Evaluation of seismic demand for substandard reinforced concrete structures

Background: Reinforced Concrete (RC) buildings with no seismic design exhibit degrading behaviour under severe seismic loading due to non-ductile brittle failure modes. The seismic performance of such substandard structures can be predicted using existing capacity demand diagram methods through the idealization of the non-linear capacity curve of the degrading system, and its comparison with a reduced earthquake demand spectrum. Objective: Modern non-linear static methods for derivation of capacity curves incorporate idealization assumptions that are too simplistic and do not apply for sub-standard buildings. The conventional idealisation procedures cannot maintain the true strength degradation behaviour of such structures in the post-peak part, and thus may lead to significant errors in seismic performance prediction especially in the cases of brittle failure modes dominating the response. Method: In order to increase the accuracy of the prediction, an alternative idealisation procedure using equivalent elastic perfectly plastic systems is proposed herein that can be used in conjunction with any capacity demand diagram method. Results: Moreover, the performance of this improved equivalent linearization procedure in predicting the response of an RC frame is assessed herein. Conclusion: This improved idealization procedure has been proven to reduce the error in the seismic performance prediction as compared to seismic shaking table test results [1] and will be further investigated probabilistically herein

Optimization of life-cycle preventative maintenance strategies using genetic algorithm and Bayesian Updating

The authors have developed an optimization genetic algorithm (GA) methodology that enables the optimization of preventative maintenance (PM) strategies applied to reinforced concrete (RC) bridges. The PM strategies are used to delay/prevent the reinforcement corrosion of bridge beams due to contamination from chloride ions and maintain the reliability profile within acceptable limits and minimum whole life costing. A key element in predicting optimum PM strategies using the GA methodology is the accuracy of estimating the degree of deterioration of an element. The use of Bayesian Updating improves the reliability of this estimation by enabling the updating of the probability of failure based on data from inspection and the adjustment if necessary of the timing of subsequent PM interventions. The case studies presented demonstrate the application and the effectiveness of the proposed updated GA methodology and also examine the influence of applying updating at different time frames in reaching the optimum PM maintenance strategy

Optimum preventative maintenance strategies using genetic algorithms and Bayesian updating

Preventative maintenance (PM) includes proactive maintenance actions that aim to prevent or delay a deterioration process that may lead to failure. This type of maintenance can be justified on economic grounds because it can extend the life of bridges and avoid the need for unplanned essential maintenance. Due to the high importance of the effective integration of PM measures in the maintenance strategies of bridges, the authors have developed an optimisation methodology based on genetic algorithm (GA) principles, which links the probabilistic effectiveness of various PM measures with their costs in order to develop optimum PM strategies. To further improve the reliability of estimating the degree of deterioration of an element, which is a key element in predicting optimum PM strategies using the GA methodology, Bayesian updating is utilised. The use of Bayesian updating enables the updating of the probability of failure based on data from site inspection or laboratory experiments and the adjustment, if necessary, of the timing of subsequent PM interventions. For the case study presented in this paper, the probability of failure is expressed as the probability of corrosion initiation of a reinforced concrete element due to de-icing salt

Effect of environmental deterioration on buildings: A condition assessment case study

Proceedings of SPIE - The International Society for Optical Engineering, Volume 9229, 2014, Article number 92290YThe deterioration of structures due to corrosion is probably the most significant factor for their damaging condition and the need for maintenance. Corrosion mechanisms depend on the environmental conditions and the geographic characteristics of the area. In this paper a condition assessment methodology is presented through an application on a deteriorated building in Cyprus. The methodology's starting point is the collection of information through Google Earth for classification of buildings in regions based on their environmental and geographic characteristics. Through this screening process, buildings in each defined region are selected for evaluation. The following steps of the methodology include testing on selected structural members for the estimation of the compression strength and the depth of carbonation. The results of the case study, are used from the responsible engineer to evaluate the current condition of the building regarding its structural integrity and the effect of corrosion. The testing data showed that the current building strength is lower than the code's requirements and that carbonation induced corrosion must be addressed to prevent further damage. © 2014 SPIE

Monitored-based methodology to predict the initiation of corrosion in RC structures

The corrosion of steel reinforcement in RC structures is the main deterioration factor of these structures and the employment of SHM techniques can provide indications on the corrosion activity at its early stages. In this study, a methodology is proposed to predict the initiation of corrosion on RC structures using information from SHM data in order to alleviate the impact of uncertainties currently employed in theoretical corrosion models. The monitored data are obtained from the half-cell potential and the concrete resistivity methods. In the methodology the data processing is combined with condition rating and risk assessment principles in order to assess the current structural condition and predict when corrosion initiation is due. The results show that the proposed methodology can provide information on the timeframe of the corrosion state of RC members and most importantly before its effect are visible and the repairing work is mandatory and costly

Integration of Probabilistic Effectiveness with a Two-stage Genetic Algorithm Methodology to Develop Optimum Maintenance Strategies for Bridges

reventative Maintenance (PM) measures can be used to postpone/delay the initiation of corrosion from chloride attack in reinforced concrete bridges. However there are a lot of uncertainties that influence their degree of effectiveness. Also the time-application of these measures can raise a conflict between safety requirements and budgets. This paper presents a stochastic approach for estimating the effectiveness of different PM measures. Additionally a two-stage optimisation methodology using the principles of Genetic Algorithms (GA) is developed to address the problem of the timeapplication by linking the effectiveness with the cost to produce optimum PM strategies. Futhermore, the role of the presented time-dependent probabilistic approach in the proposed two-stage GA methodology for obtaining optimum PM strategies is demonstrated

Applications of thermal imaging camera for assessing structural integrity

Proceedings of SPIE - The International Society for Optical Engineering, Volume 10773, 2018, Article number 107731O© 2018 SPIE. The ongoing degradation of structures is associated with expensive maintenance and the resulting decline in safety, force the engineer to search for structural health monitoring tools that will be fast, effective, cover large areas and cost as minimum as possible. In this context the thermal imaging cameras are an ideal monitoring tool; with the radical development of higher resolution thermal imaging, the decreasing cost of the camera and its portable size makes this technology promising to accomplish the requirements of modern structural monitoring. Thermal imaging camera uses algorithms to interpret visual displays of the amount of infrared energy emitted, transmitted and reflected by an object and form images that are invisible to the human eye. Therefore, the thermal imaging technology can be used as a tool to help the engineer gain better insight and viable information and thus enabling the structure to retain/sustain its function, form and strength within acceptable limits under operational loading. This paper presents applications of this technology for assessing the integrity of structures along with possible trends and gains on different areas of structural integrity, such as the detection of corrosion in steel rebars embedded in RC structures and the chloride contents on concrete surface

Sensitivity Analysis of Key Parameters in Decision Making of Two-Stage Evolutionary Optimization Maintenance Strategies

Preventative maintenance (PM) measures for bridges are proactive maintenance actions which aim to prevent or delay a deterioration process that may lead to failure. This type of maintenance can be justified on economic grounds since it can extend the life of the bridge and avoid the need for unplanned essential/corrective maintenance. Due to the high importance of the effective integration of PM measures in the maintenance strategies of bridges, the authors have developed a two-stage evolutionary optimization methodology based on genetic algorithm (GA) principles which links the probabilistic effectiveness of various PM measures with their costs in order to develop optimum PM strategies. In this paper, the sensitivity of the methodology to various key input parameters of the optimization methodology is examined in order to quantify their effects and identify possible trends in the optimum PM intervention profiles. The results of the sensitivity studies highlight the combined use of both proactive and reactive PM measures in deriving optimum strategy solutions. The precise mix and sequence of PM measures is clearly a function of the relative effectiveness and cost of the different available PM options as well as the various key parameters such as discount rate, target probability of failure, initial probability of failure and service life period examined. While the results highlight the need for more reliable data they also demonstrate the robustness and usefulness of the methodology; in the case where data is limited it can be used as a comparative tool to improve understanding of the effects of various strategies and enhance the decision making process