Emilio Bastidas-Arteaga

Effects of climate variations and global warming on the durability of RC structures subjected to carbonation

Carbonation affects the performance, serviceability and safety of reinforced concrete (RC) structures when they are placed in environments with important CO 2 concentrations. Since the kinetics of carbonation depends on parameters that could be affected by climate change (temperature, atmospheric CO 2 pressure and relative humidity (RH)), this study aims at quantifying the effect of climate change on the durability of RC structures subjected to carbonation risks. This work couples a carbonation finite element model with a comprehensive reliability approach to consider the uncertainties inherent to the deterioration process. The proposed methodology is applied to the probabilistic assessment of carbonation effects for several cities in France under various climate change scenarios. It was found that climate change and local RH have a significant impact on corrosion initiation risks.

Economic assessment of climate adaptation strategies for existing reinforced concrete structures subjected to chloride-induced corrosion

Reinforced concrete (RC) structures placed in chloride-contaminated environments are subjected to deterioration processes that affect their performance, serviceability and safety. Chloride ingress leads to corrosion initiation and its interaction with service loading could reduce its operational life. Chloride ingress and corrosion propagation are highly influenced by weather conditions in the surrounding environment including climate change. Therefore, both structural design and maintenance should be adapted to these new environmental conditions. This study focuses on the assessment of the costs and benefits of climate adaptation strategies for existing RC structures subjected to chloride ingress and climate change. We studied RC structures built at different periods under different construction standards in France. The cost-effectiveness of adaptation measures was measured in terms of the benefit-to-cost ratio (BCR) and the probability that BCR exceeds unity – i.e. Pr(BCR>1). The results of the paper could provide practical advice to policy-makers to improve the management of existing RC structures under a changing climate by discussing the influence of the following factors on the mean BCR and Pr(BCR>1): specific exposure conditions, climate change scenarios, risk reduction due to the implementation of adaptation strategies, type of structural component, years of construction and adaptation, discount rates and damage costs.

Improved Bayesian network configurations for probabilistic identification of degradation mechanisms: application to chloride ingress

Abstract Probabilistic modelling of deterioration processes is an important task to plan and quantify maintenance operations of structures. Relevant material and environmental model parameters could be determined from inspection data; but in practice, the number of measures required for uncertainty quantification is conditioned by time-consuming and expensive tests. The main objective of this study was to propose a method based on Bayesian networks for improving the identification of uncertainties related to material and environmental parameters of deterioration models when there is limited available information. The outputs of the study are inspection configurations (in space and time) that could provide an optimal balance between accuracy and cost. The proposed methodology was applied to the identification of random variables for a chloride ingress model. It was found that there is an optimal discretisation for identifying each model parameter and that the combination of these configurations minimises identification errors. An illustration to the assessment of the probability of corrosion initiation showed that the approach is useful even if inspection data are limited.

Chlordetect: Commercial Calcium Aluminate Based Conductimetric Sensor for Chloride Presence Detection

Chloride presence affects different environments (soil, water, concrete) decreasing their qualities. In order to assess chloride concentration this paper proposes a novel sensor for detecting and measuring it. This sensor is based on electric changes of commercial monocalcium aluminate (CA) when it interacts with chloride aqueous solutions. CA is used as a dielectric material between two coplanar capacitors. The geometry proposed for this sensor allows to assess the chloride content profile, or to make four times the same measurement. Besides, the experimental design gives us the possibility of study not just the chloride effect, but also the time and some geometric effects due to the sensor design. As a result, this sensor shows a limit of detection, sensitivity, and response time: 0.01 wt % Cl− and 0.06 wt % Cl−, and 2 min, respectively, comparable with other non invasive techniques as optical fibre sensors.

Probabilistic Improvement of Crack Propagation Monitoring by Using Acoustic Emission

In this work, the acoustic emission is used as a measurement technique to detect and locate the progress of the crack tip in a wooden specimen subjected to thermo-hygro-mechanical stresses. Under these stresses, the material response results in the release of energy in the form of transient elastic waves that are recorded by acoustic emission sensors. The post-processing of these acoustic signals is used to detect the position of the crack. There are many parameters that can affect the accuracy of acoustic emission such as noise signals, geometry, wood specie, etc. Consequently, this study combines repetitive tests and probabilistic approaches to characterize uncertainties and improve the acoustic emission protocol. In the experimental program, breaking of graphite mines at various known positions simulated acoustic sources. The differences between the real and detected positions are used to calibrate the tests and to improve the configuration of the sensors.

Assessing the Capability of Analytical Carbonation Models to Propagate Uncertainties and Spatial Variability of Reinforced Concrete Structures

Most of the approaches for diagnosis or prognosis of deteriorated reinforced concrete (RC) structures are based on two stages: acquiring data (concrete properties, quantitative degradation information), and then predicting the evolution of degradation by using appropriate models. Spatial variability of both properties and degradation processes cannot be neglected in the lifecycle assessment and implies that (i) data should be acquired for a representative part of the concrete surface and (ii) models should be capable of dealing with this variability. However, the assessment and modeling of spatial variability is not a straightforward task particularly when uncertainties affect the measurements or when the number of measurements is limited. The present paper aims at studying the capability of analytical carbonation models to deal with the spatial variability of model inputs in terms of spatial correlation of model outputs. Analytical models are considered herein because they provide practical and usual tools in engineering. This paper focuses on the case of a RC wall exposed to atmospheric carbonation where concrete properties and carbonation depths were measured by destructive techniques at several points over a linear portion of a wall within the framework of the French ANR EVADEOS project. Uncertainties due to experimental devices and procedures are estimated and propagated throughout random field models to account for spatial variability of spatial observations. Correspondence indexes are proposed to rank carbonation models with respect to their ability of reflecting the observed correlation profiles of carbonation depth. It was found that for the available database the proposed correspondence index that incorporates uncertainties was useful to assess the capabilities of models to deal with the spatial variability.

Optimal design of deteriorating timber components under climate variations

The mechanical and physical properties of timber structures could be affected by a combination of loading, moisture content, temperature, biological activity, etc. This paper focuses on the optimal design of new timber structures subjected to fungal decay. Among the optimization methods available in the literature, this study considers a Time-Dependent Reliability Based-Design Optimization (TD-RBDO) approach. The TD-RBDO aims at ensuring a target reliability level during the operational life by considering deterioration and the uncertainties related inherent to materials properties, models and climate. This approach is applied to design optimization of a timber truss subjected to an aggressive (very humid) French climate. The performance of the optimized solution is compared, in terms of safety, with solutions estimated from Deterministic Design Optimization (DDO) and Reliability-Based Design Optimization (RBDO) approaches. The overall results indicate that the optimized solution of TD-RBDO reduces ensures a target reliability level during the whole structural lifetime.

Optimization of inspection and monitoring of structures in case of spatial fields of deterioration/properties

The localization of weak properties or bad behavior of a structure is still a very challenging task that concentrates the improvement of Non Destructive Testing (NDT) toolson more efficiency and higher structural coverage. In case 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. The paper shows that the stationary property is useful to find the minimum quantity of NDT measurements and their position for a given quality assessment. Atwo stagesprocedure allows us (i) to quantifythe properties of the ergodic, stationary field (ii) to assess the distribution of the characteristics. The paper focuses on optimization of sensor geo-positioning to reach (i): the criterion relies on the width of the confidence interval. The concept of critical upperbound spatial correlation is introduced.

Bayesian Network Framework for Statistical Characterisation of Model Parameters from Accelerated Tests: Application to Chloride Ingress Into Concrete

Abstract 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.

Maintenance optimization for power distribution systems subjected to hurricane hazard, timber decay and climate change

Electric power systems are vulnerable to extensive damage due to hurricanes with most of the damage concentrated on overhead distribution systems. There is evidence that climate change will affect future hurricane patterns. Additionally, wood poles, which are most commonly used in distribution systems, are susceptible to decay. The scarcity of resources and increasing demand for higher reliability warrant the use of optimization techniques for wood pole maintenance planning. This paper presents a framework for optimal maintenance of wood poles subjected to non-stationary hurricane hazard and decay. Maintenance cost, service life, and system performance are considered separately and simultaneously in the optimization. Periodic chemical treatment and repair of decayed poles using fiber-reinforced polymer are considered. The distribution system of a virtual city assumed to be in Florida is used to demonstrate the framework. The results of the single-objective optimization indicate that the objective that maximizes service life resulted in higher optimal maintenance time. However, delaying maintenance will lead to a larger probability of pole failure, higher corrective maintenance cost, and lower system performance. The result of the multi-objective optimization is closer to the result of the cost-based optimization because the cost function is more sensitive to the variation of maintenance time.