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The unit influence line of a structure reflects its behaviour and changes in response to damage that may occur. An iterative algorithm is presented in this paper to obtain the shape of the instantaneous influence line of a bridge together with the relative axle loads of trucks passing overhead. One great advantage of this approach is that the need for sensor calibration with pre-weighed trucks can be avoided. The only initial information needed are the measurement data and a preliminary estimate of influence line based on engineering judgement. The concept of a so-called population unit influence line is also presented. This is an influence line that is found from a population of trucks instead of a single vehicle. An illustrative example is presented, where strain data have been collected on a reinforced concrete culvert. As well as the robustness of the proposed algorithms, the influence of temperature on the results is demonstrated. The sensitivity of the population influence line to temperature shows that it is likely to be equally sensitive to loss of stiffness in the structure.

In this paper a methodology is presented to update estimated level of corrosion damage using routine short-term health monitoring measurement data. The case study bridge is a reinforced concrete (RC) slab. It is subject to traffic loading from a weigh-in-motion database and time-dependent deterioration is modelled through corrosion. Different damage indicators based on different virtual sensor measurements (strain, deflection and rotation) are investigated. A method of comparing the performance of the damage indicators is presented and their data is used to achieve a Bayesian update of prior estimates of bridge reinforcement corrosion. It is shown how, with few and uncertain data, the estimation of deterioration level can be significantly improved, particularly with damage indicators based on rotation.

Installation, calibration protocol and data analysis of multi-electrode sensors embedded in concrete are proposed in this study. Measurements of resistivity are performed in a port wharf and analyzed in parallel with measurements of humidity, temperature, and evolution of the mechanical strength of concrete. The correlation between the evolution of the compressive strength and the resistivity, allows to validate the measurement protocol proposed. To visualize the distribution of resistivities in the studied volume, pseudo-sections of apparent resistivity were displayed. A methodology to plot and use them as an indicator of corrosion adapted to civil engineering structures is proposed.

Inspection by non-destructive testing (NDT) techniques is an effective way for assessing structures’ condition state, their pathologies and locating potential critical areas. However, establishing accurate diagnoses requires numerous measurements while available budget is limited. Nevertheless, determining the spatial variability of the material properties leads fewer of the required measurements, as it characterizes zones with almost identical properties and helps identify weak areas. But assessing this spatial variability still requires numerous measurements. It should be limited by a rational criterion that could be expressed in terms of accuracy and cost minimization. In the present work, we develop an adaptive approach to characterize spatial variability of structures’ material properties modelled by transformed Gaussian random fields. The methodology is performed in two steps. Firstly, the correlation length of the Gaussian random field is computed for a given accuracy. Secondly, the number of measurements is refined to estimate the first two moments of the random field with a given accuracy too. These two steps result in a minimization of the number of measurements (i.e the cost) for a target accuracy. The proposed methodology and theoretical results are illustrated using synthetic and real data.

The deployment of tidal technology is affected by the general bottlenecks associated with all new renewables in respect of finance and integration with the grid. In this research, a development strategy is defined for tidal range projects based on geodynamics, civil engineering, and economics with the aim of assisting policy makers and industry. Criteria related to hydrodynamics, bathymetry, marine structure safety and cost recovery apply to relevant sites and to real data power prices. The case study described is that of the Bay of Bourgneuf on the French Atlantic coast, where a tidal range power plant of 900 MW could optimally be built with respect to sedimentation, water depth, and tidal coefficients. It has been determined that a 30 m-high artificial dam could maximise the harvestable energy (3 TWh). Numerical simulations show that a tidal plant sized at just 700 MW would be cost-efficient, due to the constraints of the grid and to high power curtailment rates (30%). The expected value of the Levelised Cost of Electricity would be around 200€2016/MWh. Integration into the grid could be improved through addition of an innovative underwater energy storage system, rated to one third of the size of the tidal plant. The economics would improve (the LCOE would drop to 170€2016/MWh) due to lower curtailment and to price arbitrage opportunities. Issues related to missing investor money (>3Bln€2016) and unquantifiable positive externalities such as flood protection, energy independency, and clean energy provision are discussed, underpinning the need for regulator support.

Inflatable structures are part of the family of tensioned textile membrane structures. The operating principle is common for all these type of structures: the stiffness of the structure relies on t...

International audience; Structural reliability assessment is largely influenced by the spatial variability of material properties or defaults; however, there are still various challenges for their characterization and modeling. Structural Health Monitoring (SHM) could provide useful information in space and time for spatial variability characterization of material properties and mechanical solicitations; nevertheless, this challenge is arduous because of the large number of potential sensor positions of local disruptions/failures. This paper proposes a methodology to optimize the spatial distribution of embedded sensors used for spatial variability assessment of stationary random fields. The optimization criterion relies on the width of the confidence interval of statistics for the characteristics to identify. For sake of simplicity, the paper illustrates the method for one-dimensional problems. The proposed method is applied firstly to a numerical example were several hypothetical structural configurations that could be found in practice are studied. It is finally applied to two case studies (a reinforced concrete beam and a steel wharf) where water content and loss of steel thickness are respectively measured. The results show that the stationary property is useful to deduce the minimum quantity of sensors and their position for a given quality requirement. They also allow us to propose a criterion for defining if regular or non-regular spacing of sensors along the inspection zone is more appropriate depending on the component length and autocorrelation structure of the random field.

Image processing-based methods, capable of detecting and quantifying cracks, surface defects or recovering 3D shape information are increasingly being recognised as a valuable tool for inspecting underwater structures. It is of great practical importance for inspectors to know the effectiveness of such techniques when applied in conditions. This paper considers an underwater environment characterised by poor visibility chiefly governed by the lighting and turbidity levels, along with a range of geometry and damage conditions of calibrated specimens. The paper addresses the relationship between underwater visibility and the performance of image-based methods through the development and calibration of a first open-source underwater lighting and turbidity image repository (ULTIR). ULTIR contains a large collection of images of submerged specimens that have been photographed under controlled lighting and turbidity levels featuring various forms of geometry and damage. ULTIR aims to facilitate inspectors when rationalising the use of image processing methods as part of an underwater inspection campaign and to enable researchers to efficiently evaluate the performance of image-based methods under realistic operating conditions. Stakeholders in underwater infrastructure can benefit through this first large, standardised, well-annotated, and freely available database of images and associated metadata. Irish Research Council for Science, Engineering and Technology Science Foundation Ireland CAPACITES/IXEAD Society Marine Research Energy Ireland (MaREI)

Bio-colonisation affects the ageing of materials and the behaviour of offshore structures. Mooring systems and umbilicals belong to the family of slender bodies which are components sensitive to bio-colonisation because of a change of dynamic behaviour due to shape, roughness and mass modifications. However, this stochastic process in time and space is hard to predict. The purpose is then twofold: first, to provide a stochastic spatial model of the bio-colonisation on a mooring line, second, to show that in some defined environmental conditions, such as low wave height, low wind and current velocities, the monitoring of mooring lines tension can help to assess and reduce uncertainty on this model. Therefore, a comprehensive stochastic modelling based on mussels colonisation was carried out using on-site videotapes, experimental campaigns and expert knowledge. We studied the efficiency of a virtual sensing network using this model and a conditional entropy metric. It is first shown that the spatial model fits well with experimental data, and second that a denser medium accuracy sensor network is to be preferred to a single high accuracy fairlead sensor to reduce the uncertainty on the model parameters. It is then worth updating bio-colonisation on mooring lines during the life-time of a floating wind turbine.

Nowadays, the service life monitoring of mooring lines attracts more and more attention from the marine renewable energy (MRE) industry where developers desire to have a closer look on the safety margin. Besides, current standards are also being improved in order to ensure safe and cost-effective design for MRE devices. In the present study, we believe that monitoring and modeling should be implemented in parallel in order to better estimate the actual state and the reliability of the system during the expected service life. Besides, it is crucial to clarify the mechanic of the mooring line responses, to understand why and how they are modified during deployment, and how those can be accounted for in order to update the actual fatigue damage and predict the remaining allowable service life of system. A comprehensive modeling and service life monitoring methodology for mooring lines of floating wind turbines (FWTs) is discussed by the authors based on the reliability approach. Critical factors that influence structural behaviors such as material deterioration and marine environmental factors involved are identified and accounted for in this reliability-monitoring basis. Ultimately, based on the reliability level estimated, decisions regarding required maintenance or replacement of lines can be made.

Welded joints are used in various structures and infrastructures like bridges, ships and offshore structures, and are submitted to cyclic stresses. Their fatigue behaviour is an industrial key issue to deal with and still offers original research subjects. One of the available methods relies on the computing of the stress concentration factor. Even if some studies were previously driven to evaluate this factor onto some cases of welded structures, the shape of the weld joint is generally idealized through a deterministic parametric geometry. Previous experimental works however have shown that this shape plays a key role in the lifetime assessment. We propose in this paper a methodology for computing the stress concentration factor in presence of random geometries of welded joints. In view to make the results available by engineers, this method merges stochastic computation and semi-probabilistic analysis by computing partial safety factors with a dedicated method.

International audience; This article is within the context of decision models aimed for maintenance of structures and infrastructures in civil engineering. The contribution relies on the construction of a degradation model oriented toward risk analysis. The proposed model can be defined as a meta-model in the sense that it is based on observations while incorporating key features from the degradation process necessary for the maintenance decision. We propose to stimulate the construction of the degradation model based on the crack propagation of a submerged reinforced concrete structure subject to chloride-induced corrosion. Furthermore, a set of numerical illustrations is performed to demonstrate the advantages and applicability of the proposed approach in risk management and maintenance contexts.

Physics-based models are intensively studied in mechanical and civil engineering but their constant increase in complexity makes them harder to use in a maintenance context, especially when degradation model can/should be updated from new inspection data. On the other hand, Markovian cumulative damage approaches such as Gamma processes seem promising; however, they suffer from lack of acceptability by the civil engineering community due to poor physics considerations. In this article, we want to promote an approach for modeling the degradation of structures and infrastructures for maintenance purposes which can be seen as an intermediate approach between physical models and probabilistic models. A new statistical, data-driven state-dependent model is proposed. The construction of the degradation model will be discussed within an application to the cracking of concrete due to chloride-induced corrosion. Numerical experiments will later be conducted to identify preliminary properties of the model in terms of statistical inferences. An estimation algorithm is proposed to estimate the parameters of the model in cases where databases suffer from irregularities.

Due to both wave and wind fluctuations, the steel foundations of offshore wind turbines are highly submitted to fatigue. To date, current methods of fatigue design proposed in the regulations are not devoted to structural optimization and to the consideration of time-variant hazards. We propose hence an incremental two-scale model of damage in order to follow the time evolution of the damage. This temporal evolution allows the updating of model parameters using records from Structural Health Monitoring. In this paper, we focus on Bayesian updating of damage parameters and sensitivity analysis of damage assessment to material parameters.

This paper presents the Structural Health Monitoring (SHM) system developed for a port wharf of a freight terminal, in Saint-Nazaire, France. This concrete structure has been equipped with a multi-sensor system for the monitoring of concrete ageing. The measurement chain is designed to detect the penetration of chloride ions in order to quantify the risk of reinforcement bars corrosion. Modifications of the mechanical behavior of the structural elements of the wharf are also monitored. At first, the sensors embedded within the structure and the acquisition devices are described. The data from the monitoring performed during the first months of the structure service life are then presented. The concrete monitoring at early age providing data like temperature history, strain and resistivity is useful both for the wharf owner and the construction company since it indicates where concrete shrinkage is likely to cause cracking and gives an indicator of material hardening. These data were compared to the results of material tests carried out on concrete. The study shows that a measurement chain dedicated to the SHM could be a useful tool for validating the quality of the construction of a reinforced concrete structure before being used in the framework of long-term monitoring.

During the diagnosis of existing structures, errors can be generated by uncertainty of measurements. The latter are affected by several factors and should be evaluated on-site. In this paper, a large data of resistivity values (1800 measurements) measured on a bridgein a marine environment has been gathered utilizing a four electrodes probe under specific procedures. The uncertainty assessment based on measurements repeatability or local material anisotropy and variability were characterized from this database. Moreover, considering the uncertainty results together with the corrosion risk from RILEM TC154 recommendation, Probability of Wrong Assessment (PWA) is evaluated. Finally, the influence of uncertainty and PWA were discussed in order to help the owner of the bridge in taking decision on additional measurements or complementary investigations.

International audience; Chloride ingress and carbonation are the main factors leading to corrosion initiation of reinforced concrete (RC) structures. Lifetime assessment and maintenance optimization of these deteriorating structures has been based for several practical applications on analytical predictive models. The literature review shows that there are numerous analytical models of chloride ingress and carbonation. It was also found that comprehensive lifetime assessment requires that the selected models be able to propagate uncertainties in a correct manner. Thus, this paper proposes a methodology for probabilistic sensitivity analysis of analytical models for chloride ingress and carbonation. Given that di↵erent parameter notations were found for each model, a homogenization of notations and generic forms are first proposed. The sensitivity analysis is based on the following indicators: elasticity coecient, Pearson coecient, bias of the mean and standard deviation of the response of the model. The results highlighted which parameters are influential for each model by considering three concrete types. Overall results indicated a higher e↵ect of the non-linearity of the models on the sensitivity indicators. It was found that the level of influence of each parameter depends also on the concrete type and the duration of the exposure to chlorides or carbon dioxide.

A relevant knowledge of the state of the reinforced concrete is the basis for a successful management of the structures such as civil engineering bridges. To provide answers, IFSTTAR (coordinator), Cerema, Nantes University and Capacité SAS, SIXENSE-Concrete Company and the Département de la Charente-Maritime have contributed and funded a collaborative research project from 2015 to 2018. The French Project DéCoF-Ré meaning Decision Corrosion and Reliability of the ile de Ré Bridge has two main objectives. The first objective is to perform a reliable corrosion diagnosis of the piers of a bridge in a marine environment by means of non-destructive tests such as half-cell potential and resistivity. The second objective is to determine the durability of the concrete cover of the piers in terms of durability indicators. The aim of this paper is to present the Project DéCoF-Ré and to give some preliminary results., Savoir caractériser l'état d'une structure en béton armé est la base pour assurer la gestion des ouvrages du génie civil tels que les ponts par exemple. Avec pour ambition de répondre à cette problématique, l'Ifsttar (coordinateur, le Cerema, l4université de Nantes et sa filiale Capacités, l'entreprise Concrete et le Département de la Charente Maritime ont collaboré et financé un projet de recherche de 2015 à 2018. Le projet français DéCoF-Ré Décision Corrosion Fiabilité du diagnostic de corrosion sur le pont de l'ile de Ré a deux objectifs majeurs. Le premier est d'élaborer un diagnostic de corrosion fiabilisé sur les piles d'un ouvrage en milieu marin à l'aide de contrôles non destructifs tels que les potentiels et les résistivités. Le second objectif est de déterminer la durabilité du béton d'enrobage des piles en termes d'indicateurs de durabilité et de témoins de durée de vie. Cet article a pour vocation la présentation du projet DéCoF-Ré et de certains résultats préliminaires.

Recent breakthroughs in the computer vision community have led to the emergence of efficient deep learning techniques for end-to-end segmentation of natural scenes. Underwater imaging stands to gain from these advances, however, deep learning methods require large annotated datasets for model training and these are typically unavailable for underwater imaging applications. This paper proposes the use of photorealistic synthetic imagery for training deep models that can be applied to interpret real-world underwater imagery. To demonstrate this concept, we look at the specific problem of biofouling detection on marine structures. A contemporary deep encoder&ndash, decoder network, termed SegNet, is trained using 2500 annotated synthetic images of size 960 &times, 540 pixels. The images were rendered in a virtual underwater environment under a wide variety of conditions and feature biofouling of various size, shape, and colour. Each rendered image has a corresponding ground truth per-pixel label map. Once trained on the synthetic imagery, SegNet is applied to segment new real-world images. The initial segmentation is refined using an iterative support vector machine (SVM) based post-processing algorithm. The proposed approach achieves a mean Intersection over Union (IoU) of 87% and a mean accuracy of 94% when tested on 32 frames extracted from two distinct real-world subsea inspection videos. Inference takes several seconds for a typical image.

International audience; Cracking initiation and propagation are frequently recognized as main causes leading to failure of timber structures. Since the kinematics of both processes is largely influenced by environmental conditions, a comprehensive reliability assessment of notched structures should take into account such environmental factors. The main purpose of this paper is to propose a methodology for reliability assessment and updating of notched timber components based on mechanical (A-integral formulation) and reliability (simulation and Bayesian networks) methods, and experimental data. The A-integral formulation is used to estimate energy release rates in modes I and II by taking into account thermal effects; but its numerical implementation is time-consuming for uncertainty propagation. In order to deal with this problem, Bayesian networks were used for reliability assessment and updating. The experimental data used for updating purposes were obtained from measurements of deflection, temperature and relative humidity on a notched beam (Douglas Fir specie) exposed to outdoor environment and constant loading. The whole proposed methodology was illustrated with the reliability assessment and updating of the studied notched beam. The results indicated that the proposed approach is able to integrate measurements of temperature and deflection for reliability updating.

International audience; Relevant material and environmental parameters are required in modelling chloride ingress into concrete. They could be determined from experimental data (concrete cores taken during inspection) but in practice data availability is limited by time-consuming and expensive tests. Consequently, the main objective of this paper is to develop an approach based on Bayesian networks (BN) to improve the parameter identification when inspection data is limited. We aim at proposing appropriate inspection configurations that reduce inspection costs and identification errors for different exposure conditions and materials. It was found that it is possible to define an optimal number of inspection points in depth for allowed identification errors defined by decision makers. The optimal number of inspection points depends on both exposure and material properties. The random variables identified with the improved BN configurations are used to assess the probability of corrosion initiation. The results indicate that the improved BN configurations are useful to identify model parameters even from scarce inspection data.

Tomorrow’s Megastructures exploitation will require robust management policies, including Non- Destructive Techniques measurements and Structure Health Monitoring. These inspections strategies already allow acquisition of large amounts of data at reasonable cost. However, the bigger the structure, the costlier the data collecting. This raises the question of spatial optimization, closely related to the determination of spatial variability. Related studies have so far characterized it using covariograms on raw data without paying attention to their conditions of validity. As this lack of rigor may lead to severe misestimating, we propose a mathematically rigorous spatial variability assessment procedure and apply it to measurements coming from the Île de Ré bridge diagnosis, performed during the DéCoF-Ré (Décision par diagnostic de Corrosion en toute Fiabilité sur le pont de l’île de Ré) project. This unique data set of resistivity and corrosion potential measurements allow us to begin a spatial variability database for marine concrete structures.

Tensioned textile membrane structures are now common in the field of construction and they are the subject of a standardization process to harmonize the design rules among Europe. The stiffness of the structure relies on the pretension of the membrane, which can be provided mechanically or pneumatically. The stiffness of pneumatic structures depends on the internal pressure. They are able to support external loads which are much more important than their own weight. A first reliability study is conducted on a two parallel inflatable beams structure, which can be used as a footbridge. The serviceability limit states (SLS) and ultimate limit state (ULS) are defined. The reliability analysis is conducted with the FORM method, providing reliability indices as well as sensitivity factors.

40th IABSE Symposium : Tomorrow's Megastructures, NANTES, FRANCE, 19-/09/2018 - 21/09/2018; For RC structures exposed to marine environments, chloride-induced corrosion leads to deteriorations that can be associated to different exposure conditions: tidal, splash and atmospheric. The on-site assessment of the corrosion condition of the reinforcement in concrete relies on a corrosion diagnosis which consists in different techniques and among them non destructive techniques such as half-cell potential, resistivity and corrosion measurements. Although the two first techniques are commonly used, variability and uncertainties in NDT condition assessment remain factors that need to be better understood in order to increase the reliability of the diagnosis particularly in the case of marine structures (in comparison with atmospheric structures for which literature is more important). The objectives of the Project DéCoF-Ré (2014-2017) were to study both the concrete durability and the corrosion state of the piers of the Ile de Ré bridge which is located in the French Atlantic coast. Concerning the corrosion study, visual inspections, rebar localization, half-cell potential mapping, electrical concrete resistivity mapping and corrosion rate measurements were performed during three years in order to analyse the influence of the measurement procedures, the investigated zones (pier number, side, height) and the environments (tide level, climatic conditions). This paper focuses on the resistivity measurements conducted with a four electrodes arrangement probe (Wenner type) on the piers of the Ile de Ré bridge using a vessel. Although resistivity values cannot directly provide an information on the passive/active state of the rebar because the measurement qualifies the electrical resistivity of the concrete cover (and not the resistivity of the steel/concrete interface which is usually obtained by Linear Polarisation Resistance measurements), it is a useful non destructive technique to point out the areas where the corrosion might be strongest. The purpose of this paper is to outline the errors made while measuring the resistivity given the uncertainty assessment based on both (i) the repeatability tests and (ii) the material local anisotropy and measurement's variability. Moreover, a model based on the evaluation of Probability of Wrong Assessment is proposed to help the owner of the bridge in taking a decision.

Spatial variability is an essential key in modeling material properties, loading or the deterioration process in structural engineering degradation. Non-destructive Testing (NDT) technique is a crucial tool to characterize this spatial variability. In this paper, we propose an original approach that enables to characterize the spatial variability of structure through discrete and limited measurements in efficient and accurate way. An adaptive approach based on two indicators errors is performed to model the quantities of interest. Firstly, an error indicator is run to estimate an accurate parameter of fluctuations using Maximum Likelihood Estimate (MLE) from a set of measurements. Secondly, the accuracy of the estimated moments of the field from the current number of measures is revised by analyzing the behavior of the second indicator. The advantage of this approach is that the spatial correlation is not neglected between measurements and the range of the correlation is given in good way within a desirable threshold. The potential of the presented approach is demonstrated through numerical examples with synthetic and experimental data.

Reliability methods have proved in the past that they were rational aid-tools for the safety assessment of existing structures, within which some uncertainties occurred. Condition assessment is usu...

International audience; Non-destructive monitoring of crack growth within material becomes an essential tool to better understand cracking mechanisms of materials, and therefore to reduce failure risks. This paper presents results from laboratory static tests that were conducted to investigate the monitoring of crack tip growth within wood material. Double Cantilever Beam specimens with variable inertia were tested in opening mode configuration. Acoustic emission (AE) technique was used to monitor the acoustic activity within wood material. In a first step, the reliability of the crack tip monitoring is evaluated in terms of Probability of Detection (PoD). Results show that for all specimens the PoD is larger than 70% for a detection threshold of 1 mm. In a second step, a K-means++ algorithm was used to perform a cluster analysis of AE data, and to allow the AE events that were generated by the crack tip growth to be identified. Evolutions of both strain energy and AE energy are compared, and the experimental results show that the first AE events appear when the wood material reaches its limit to store strain energy, so at the vicinity of the end of its linear elastic behaviour.Highlights DCB wood specimens with variable inertia were tested in opening mode configuration. Unsupervised classification of AE events to identify three failure mechanisms. Probability of Detection to evaluate the reliability of the crack tip monitoring. Probability of Detection is larger than 70% for a detection threshold of 1 mm.

Probabilistic assessment of ageing bridges has become an important research area as it interests not only researchers but investors, municipalities and even governments. In this paper a simple bridge model is presented in a probabilistic context. A comparative study is carried out involving damage indicators and Bayesian updating. Bayesian updating is a powerful tool, which has been used in various research areas. However, using it for approximating the safety level of a bridge is challenging due to the various sources of uncertainties that may affect the performance of a measurement based damage indicator. The effects of different factors involved in the updating are examined in this paper and compared.

Daly's Bridge is a historic steel suspension footbridge in Ireland, known locally as the ‘Shaky Bridge’ for its noticeable movement under pedestrian loading. Although there is concern regarding the performance of the structure, testing or modelling has not been carried out to date and inadequate information exists in relation to carrying out such analyses. In this paper, Daly's Bridge is instrumented and tested for the first time and a model of the bridge is established and improved in the process. Apart from ambient vibration, excitation from traversing pedestrians and cyclists is considered. Video analysis of dynamic deflection, a wavelet-packet-based technique using acceleration responses and dynamic measurements from a cheap smartphone accelerometer application are used to identify and compare the natural frequency of the bridge. The work contributes to the evidence base of full-scale measurements from instrumenting and analysing responses of aging pedestrian bridges, highlighting the complexity, challenges, opportunities and limitations related to the varied levels of information available from disparate sources. The study also highlights the need to investigate to what extent cheap sensors can be successfully used as compared to their more expensive and sophisticated counterparts.

The understanding of the real in-service behavior of complex structures is the first step before modeling. That is still a challenge that requires structural monitoring in conjunction with structural modeling. Moreover, structural reassessment based on reliability updating during in-service conditions is needed for the identification and updating of boundary conditions of the structural model. Polynomial chaos allows providing an automatic treatment of data for monitored structures since it leads, on the one hand, to the decomposition of random variables and their efficient representation when considering maximum likelihood estimate and, on the other hand, to provide a format suitable for direct stochastic finite element analysis. In this paper, we illustrate a reliability updating of two monitored port structures to highlight similarities and specificities: we have chosen two on-piles quays instrumented by the GeM in the west coast of France. One of these quays involves an imperfect mechanical connection. One of the novelties of this paper is to identify the probabilistic properties of this defect. These structures are subjected to complex loads and among them we can underline wind actions. In fact, due to global warming, speed of wind is considerably increasing in some areas and will change the reliability level of some infrastructures. We present at first, from measurements of trajectories of stochastic fields of loads along the quay, an updating of basic variables representing random mechanical parameters of sensible components of the structure. Another novelty of the paper concerns similarities in the distribution of basic random variables which allow us to generalize the probabilistic modeling for other non-instrumented and similar quays, i.e., with the same global design and anchorage technology. Then, a mechanical finite element model of the quays is used as a transfer function of the random variable inputs to model the in-service behavior of the structure during severe conditions of wind action on a lift-crane. Non-intrusive stochastic finite element method is finally selected to evaluate the probability of failure for several scenarios.

Imaging-based damage detection techniques are increasingly being utilized alongside traditional visual inspection methods to provide owners/operators of infrastructure with an efficient source of quantitative information for ensuring their continued safe and economic operation. However, there exists scope for significant development of improved damage detection algorithms that can characterize features of interest in challenging scenes with credibility. This article presents a new regionally enhanced multiphase segmentation (REMPS) technique that is designed to detect a broad range of damage forms on the surface of civil infrastructure. The technique is successfully applied to a corroding infrastructure component in a harbour facility. REMPS integrates spatial and pixel relationships to identify, classify, and quantify the area of damaged regions to a high degree of accuracy. The image of interest is preprocessed through a contrast enhancement and color reduction scheme. Features in the image are then identified using a Sobel edge detector, followed by subsequent classification using a clustering-based filtering technique. Finally, support vector machines are used to classify pixels which are locally supplemented onto damaged regions to improve their size and shape characteristics. The performance of REMPS in different color spaces is investigated for best detection on the basis of receiver operating characteristics curves. The superiority of REMPS over existing segmentation approaches is demonstrated, in particular when considering high dynamic range imagery. It is shown that REMPS easily extends beyond the application presented and may be considered an effective and versatile standalone segmentation technique.

In this paper, we aim to achieve a probabilistic modelling of the compressive strength of concrete using three response surface models (RSM) and the artificial neural network (ANN) method. The input random variables for the three RSM and for the ANN are cement content, water content, measure of slump and air content, while the output for all the models is the compressive strength of concrete at 28 days. More than 800 cylindrical specimens 16 $$\times$$ 32 cm were tested. The experimental data are used to check the reliability of the suggested probabilistic models and their prediction capability. It is shown that the use of these new RSM is as simple as that of any of the basic formulas, yet they provide an improved tool for the prediction of concrete strength and for concrete proportioning. It is also shown that the concrete compressive strength can be readily and accurately estimated from the established ANN.

International audience; This paper addresses the topic of long-term characterisation and probabilistic modelling of chloride ingress into reinforced concrete (RC) structures. Since the corrosion initiation stage may cover various decades, normal tests which simulate chloride penetration into concrete in laboratory conditions as the same as natural conditions, will require significant experimental times. Hence, long-term lifetime assessment of RC structures under chloride attack remains still a challenge. In practice this problem is solved through the use of accelerated tests which speed up the chloride ingress rate and provide valuable mid-and long-term information on the chloride penetration process. Nevertheless, this information cannot be directly used for parameter statistical characterisation if the equivalent times required in natural conditions to reach the same chloride concentrations in the accelerated tests are unknown. Consequently, this study proposes a novel iterative approach based on Bayesian network updating to estimate chloride ingress model parameters from the data obtained under accelerated laboratory conditions. The Bayesian Network structure and iterative approach are first tested with numerical evidences. Thereafter, the complete proposed methodology is verified with results from real experimental measurements. The results indicate that combining data from normal and accelerated tests significantly reduces the statistical characterisation error of model parameters.

International audience; For some materials, such as RC, there is an initiation phase of deterioration where aggressive agents such as chlorides diffuse into the concrete cover. It is necessary to model this initiation time when attempting to determine the optimal maintenance strategy for the lifetime cost associated with a structure. It has been recommended in the literature that Markov chain models should be further improved within maintenance management systems to take initiation time into consideration. Many existing bridge management systems (BMSs), such as BRIDGIT and Pontis, use Markovian-based maintenance management to simulate the propagation phase of deterioration and repair of structures over time. It is therefore useful to continue this progress through incorporation of an initiation phase in relation to the development of Markovian-based maintenance management, which can lead to an improvement in BMSs that are already in place. The incorporation of the initiation phase into deterioration modeling can affect the frequency of inspections, repairs, and failures and hence the expected cost over the remaining lifetime of the structure. On this basis, this issue was addressed as part of this study. A maintenance management model has been further developed to take this two-step deterioration process (i.e., initiation and propagation) into account. This allows owners/managers of bridges to compare the efficiency of different maintenance strategies in terms of both the initiation phase and the propagation phase of deterioration to determine the optimal maintenance strategy for the structure or group of structures being considered. The capabilities of the developed methodology are demonstrated using a practical example.

To make visual data a part of quantitative assessment for infrastructure maintenance management, it is important to develop computer-aided methods that demonstrate efficient performance in the presence of variability in damage forms, lighting conditions, viewing angles, and image resolutions taking into account the luminous and chromatic complexities of visual data. This article presents a semi-automatic, enhanced texture segmentation approach to detect and classify surface damage on infrastructure elements and successfully applies them to a range of images of surface damage. The approach involves statistical analysis of spatially neighboring pixels in various color spaces by defining a feature vector that includes measures related to pixel intensity values over a specified color range and statistics derived from the Grey Level Co-occurrence Matrix calculated on a quantized grey-level scale. Parameter optimized non-linear Support Vector Machines are used to classify the feature vector. A Custom-Weighted Iterative model and a 4-Dimensional Input Space model are introduced. Receiver Operating Characteristics are employed to assess and enhance the detection efficiency under various damage conditions.

International audience; This article summarises research led during the second phase of the French GEROM (risk management of maritime and river harbour structures) project in order to quantify the risks associated with vulnerable structures. A reliability analysis is applied here to steel sheet-pile seawalls. For this purpose, a stochastic (spatial-temporal) model of steel corrosion is proposed, based on a statistical analysis of data collected from wharves located in several French ports and of various ages. The predictions obtained from the corrosion model are then integrated into a reliability analysis to carry out a time-function reliability analysis of corroded harbour structures. The results are then compared and analysed both in terms of reliability and sensitivity to basic random variables. This approach allows suggesting preliminary requirements about maintenance optimisation.

International audience; Multiple constraints imposed by economic, social and environmental considerations lead maintenance planning optimization to a major challenge for designers, owners and users of infrastructure. This study focuses on the management of reinforced concrete (RC) structures placed in chloride-contaminated atmospheres. Under these exposure conditions, chloride penetration generates corrosion of the reinforcing bars reducing the RC durability. Therefore, modeling the deterioration process as well as the maintenance actions carried out during the operational life becomes paramount for the formulation of a comprehensive maintenance strategy. This paper presents a new methodology for improving the performance of maintenance strategies for corroding RC structures. The proposed approach is based on preventive maintenance and combines: a numerical model of chloride penetration, Markov processes and decision theory. The uncertainty related to material properties, model and environmental actions as well as the effect of imperfect inspections are also integrated in the model. The aforementioned points are illustrated with a numerical example. In general, the overall results indicate that the proposed methodology can be useful to improve the costs of a given maintenance strategy by ensuring appropriate levels of serviceability and safety.

International audience; In the past years, an increasing number of structures have been equipped with permanent monitoring systems, able to record the structural response in terms of displacements and strains over very long periods of time, and theoretically, for the entire life of the structure (Structural Health Monitoring). Despite the high number of applications, to date, very few studies have been presented aimed at interpreting the data, in particular, when the spatial and behavioral complexity of the structure under investigation requires a non-model-based approach. This article shows that the interpretation of data from a long-term static monitoring can be very helpful for the comprehension of the structural behavior under complex interaction structure-environment. The discussion aims at underlining that a scheduled and unique procedure is very hard to define depending on the great variety of structures and applications and on the right identification of the parameters able to explain structural degradation evolutions or sudden changes. In these cases, a multi-step algorithm with different signal processing techniques applied in cascade seems to be the only reliable approach. The proposed procedure will be carried out using available data from the long-term monitoring of a quay wall in the Port of Genoa, in Italy, but it can be generalized to several structures under cyclic environmental loading (tides, temperature, etc.). The characterization of the structural behavior under temperature variations will be used here to define the representative features of the quay. Once defined, these parameters will provide a means for detecting and localizing the insurgence of damage or material degradation from the measurements.

The modeling of in-service behavior is of first importance when reassessing complex structures like harbor structures and when performing risk analysis. To this aim, the monitoring of structures allows assessment of the level of loading and to provide more realistic models for mechanical behavior or input values for their parameters. Moreover, for complex structures and due to building hazards, a stochastic modeling is needed to represent the large scatter of measured quantities. In this article, a step-by-step procedure for structural identification is presented. A decomposition of random variables on Polynomial Chaos is selected and it is shown to represent better the basic variables in comparison to preselected distribution functions, when considering maximum likelihood estimate. The decomposed variables are used for a stochastic analysis to be further updated with available monitoring data. The model can be used to follow the structure behavior during in-service or extreme conditions and to perform a reliability analysis. The proposed procedure will be carried out by using available data from the monitoring of a pile-supported wharf in the Port of Nantes, in France, but it can be generalized to similar monitored structures.

International audience; inspection by non-destructive testing (NDT) techniques of existing structures is not perfect and it has become a common practice to model their reliability in terms of probability of detection (PoD), probability of false alarms (PFA) and receiver operating characteristic (ROC) curves. These results are generally the main inputs needed by owners of structures in order to achieve inspection, maintenance and repair plans (IMR). The assessment of PoD and PFA is even deduced from intercalibration of NDT tools or from the modelling of the noise and the signal. In this last case when the noise and the signal depend on the location on the structure PoD and PFA are spatially dependent. This paper presents how to define PoD and PFA when damage and detection are stochastic fields or spatially dependent. Corrosion of coastal structures in harbours is considered for illustration and ROC curves are deduced. Identification of probability density functions on polynomial chaos is shown to be more suitable than predefined probability distribution functions (pdf) in view of fitting noise and signal plus noise distributions.

International audience; Inspection by non-destructive testing techniques of existing structures is not perfect and it has become a common practice to model their reliability in terms of receiver operating characteristic (ROC) curves. This paper suggests a method of building ROC curves in case of random fields of defects on a structure by using polynomial chaos decomposition. Knowledge of spatial distribution of defects allows a reliability analysis to be performed. When selecting stochastic fi nite element analysis to solve this problem, the format is the same as the one chosen for modelling inspections results. The paper shows how to link these quantities (ie. reliability and inspection results) in a risk analysis by using polynomial chaos decomposition as a common language.

This article presents an accurate survey of two on-piles quays with tie-rods during the building stage and the two first years of their opening. These first years after building are characterized by a very fair exploitation of both quays. The influence of the building conditions on the loads in the tie-rods of the two quays has been identified from measured data. On these two quays, identically designed, an original instrumentation strategy has been achieved: it aims to follow the behavior of the quays during the next 5 years including their building in view to set up models for the prediction of the loadings evolutions and to base the maintenance policy or exploitation during extreme events (storms) on a better understanding of the in-service behavior. This aim has been achieved by monitoring key components of the structure as the tie-rods and by surveying displacements of the quay, tide level, and water level in the embankment. An analysis of all the measurements obtained from 2002 to 2006 has led to identifying probability distributions which represent the randomness of the measured values. The study concludes on the use of these results for assessing structural health.

International audience; Characterization of randomness and spatial variability of material properties, loading or deterioration processes of structures, from inspection, is a very challenging task that concentrates the improvement of Non Destructive Testing (NDT) tools on more efficient and higher structural coverage. In case of characterization of random loading or material properties, this challenge is arduous because of the limited number of measures and the quasi-infinite potential positions of local failures. This paper presents a two stages procedure for the stochastic characterization of random fields from NDT measurements. According to the stationarity property, the optimal quantity of NDT measurements and their position can be assessed once a quality requirement is included. The proposed procedure allows: (i) to quantify the properties of the ergodic stationary field, and (ii) to assess the second order parameters of the studied random variables. The paper ends illustrating the methodology with an application that considers the inspection of a concrete beam with a capacitive technique. The results indicate that the proposed methodology is useful to identify the parameters of a random field by reducing the number of NDT measures under given quality requirements.

Managers of sewer systems are faced with their infrastructure system ageing. Even when they are conscious about the needs of maintenance to keep the system in a good condition, they lack efficient methods and tools that may help them in taking appropriate decisions. One can say that no really satisfactory and efficient tool exists, enabling the optimization of Inspection, Maintenance, or Rehabilitation (IMR) strategies on such systems. Sewer managers and researchers have been involved for many years in the French National Research Project for Renewal of Non Man Entry Sewer System (RERAU—Rehabilitation des Reseaux d'Assainissement Urbains, in French) to improve their knowledge of these systems and the management policies. During the RERAU project, a specific action has been dedicated to the modeling of asset ageing and maintenance. A special attention has been dedicated to the description of defects and dysfunctions, to the evaluation of performances and its modeling, accounting for its various dimensions (from the point of view of the manager, of the user, of the environment…). After having defined an Index of Technical Performance (ITp), we will introduce the Index of Technical and Economic Performance (ITEp) that is a combined measure of performance (including social costs) and technical costs. This index provides an objective standard tool for managers to compare different alternatives. It is used in the article to compare some simple IMR strategies. It sets the basis of a new method for no-man entry sewer system management, enabling us to analyze the profitableness of investment in terms of both technical and economic performance.

The reassessment of existing structures leads to analyse the impact of local defects on the structural behaviour. This paper proposes a cracked beam finite element with a view to introducing the effect of large through-cracks in the structural analysis for framed structures like jacket offshore platforms. The model parameters are identified for several joint typologies using 3D finite element results. Copyright © 2007 John Wiley & Sons, Ltd.