Set Theoretical Variants of Optimization Algorithms for System Reliability-based Design of Truss Structures

In this paper, recently developed set theoretical variants of the teaching-learning-based optimization (TLBO) algorithm and the shuffled shepherd optimization algorithm (SSOA) are employed for system reliability-based design optimization (SRBDO) of truss structures. The set theoretical variants are designed based on a simple framework in which the population of candidate solutions is divided into some number of smaller well-arranged sub-populations. In addition, the framework is applied to the Jaya algorithm, leading to a set-theoretical variant of the Jaya algorithm. So far, most of the reliability-based design optimization studies have focused on the reliability of single structural members. This is due to the fact that the optimization problems with system reliability-based constraints are computationally expensive to solve. This is especially the case of statically redundant structures, where the number of failure modes is so high that it is impractical to identify all of them. System-level reliability analysis of truss structures is carried out by the branch and bound method by which the stochastically dominant failure paths are identified within a reasonable time. At last, three numerical examples, including size optimization of truss structures, are presented to illustrate the effectiveness of the proposed SRBDO approach. The results indicate the efficiency and applicability of the set theoretical optimization algorithms to solve the SRBDO problems of truss structures

Optimal Design of Reinforced Concrete Cantilever Retaining Walls Utilizing Eleven Meta-Heuristic Algorithms: A Comparative Study

In this paper, optimum design of reinforced concrete cantilever retaining walls is performed under static and dynamic loading conditions utilizing eleven population-based meta-heuristic algorithms. These algorithms consist of Artificial Bee Colony algorithm, Big Bang-Big Crunch algorithm, Teaching-Learning-Based Optimization algorithm, Imperialist Competitive Algorithm, Cuckoo Search algorithm, Charged System Search algorithm, Ray Optimization algorithm, Tug of War Optimization algorithm, Water Evaporation Optimization algorithm, Vibrating Particles System algorithm, and Cyclical Parthenogenesis Algorithm. Two well-known methods consisting of the Rankine and Coulomb methods are used to determine lateral earth pressures acting on cantilever retaining wall under static loading condition. In addition, Mononobe-Okabe method is employed for dynamic loading condition. The design is based on ACI 318-05 and the goal of optimization is to minimize the cost function of the cantilever retaining wall. The performance of the utilized algorithms is investigated through an optimization example of cantilever retaining wall. In addition, convergence histories of the algorithms are provided for better understanding of their performance

On the Generalized Log Burr III Distribution: Development, Properties, Characterizations and Applications

In this paper, we present a generalized log Burr III (GLBIII) distribution developed on the basis of a generalized log Pearson differential equation (GLPE). The density function of the GLBIII is exponential, arc, J, reverse-J, bimodal, left-skewed, right- skewed and symmetrical shaped. The hazard rate function of GLBIII distribution has various shapes such as constant, increasing, decreasing, increasing-decreasing, upside- down bathtub and modified bathtub. Descriptive measures such as quantile function, sub- models, ordinary moments, moments of order statistics, incomplete moments, reliability and uncertainty measures are theoretically established. The GLBIII distribution is characterized via different techniques. Parameters of the GLBIII distribution are estimated using maximum likelihood method. A simulation study is performed to illustrate the performance of the maximum likelihood estimates (MLEs). Goodness of fit of this distribution through different methods is studied. The potentiality and usefulness of the GLBIII distribution is demonstrated via its applications to two real data sets

On the Burr XII-moment Exponential Distribution

In this study, a new flexible lifetime model called Burr XII moment exponential (BXII-ME) distribution is introduced. We derive some of its mathematical properties including the ordinary moments, conditional moments, reliability measures and characterizations. We employ different estimation methods such as the maximum likelihood, maximum product spacings, least squares, weighted least squares, Cramer-von Mises and Anderson-Darling methods for estimating the model parameters. We perform simulation studies on the basis of the graphical results to see the performance of the above estimators of the BXII-ME distribution. We verify the potentiality of the BXII-ME model via monthly actual taxes revenue and fatigue life applications

Effectiveness of Value Engineering in Reducing Delay in Urban Projects

Because of misappropriate design, technology complications, excessive number of organizations and involving people in the project, variation of needed qualifications and extensive number of activities, development projects don’t execute within primary determined cost and time constraint. Therefore presence of a regular system for optimizing investment in development and urban plans is needed. Optimization methods causing project implementation within primary cost and time constraint work are effective to eliminate negative effects of mentioned factors. The objective of this research is to investigate the effect of using value engineering in order to eliminate causes of delays in the urban projects of country. Regarding the results of this research, the most and least effect of value engineering are in order to eliminate delay causes arising from integration and human resource mismanagement. Moreover, value engineering must be employed for refinement and optimization of processes before and during project execution in order to decline time delay and costs and to increase value of projects. Value engineering studies, specifically whenever strategic decisions should be taken, could affect positively on the project execution. Finally, employing value engineering technique and compounding these methods to project management science can increase the value index of urban projects

Multiple Teachers-Meticulous Student:A Domain Adaptive Meta-Knowledge Distillation Model for Medical Image Classification

Background: Image classification can be considered one of the key pillars of medical image analysis. Deep learning (DL) faces challenges that prevent its practical applications despite the remarkable improvement in medical image classification. The data distribution differences can lead to a drop in the efficiency of DL, known as the domain shift problem. Besides, requiring bulk annotated data for model training, the large size of models, and the privacy-preserving of patients are other challenges of using DL in medical image classification. This study presents a strategy that can address the mentioned issues simultaneously. Method: The proposed domain adaptive model based on knowledge distillation can classify images by receiving limited annotated data of different distributions. The designed multiple teachers-meticulous student model trains a student network that tries to solve the challenges by receiving the parameters of several teacher networks. The proposed model was evaluated using six available datasets of different distributions by defining the respiratory motion artefact detection task. Results: The results of extensive experiments using several datasets show the superiority of the proposed model in addressing the domain shift problem and lack of access to bulk annotated data. Besides, the privacy preservation of patients by receiving only the teacher network parameters instead of the original data and consolidating the knowledge of several DL models into a model with almost similar performance are other advantages of the proposed model. Conclusions: The proposed model can pave the way for practical clinical applications of deep classification methods by achieving the mentioned objectives simultaneously