Composite pressure tanks have received increased attention across a number of civilian applications due to their lightweight and high strength. Traditionally, the design of composite vessels is based on deterministic analysis. However, the design of these structures is challenging and involves several kinds of uncertainties. In fact, different computational investigations have been carried out but no studies provide a resolution for small failure probability evaluation of composite pressure tanks. The aim of this study is to establish a computational framework to investigate small failure probability levels of composite tanks using the Subset Simulation method (SS). The model was developed in two steps, first, the development of limit state functions for hoop and helical layers using netting analysis, and afterwards, a probabilistic computation with six random variables. To quantify the effect of the randomness of different parameters on the structural reliability of composite tanks, a sensitivity analysis was performed using different values of coefficients of variation (COV). It was observed from the results that SS has the ability and the accuracy required to evaluate small failure probabilities which are commonly encountered in composite tank applications. In addition, the hoop strength, the internal pressure, and the thickness of the composite are the major design variables that have a great impact on the structural reliability of the axially symmetric composite tank whereas the fiber winding angle has little effect. Moreover, high COV values drastically reduce the safety zone, which could eventually lead to the burst failure of the composite pressure tank. Furthermore, this study implements a reliability-based design from the perspective of hoop and helical composite layer thicknesses, thus providing a rational assessment of the risk of structural failure.

The paper presents a study on the fatigue strength of Steel Fibre Reinforced Concrete (SFRC) containing fibres of mixed aspect ratio. To obtain the fatigue lives of SFRC at various stress levels, beam specimens of size 500 mm × 100 mm × 100 mm were tested under four-point flexural fatigue loading. Static flexural tests were also carried out to determine the static flexural strength of SFRC prior to fatigue testing. The specimens incorporated 1.0% volume fraction of corrugated mixed steel fibres of size 0.6 × 2.0 × 25 mm and 0.6 × 2.0 × 50 mm in different proportions. All the fatigue tests were conducted on MTS servo-controlled test system where as all the static tests were conducted on a similar INSTRON machine. Fatigue life data obtained has been analysed in an attempt to determine the relationship among stress level S, number of cycles to failure N and probability of failure Pf for SFRC. It was found that this relationship can be represented reasonably well graphically by a family of S-N-Pf curves. The experimental coefficients of the fatigue equation have been obtained from the fatigue test data to represent the S-N-Pf relationships analytically.        

In this paper, we focus on a continuously deteriorating critical equipment which its failure cannot be measured by cost criterion. For these types of systems like military systems, nuclear systems, etc it is extremely important to avoid failure during the actual operation of the system. In this paper we propose an approach which constrains failure probability to a pre-specified value. This value guarantees a chance of failure less than or equal to the pre-specified value during real operation of the system. The inspection periods and maintenance policy are found in two phases. Failure probability is limited to a pre-specified value In the first phase, and in the second phase optimum maintenance thresholds and inspection periods are obtained in such a way that minimize long-run expected costs. Due to the complexity of the model, Monte Carlo simulation is used to obtain optimum results.

Wastewater systems form an important urban infrastructure that are used for the collection and treatment of wastewater for return into the environment or water reuse. The sewers network in this system forms its most important component, any failure in which may lead to adverse consequences and disruption in urban life. Proper functioning of a sewers network depends on its operation and maintenance (O & M) program that requires timely inspections to identify the high risk sewers with any likelihood of failure in order to gurantee the sustained and sound performance of the whole network. In this study, the Bayesian network is used to develop a model for calculating failure event probability in wastewater collection systems. Given the capabilities of the Bayesian network and the characteristic features of the sewers network, the proposed model is capable of predicting likely failure events. The procedure used for model implementation consists of the following four main steps: preparation of model inputs, training the Bayesian network, validation of the trained network, and receiving output results. To illustrate the application of this method, part of Tehran wastewater collection system is selected as a case study and failure probabilities are calculated. Based on the model results, Tehran sewers can be divided into five categories priotized according to inspection and maintenance requirements. The results indicate that the probability of failure for most of the existing sewers is very low and low (37%) or moderate (60%) due to the newly annexed sewers in the collection system.

Recent weather-related disasters experienced worldwide with considerable damages to the interconnected power infrastructure have highlighted the importance and urgency of enhancing the resiliency of the distribution grid. A Resilient distribution grid can withstand and recover from such rare events. Resiliency against extreme events is conceptualized in three distinct stages: prior, during, and after the event. Rapid recovery is a feature of after the event stage. In this paper, restoration strategies to restore maximum loads as quickly as possible are investigated. The proposed approach attempts to restore the critical loads by using tie-switches to reconfigure the network. In the case of isolated areas without the possibility of using upstream utility grid, sectionalizing the grid into several microgrids (MGs) is proposed to improve the system resiliency. The number of isolated MGs is an issue that is required to be correctly determined. So, a new approach is proposed to compromise between amount and reliability of supplied load to find the optimum number of MGs. The proposed method is simulated on the unbalanced IEEE-123 and 37-bus distribution grid with random locations for DERs.

Due to the probabilistic nature and uncertainties of structural parameters, reliability-based optimization will enable engineers to account for the safety of the structures and allow for its decision making applicability. Thus, reliability-based design will substitute deterministic rules of codes of practice. Space structures are of those types that have an exceedingly high range of applicability aspects in civil engineering. Therefore optimization of such structures with great and considerable number of members will be economically wise. For this purpose, the optimization process could be carried out using various mathematical models. One such model is to minimize weight while considering elements failure probability as constraints. Another form is to minimize weight and then regarding the whole structure system reliability as constraint. The third type could be to minimize failure probability as well as its weight, while taking into account the structural system reliability as the constraint. In this research each of the above forms were studied and the results were compared. Also, apart from reliability considerations for the members, the reliability of nodes was also taken into account. Node failure means that node displacement in at least one direction exceeds that of the allowable value. As well the effect of various stochastic parameters such as load, yield stress, modulus of elasticity and cross section were studied. The stochastic parameters discussed in this study are statistically independent and possess standardized normal distribution. To avoid local convergence during the process of optimization, Genetic Algorithms is used as means of optimization. This study show that with increasing the members or system admissible failure probability, optimum weight of structure increases, but with increasing the coefficient of variation of load or yield stress, optimum weight increases. 

Bridges are a critical part of the urban and suburban transportation network, so they are supposed to be designed to sustain earthquake induced damages to be utilized after earthquake. Various parameters can affect the behavior and probability of failure of a bridge and the present work aims to evaluate the effects of changes in structural parameters on the probability of failure in isolated concrete bridges OpenSees software is used for simulating and analyzing 16 different bridge models. Incremental dynamic analysis is conducted using this software and IDA and fragility curves of models are derived and presented. The results show that the probability of failure decreases with the increase of the pier diameter, concrete compressive strength, yield strength of longitudinal rebars and diameter of longitudinal bars. Also increasing the stiffness of elastic isolator and decreasing confined diameter of pier results in increasing the probability of failure. Furthermore, results show that, the probability of failure is more sensitive on the variation of pier confined diameter, yield strength of longitudinal rebars, diameter of longitudinal bars and the stiffness of elastic isolators in comparison with the variation of concrete compressive strength.