Decision-making model based multilayer perceptrons for estimation of optimum design properties for truss structure

2021 ◽  
Vol 7 (4) ◽  
pp. 170
Author(s):  
Melda Yücel ◽  
Gebrail Bekdaş ◽  
Sinan Melih Nigdeli
Keyword(s):  
Decision Making ◽  
Optimum Design ◽  
Structural Engineering ◽  
Structural Model ◽  
Population Based ◽  
Multilayer Perceptrons ◽  
Optimal Section ◽  
Optimum Model ◽  
Teaching Learning ◽  
Decision Making Model

Many branches of the structural engineering discipline have many problems, which require the generating an optimum model for beam-column junction area reinforcement, weight lightening for members such a beam, column, slab, footing formed as reinforced concrete, steel, composite, and so on, cost arrangement for any construction, etc. With this direction, in the current study, a structural model as a 5-bar truss is handled to provide an optimum design by determining the fittest areas of bar sections. It is aimed that the total bar length is minimized through population-based metaheuristic algorithm as teaching-learning-based optimization (TLBO). Following, the decision-making model is developed via multilayer perceptrons (MLPs) by performing an estimation application to enable directly foreseen of the optimal section areas and total length of bars, besides, the approximation and correlation success are evaluated via some metrics. Thus, determination of the real optimal results of unknown and not-tested designs can be realized with this model in a short and effective time.

scholarly journals Discrete Optimum Design of Planar Steel Curved Roof and Pitched Roof Portal Frames Using Metaheuristic Algorithms

2021 ◽  
Author(s):  
Ali Kaveh ◽  
Mohammad Iman Karimi Dastjerdi ◽  
Ataollah Zaerreza ◽  
Milad Hosseini
Keyword(s):  
Optimal Design ◽  
Optimum Design ◽  
Population Based ◽  
Colliding Bodies Optimization ◽  
Industrial Buildings ◽  
Enhanced Colliding Bodies Optimization ◽  
Teaching Learning Based Optimization ◽  
Fire Stations ◽  
Teaching Learning ◽  
Discrete Optimum

Portal frames are single-story frame buildings including columns and rafters, and their rafters can be either curved or pitched. These are used widely in the construction of industrial buildings, warehouses, gyms, fire stations, agricultural buildings, hangars, etc. The construction cost of these frames considerably depends on their weight. In the present research, the discrete optimum design of two types of portal frames including planar steel Curved Roof Frame (CRF) and Pitched Roof Frame (PRF) with tapered I-section members are presented. The optimal design aims to minimize the weight of these frame structures while satisfying some design constraints based on the requirements of ANSI/AISC 360-16 and ASCE 7-10. Four population-based metaheuristic optimization algorithms are applied to the optimal design of these frames. These algorithms consist of Teaching-Learning-Based Optimization (TLBO), Enhanced Colliding Bodies Optimization (ECBO), Shuffled Shepherd Optimization Algorithm (SSOA), and Water Strider Algorithm (WSA). Two main objectives are followed in this paper. The first one deals with comparing the optimized weight of the CRF and PRF structures with the same dimensions for height and span in two different span lengths (16.0 m and 32.0 m), and the second one is related to comparing the performance of the considered metaheuristics in the optimum design of these portal frames. The obtained results reveal that CRF is more economical than PRF in the fair comparison. Moreover, comparing the results acquired by SSOA with those of other considered metaheuristics reveals that SSOA has better performance for the optimal design of these portal frames.

scholarly journals Constrained Optimization Based on Hybridized Version of Superiority of Feasibility Solution Strategy

2021 ◽  
Author(s):  
Wali Khan ◽  
Faiz Ur Rehaman ◽  
Habib Shah
Keyword(s):  
Decision Making ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Population Based ◽  
Stochastic Algorithm ◽  
Weighted Mean ◽  
Hybrid Superiority ◽  
Decision Making Factors ◽  
Two Phases ◽  
Teaching Learning

Abstract Teaching learning based optimization (TLBO) is a stochastic algorithm which was first proposed for unconstrained optimization problems. It is population based, nature-inspired, and meta-heuristic that imitates teaching learning process. It has two phases, teacher and learner. In teacher phase, the teacher who is well-learned person transfers his/her knowledge to the learners to raise their grades/results; while in learner phase, learners/pupils learn and refine their knowledge through mutual interconnection. To solve constrained optimization problems (COPs) through TLBO we need to merge it with some constraint handling technique (CHT). Superiority of feasibility (SF) is a concept for making CHTs, existed in different forms based on various decisive factors. Most commonly used decision making factors in SF are number of constraints violated (NCV) and weighted mean (WM) values for comparing solutions. In this work, SF based on number of constraints violated (NCVSF) and weighted mean (WMSF) are incorporated in the framework of TLBO. These are tested upon CEC-2006 constrained suit with the remark that single factor used for the decision making of winner is not a wise idea. Mentioned remark leads us to made a single CHT that carries the capabilities of both discussed CHTs. It laid the foundation of hybrid superiority of feasiblity (HSF); where NCV and WM factors are combined with giving dominance to NCV over WM. In current research three constrained versions of TLBO are formulated by the name NCVSF-TLBO, WMSF-TLBO, and HSF-TLBO; while implanting NCVSF, WMSF, and HSF in the framework of TLBO, respectively. These constrained versions of TLBO are evaluated on CEC-2006 with the remarks that HSF-TLBO got prominent and flourishing status among these.

Caring Enough to Provide Healthcare: An Organizational Framework for the Ethical Delivery of Healthcare Among Aging Patients

2010 ◽  
Vol 14 (4) ◽  
pp. 27-30
Keyword(s):  
Decision Making ◽  
Ethical Issue ◽  
Healthcare Resource ◽  
Medical Condition ◽  
Population Based ◽  
Delivery Of Healthcare ◽  
Organizational Decision Making ◽  
Healthcare Resources ◽  
Decision Making Model ◽  
Healthcare Resource Allocation

Healthcare allocation among aging patients is at the forefront of ethical discussions. The ultimate ethical issue of aged-based rationing is not whether too little or too much treatment is offered, but rather establishing a protocol for seeking optimum reasonable treatment based upon the medical condition of aging patients. Without an established model, aging patients go without needed healthcare resources. The ethical issue that the model will address is not one of prolonging or terminating life, but giving resources to those who fairly deserve them. This paper describes an organizational decision making model for healthcare resource allocation among the aging patient population based upon the theory of distributive justice.

Applying the Multi-Criteria Decision Making Model for Ranking Commercial Banks: The Case of Vietnam

2019 ◽  
pp. 125-133
Author(s):  
Duong Truong Thi Thuy ◽  
Anh Pham Thi Hoang
Keyword(s):  
Decision Making ◽  
Commercial Banks ◽  
Decision Model ◽  
Business Models ◽  
Multi Criteria Decision Making ◽  
Ideal Solution ◽  
Topsis Method ◽  
Decision Making Model

Banking has always played an important role in the economy because of its effects on individuals as well as on the economy. In the process of renovation and modernization of the country, the system of commercial banks has changed dramatically. Business models and services have become more diversified. Therefore, the performance of commercial banks is always attracting the attention of managers, supervisors, banks and customers. Bank ranking can be viewed as a multi-criteria decision model. This article uses the technique for order of preference by similarity to ideal solution (TOPSIS) method to rank some commercial banks in Vietnam.

Multi-Attribute Decision-Making Model by Applying Grey Numbers

Informatica ◽  
2009 ◽  
Vol 20 (2) ◽  
pp. 305-320 ◽  
Author(s):  
Edmundas Kazimieras Zavadskas ◽  
Arturas Kaklauskas ◽  
Zenonas Turskis ◽  
Jolanta Tamošaitienė
Keyword(s):  
Decision Making ◽  
Multi Attribute Decision Making ◽  
Decision Making Model
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