Performance Based Earthquake Engineering of Concrete Dams

The main objective of this thesis is to develop a framework for performance based earthquake engineering (PBEE) of concrete dams. To pursue this goal, this study first develops an extended and quantitative version of potential failure mode analysis (PFMA) for concrete dams. Different failure modes are investigated for all types of concrete dams. A Matlab-based code is developed for probabilistic performance assessment of concrete dams (PPACD). This code is used for assessment of concrete dams within the context of PBEE. A probabilistic seismic demand model (PSDM) is proposed for concrete dams based on cloud analysis methodology. The outcome of PSDM is selection of optima intensity measure (IM) parameters for gravity dams. Then, the sensitivity and uncertainty of dam-foundation system is quantified under the mixed-mode fracture of zero-thickness interface joint element. Capacity and fragility curves are derived for most sensitive random variables. This research also examined the performance of the dam under incremental dynamic analysis (IDA). First, the anatomy of a single-record IDA is studied and contrasted by framed structures. Then, the collapse fragility curves are derived for single and multiple-component ground motions. The impact of epistemic uncertainty is investigated in addition to the aleatoric one. Finally, a multi-scale damage index (DI) is proposed for gravity dams which is a function of crest displacement, crack ratio, and dissipated energy. Using this hybrid DI, a computationally simple but effective methodology is proposed for progressive failure analysis of dams. In all cases, first the methodology is discussed and then, a numerical example illustrates the details

Developing a Library of Shear Walls Database and the Neural Network Based Predictive Meta-Model

There is a large amount of useful information from past experimental tests, which are usually ignored in test-setup for the new ones. Variation of assumptions, materials, test procedures, and test objectives make it difficult to choose the right model for validation of the numerical models. Results from different experiments are sometimes in conflict with each other, or have minimum correlation. Furthermore, not all these information are easily accessible for researchers and engineers. Therefore, this paper presents the results of a comprehensive study on different experimental models for steel plate and reinforced concrete shear walls. A unique library of up to 13 parameters (mechanical properties and geometric characteristics) affecting the strength, stiffness and drift ratio of the shear walls are gathered including their sensitivity analysis. Next, a predictive meta-model is developed based on artificial neural network. It is capable of forecasting the responses for any desired shear wall with good accuracy. The proposed network can be used to as an alternative to the nonlinear numerical simulations or expensive experimental test.</p

Seismic risk prioritization of a large portfolio of dams: Revisited

The development of potential failure mode analysis and risk analysis has greatly improved the state-of-practice for the safety of dams. Risk analysis are well developed in many industries (such as building design, medicine, and insurance) and has greatly advanced in the dams industry over the last 40 years. Engineers and scientists are now deeply investigating and thinking about failure mechanisms associated with operating dams and the probabilities of dam failures. As such, the condition of dams and the risks associated with their operation are now being portrayed better than ever before to dam safety officials and decision-makers. Accurate and adequate risk analyses for a portfolio of dams is extremely important in today’s environment to manage limited budgets and potentially save (or redirect) expensive rehabilitations to identified and critical needs. The goal is to reduce risks of a portfolio of dams in an efficient and cost-effective manner. This article provides a review on risk-based dam terminology and bridging the semi-quantitative and numerical simulation. Moreover, a review of the current state-of-practice for prioritizing a large portfolio of dams subjected to seismic loadings and potential risks is provided. As a potential application, the seismic risk of the 18 dams (which have been experienced relatively large earthquakes) all over the world is evaluated. The semi-quantitative approach is contrasted with finite element model for one of the selected dams

Vibration Anatomy and Damage Detection in Power Transmission Towers with Limited Sensors

This study presents a technique to identify the vibration characteristics in power transmission towers and to detect the potential structural damages. This method is based on the curvature of the mode shapes coupled with a continuous wavelet transform. The elaborated numerical method is based on signal processing of the output that resulted from ambient vibration. This technique benefits from a limited number of sensors, which makes it a cost-effective approach compared to others. The optimal spatial location for these sensors is obtained by the minimization of the non-diagonal entries in the modal assurance criterion (MAC) matrix. The Hilbert&ndash;Huang transform was also used to identify the dynamic anatomy of the structure. In order to simulate the realistic condition of the measured structural response in the field condition, a 10% noise is added to the response of the numerical model. Four damage scenarios were considered, and the potential damages were identified using wavelet transform on the difference of mode shapes curvature in the intact and damaged towers. Results show a promising accuracy considering the small number of applied sensors. This study proposes a low-cost and feasible technique for structural health monitoring.</div

A cost-effective neural network-based damage detection procedure for cylindrical equipment

This article presents a vibration-based technique for damage detection in the cylindrical equipment. First, a damage index based on the residual frequency responses is defined. This technique uses the principal component analysis for data reduction by eliminating the components that have the minimum contribution to the damage index. Then, the principal components are fed into neural networks to identify the changes in the damage pattern. Furthermore, the efficiency of this technique in the field condition is investigated by adding different noise levels to the output data. This study aims at proposing a cost-effective damage detection model using only one sensor. Therefore, the optimal location of the sensor is also discussed. A case study of capacitive voltage transformer is used for validation of finite element models. The neural networks are trained using numerical data and tested with experimental one. Several parametric analyses are performed to investigate the sensitivity of the model. </div

Living in a Multi-Risk Chaotic Condition: Pandemic, Natural Hazards and Complex Emergencies

Humans are living in an uncertain world, with daily risks confronting them from various low to high hazard events, and the COVID-19 pandemic has created its own set of unique risks. Not only has it caused a significant number of fatalities, but in combination with other hazard sources, it may pose a considerably higher multi-risk. In this paper, three hazardous events are studied through the lens of a concurring pandemic. Several low-probability high-risk scenarios are developed by the combination of a pandemic situation with a natural hazard (e.g., earthquakes or floods) or a complex emergency situation (e.g., mass protests or military movements). The hybrid impacts of these multi-hazard situations are then qualitatively studied on the healthcare systems, and their functionality loss. The paper also discusses the impact of pandemic&rsquo;s (long-term) temporal effects on the type and recovery duration from these adverse events. Finally, the concept of escape from a hazard, evacuation, sheltering and their potential conflict during a pandemic and a natural hazard is briefly reviewed. The findings show the cascading effects of these multi-hazard scenarios, which are unseen nearly in all risk legislation. This paper is an attempt to urge funding agencies to provide additional grants for multi-hazard risk research

Soft Computing and Machine Learning in Dam Engineering

Dams have played a vital role in human civilization for thousands of years, providing vital resources such as water and electricity, and performing important functions such as flood control [...

A series of forecasting models for seismic evaluation of dams based on ground motion meta-features

Uncertainty quantification (UQ) due to seismic ground motions variability is an important task in risk-informed condition assessment of infrastructures. Since performing multiple dynamic analyses is computationally expensive, it is valuable to develop a series of forecasting models based on the unique ground motion characteristics. This paper discusses the application of six different machine learning techniques on forecasting the structural behavior of gravity dams. Various time-, frequency-, and intensity-dependent characteristics are extracted from ground motion signals and used in machine learning. A large set of about 2000 real ground motions are used, each includes about 35 meta-features. The major outcome of this study is to show the applicability of meta-modeling-based UQ in seismic safety evaluation of dams. As an intermediary result, the advantages of different machine learning algorithms, as well as meta-feature selection possibility is discussed for the current dataset. This paper proposes a feasibility study to reduce the computational costs in UQ of large-scale infra-structural systems.Web of Science203art. no. 10965