aware of this need, progress is slow because, beyond the only connectivity, it is the urban intelligence that will create the sustainable city, through coordinated capabilities of Perception, Decision and Action: to measure phenomena; to analyze their impact on urban sustainability in order to define strategies for improvement; to effectively act on the cause of the phenomenon. In this very active context with a strong societal impact, the Sense-City project aims to accelerate research and innovation in the field of sustainable city, particularly in the field of micro and nanosensors. The project is centered around a mini climatic City, a unique mobile environmental chamber in Europe of 400m 2 that can accommodate realistic models of city main components, namely buildings, infrastructures, distribution networks or basements. This R&D test place, available in draft form from January 2015 and in finalized version in 2016, will allow to validate, in realistic conditions, innovative technologies performances for the sustainable city, especially micro-and nano-sensors, at the end of their development laboratory and upstream of industrialization. R & D platform located in the heart of the Cite Descartes in Paris Est and open to both academic as industrial and communities, Sense-City participates in the positioning of the Cite Descartes as a flagship tertiary center for the city of the future. The areas of interest cover the energy performance of buildings and neighborhoods, the sanitary quality of the frame (indoor air pollution), the quality and sustainability of urban networks (transport, fluid), the quality of outdoor air, soil and water, control of waste storage areas, sustainability and infrastructure security. In the framework of this project, a first outdoor test bed was designed and built in 2014. Various sensing capacity have been implemented and first experimentations started in 2015. The project partners, IFSTTAR, ESIEE-CCIP LPICM (UMR CNRS Ecole Polytechnique), CSTB, INRIA and UPEM, controls the entire value chain for the development of innovative products for the sustainable city, nano or prototyping microsensors up to validation in real conditions, not to mention the steps of integration , packaging and deployment of the sensors or the processing steps, modeling and representation of information.