International Journal of Reliability, Risk and Safety: Theory and Application
Volume:5 Issue: 2, Dec 2022

Reinforced concrete tall building failure, in residual areas, can cause catastrophic disaster if they can’t survive during the destructive earthquakes. Hence, determining the damage of these buildings in the earthquake and detecting the probable mechanism formation are necessary for insurance purposes in urban areas. This paper aims to determine the failure modes of the moment resisting concrete frames (MRFs) according to the damage of the beam and column.  To achieve this goal, a 15-storey moment resisting reinforced concrete frame is modeled via IDARC software, and nonlinear dynamic time history analysis is performed through 60 seismic accelerograms. Then the collapse and non-collapse vectors are constructed obtaining the results of dynamic analysis in both modes. The artificial neural network is used for the classification of the obtained modes. The results show good agreement in failures classes. Hence it is possible to introduce the simple weight factor for frame status identification.

For both big and small flows, reverse osmosis is particularly effective at treating brackish, surface, and groundwater. Pharmaceutical, boiler feed water, food and beverage, metal finishing, and semiconductor production are a few examples of businesses that employ RO water. This research establishes a method for testing the performance reliability of RO systems. The RO can turn unrestricted amounts of impure water into portable drinkable water without releasing carbon dioxide or other contaminants into the atmosphere. Because of these advantages, RO has been increasingly incorporated to meet pure water demand. In the present research, we consider a reverse osmosis system that is made up of six components. Raw water tank, with two units of tanks, one of them needs to be operational at a time. The second subsystem is the sand filter. The sand filter is needed for the system operation. The third subsystem is the activated carbon filter. Two out of three consecutive units of the activated carbon filter are necessary for the operation. Subsequently, the precision filter has one unit. And the unit is essential for operation. The RO membrane is the next subsystem. In this paper, one out of the three is essential for the system to be in operation. Finally, the last subsystem is the water-producing tank. One out of one of the water-producing tanks is necessary for operation. Availability, mean time to failure (MTTF), cost analysis, and reliability are discussed in the paper.

Systems with multiple components and various configurations are classified as complex. Unless failure modes are carefully considered, the replacement of components or breakdown can lead to the shutdown of the whole system. Because of this, maintaining a complex system output can be challenging, especially if the right preventive maintenance schedule is not determined. In order to support replacement activities, a sufficient supply of spare parts is required. Based on the failure mode identified and effects analysis, this research presents an integrated preventive maintenance scheduling methodology for complex systems. Components and subsystems in the system can be modelled, such that failures in different parts of the system can be predicted based on expected life. To maintain a high level of production during PM, the need to analyze failure modes that result in only partial system failures is necessary. For determining the required number of spare parts, we factor in preventive replacements for each FMEA block. Optimal replacement intervals and spare part quantities are determined using the genetic algorithm. In order to demonstrate the application of the proposed method, numerical experiments are conducted. The developed method in this paper not only improves system reliability and minimises costs but also maintains the continuity of system outcomes during replacement activities.

As we know, people are primarily at risk of different incidents during their life, especially when they encounter unpredictable accidents. For example, fires in public places such as governmental or trade centers during their daily activities make them obliged to evacuate the building rapidly. This research deals with the fire safety of mentioned people by means of the probabilistic method. For this purpose, fire safety is addressed by modeling the egress of the people from the fire to a safe zone. A trade center building with a common layout has been chosen for safety analysis and a limit state function has been developed according to the timeline evacuation model and fire scenario. To define the safety of building visitors, the safety index method has been selected for computing the probability of trapping in fire (fatality) and safety index (beta index). The harmony search algorithm has been used to obtain Hasfoer and Lind reliability index. A sensitivity analysis of the model’s variables has been done to find the most important and effective parameters for fire safety. Results show response time to the fire, area of buildings and length of escape route are more critical in buildings. In other words, the safety of people will improve with decreasing response time and length of evacuation route and increasing dimensions of interior space of buildings.

One of the most important steps to design an engineering system is reliability allocation. Often, redundancy is used to achieve a highly reliable system. The redundancy allocation problem (RAP) is increasingly becoming an important tool in the initial stages of or prior to the plan, design, and control of systems. The multi-level redundancy allocation problem (MLRAP) is an extension of the traditional RAP such that all available items for redundancy (system, module, and component) can be simultaneously chosen. Although RAP has been considered by several researchers, MLRAP attracts only a little attention. Ordinarily, reliability uncertainty is ignored too. In this paper, this subject is studied and a new method to solve MLRAP is developed. The total cost is considered the most important constraint. A new meta-heuristic optimization algorithm, called Modified bat algorithm (MBA), to solve the constrained optimization problem (MLRAP) is proposed. This method is based on the Bat behavior to detect a prey. To demonstrate this method's capability, MLRAP for a system is described. The results are comprised with HGA, MA, and two-dimensional arrays encoding and a hybrid genetic algorithm (TDA-HGA). For this system, optimal results are the same as TDA-HGA and better than HGA and MA in all cases. Also, the reliability uncertainty and its influence on reliability allocation are studied. The optimal result is changed when uncertainty is considered. The proposed method is a simple and powerful tool to determine the optimal multi-level redundancy allocation and reliability uncertainty modeling.

A phased-mission system (PMS) involves several different tasks or phases that must be accomplished in sequence. The system configuration, task success criteria, and component failure characteristics may vary from phase to phase. Consequently, the reliability evaluation of PMSs is more challenging than that of single-phase in the field of system reliability analysis. The paper deals with the reliability evaluation of non-repairable Phased-Mission Systems with three phases and five phases involving dependent components in each phase.  The cumulative exposure model has been used to model a PMS, and the dependency between components of a system in a phase is modeled using the Gumbel-Hougaard copula. Reliability importance analyses of the 3-PMS and  5-PMS  have also been carried out. The method developed has been illustrated using numerical examples. The proposed methodology can also be generalized to PMSs with more than five phases.

Jaynes's information principle, i.e., maximum entropy principle (MEP), constrained by probability weighted moments (PWM), has been well established as an alternative method to directly estimate quantile functions (QF) from samples of a random variable. The existence, unbiasedness, and efficiency of the maximum entropy QFs have been illustrated in the literature. However, the issue of how many orders of PWMs is optimal for a given sample of data remains unsolved, and applications of the maximum entropy QFs to reliability analysis in civil engineering are still obscure. This paper serves four main purposes (1) a new general formulation is developed for the PWM-based MEP without sample normalization; (2) the optimal order of PWMs in MEP is determined by another information principle, i.e., Akaike information criterion; (3) The feasibility of the proposed maximum entropy QFs is illustrated by two case studies in probabilistic modeling of the soil undrained shear strength and the flood frequency; (4) applications of the proposed maximum entropy QFs are substantiated in QF-based first order reliability analysis of a cantilever steel beam with uncorrelated random variables and with correlated random variables. The maximum entropy QFs are compared to common empirical probability distributions, such as normal and lognormal distributions, in reliability analysis to demonstrate the advantages and disadvantages of the method developed.

Due to the unique features of High Tech products such as rapid and continuous changes in them, competition in production innovation, short life, the use of advanced technology and transformational management and leadership, the need to ensure product quality before delivery to the customer is of great importance. This guarantee is done by establishing a quality management system and observing the related requirements. The purpose of this study is to identify and rank the factors affecting the quality assurance of high tech products with a structural equation modeling approach. The statistical population of this study were managers and quality control experts of engineering and turbine manufacturing companies. Structural equations and SMART PLS software were used to confirm the indicators and model fit. Findings of the study indicate the identification of 21 indicators that according to experts are classified into five key factors. The results showed that all five factors had a significant effect on quality assurance of High Tech products. Also in the ranking, the quality control factor ranked first, the technical and engineering factor ranked second, the technology factor ranked third, the system process factor ranked fourth, and the leadership factor ranked fifth.

One of the inputs of probabilistic seismic hazard analysis (PSHA) is the minimum magnitude (mmin) of damaging earthquakes. Recent studies have shown that the choice of mmin can affect the results of PSHA. That is, if the mmin value is low, the PSHA will be overestimated. Therefore, it is important to choose the mmin value in such a way that earthquakes with greater magnitude than mmin have the capability to damage the structure. Obviously, the mmin depends on the characteristics of the structure and the earthquake. The mechanism of occurrence of earthquakes in each region is such that earthquakes with different characteristics can occur. Therefore, earthquakes with the same magnitude cause different levels of damage to the structure. This paper uses a tapered line instead of the cut-off magnitude for mmin. In this regard, we model The 3, 5, and 8-story intermediate concrete frame using Opensees software and perform time history dynamic analysis based on 246 earthquake accelerograms. The structural damage is assumed based on the drift ratio. The drift ratio of 0.004 is assumed as the limit state for the operational performance (OP) level. Using the non-uniform distance number, the mmin taper line is obtained as [4.5, 5.5]. This number can be used as the integral lower bound in the PSHA.

Industries' increasing progress and complexity has made maintenance and repair tasks very challenging, complex, and time-consuming. Maintenance is one of the important sectors in several industries, and improvement in this sector can have excellent results. This paper develops a new maintenance prediction model based on Bayesian networks (BN) capabilities. The models include several variables that experts determine and their influence on each other's-called conditional probability tables-which are learned from historical data. The model is implemented in an automobile repair department case study to show its performance. The model is evaluated through a sensitivity analysis, and the results show the proficiency of the proposal mode.

This paper presents a comprehensive framework for enhancing the safety and reliability of quadrotor UAVs by integrating second-order sliding mode control (2-SMC) and an advanced anomaly detection and prediction system based on machine learning and AI. The paper addresses the challenges of designing controllers for quadrotors by proposing a novel sliding manifold approach divided into two subsystems for accurate position and attitude tracking. The paper also provides a detailed analysis of the nonlinear coefficients of the sliding manifold using Hurwitz stability analysis. It demonstrates the effectiveness of the proposed method through extensive simulation results. To further assess the safety and reliability of the quadrotor, an anomaly detection and prediction system is integrated with the position and attitude tracking control. The system utilizes machine learning and AI techniques to identify and predict abnormal behaviours or faults in real time, enabling the quadrotor to quickly and effectively respond to critical situations. The proposed framework provides a promising approach for designing robust and safe controllers for quadrotor UAVs. It demonstrates the potential of advanced machine learning and AI techniques for enhancing the safety and reliability of autonomous systems.

Considering the importance of risk assessment for exploitation projects of new technologies in the manufacturing power plant equipment industry in MAPNA Group, a suitable model for assessing the related risks was extracted, along with identifying and ranking the factors affecting it. This study was carried out using the library-field method, and data collection tools were questionnaires and interviews. It should be noted that with the review of the literature and study of the related research along with the expert viewpoints, a number of 78 measured variables affecting the risk assessment model for the exploitation of technologies in the power generation industry were extracted. Finally, 43 measured variables that affect the mentioned model were determined after screening by expert judgment and university professors in the form of 8 latent variables. Then, a questionnaire was developed and distributed among 89 experts in the field of power plant equipment, and the completed questionnaires were collected. To test the research model's validation and goodness of fit (GOF), the variables and their effects, confirmatory factor analysis using structural equation modeling and Smart PLS software were used, and 24 measured variables were accepted. In addition, paired comparisons with the analysis of the network process and Super Decision software were used to prioritize the variables affecting the risk assessment model for the exploitation of new technologies in power plant equipment industry. The results show that the risk assessment model for exploiting new technologies in the power plant manufacturing industry includes 7 latent variables: 1- Operational and Processes 2-Human 3- Technical and Technological, 4- Environmental and Industrial, 5- Strategic, 6- Financial, and 7- Managerial. Also, ranking showed that variables such as Technical and technological, Operational and Processes, and Human ranked first to third, and financial variables ranked last.