Optimum design of grillage structures to LRFD-AISC with teaching-learning based optimization

2013 ◽  
Vol 48 (5) ◽  
pp. 955-964 ◽  
Author(s):  
Tayfun Dede
Keyword(s):  
Optimum Design ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

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.

Optimum Design of Reinforced Concrete Retaining Walls

2020 ◽  
pp. 161-182
Keyword(s):  
Reinforced Concrete ◽  
Optimum Design ◽  
Optimization Algorithm ◽  
Computer Code ◽  
Retaining Walls ◽  
Optimization Process ◽  
Stability Conditions ◽  
Teaching Learning Based Optimization ◽  
The Stability ◽  
Teaching Learning

In this chapter, the optimization of reinforced concrete (RC) retaining walls is presented. RC retaining walls are one of the structural types that are constructed on land and used for retaining soil backfill. Because of this reason, both structural and geotechnical limits are in progress in the optimization process. Additionally, the stability conditions against pressure of soils are the key constraints in the optimum design of RC retaining walls. The presented methodology in this chapter considers both static and dynamic soil pressures resulting from earthquakes. A computer code employing teaching-learning-based optimization algorithm is also given.

Optimum variable input speed for kinematic performance of Geneva mechanisms using teaching-learning-based optimization algorithm

2015 ◽  
Vol 231 (10) ◽  
pp. 1871-1883 ◽  
Author(s):  
WY Lin ◽  
YH Tsai ◽  
KM Hsiao
Keyword(s):  
Optimum Design ◽  
Angular Position ◽  
Angular Acceleration ◽  
Optimization Methods ◽  
Polynomial Coefficients ◽  
Design Variables ◽  
Teaching Learning Based Optimization ◽  
Kinematic Performance ◽  
The Difference ◽  
Teaching Learning

An optimum design of variable input speed for the Geneva mechanism is aimed at improving the kinematic performance of the traditional Geneva mechanism by eliminating infinite angular jerks and reducing the peak angular acceleration of the Geneva wheel during the indexing motion. The normalized angular velocity and acceleration of the Geneva wheel corresponding to the normalized time are introduced. A polynomial function of the normalized time is used to describe the normalized angular position of the crank, and therefore, the corresponding polynomial coefficients are considered as the design variables. The optimum design task is very specialized and difficult to solve with some evolutionary and swarm optimization methods because of the extremely large range for the value of the design variable, arising from the utilization of a higher order polynomial for the normalized time parameter with a value between 0 and 1. A new evolutionary algorithm termed teaching-learning-based optimization comprises a teacher phase and a learner phase. In the teacher phase, the entire population can be gradually shifted to a more promising region, which may be very far from the relatively small initial region. The obtained optimal results are compared with those obtained using the length-adjustable deriving link method discussed in the literature. The findings show that the difference in the effectiveness of the variable input speed method and the length-adjustable driving link method for the reduction of the peak angular acceleration of the Geneva wheel is small.

scholarly journals Optimum Design of Braced Steel Space Frames including Soil-Structure Interaction via Teaching-Learning-Based Optimization and Harmony Search Algorithms

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Ayse T. Daloglu ◽  
Musa Artar ◽  
Korhan Ozgan ◽  
Ali İ. Karakas
Keyword(s):  
Optimum Design ◽  
Soil Structure ◽  
Harmony Search ◽  
Soil Structure Interaction ◽  
Structure Interaction ◽  
Space Frames ◽  
Foundation Model ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Steel Space Frames

Optimum design of braced steel space frames including soil-structure interaction is studied by using harmony search (HS) and teaching-learning-based optimization (TLBO) algorithms. A three-parameter elastic foundation model is used to incorporate the soil-structure interaction effect. A 10-storey braced steel space frame example taken from literature is investigated according to four different bracing types for the cases with/without soil-structure interaction. X, V, Z, and eccentric V-shaped bracing types are considered in the study. Optimum solutions of examples are carried out by a computer program coded in MATLAB interacting with SAP2000-OAPI for two-way data exchange. The stress constraints according to AISC-ASD (American Institute of Steel Construction-Allowable Stress Design), maximum lateral displacement constraints, interstorey drift constraints, and beam-to-column connection constraints are taken into consideration in the optimum design process. The parameters of the foundation model are calculated depending on soil surface displacements by using an iterative approach. The results obtained in the study show that bracing types and soil-structure interaction play very important roles in the optimum design of steel space frames. Finally, the techniques used in the optimum design seem to be quite suitable for practical applications.

TLBO-FLN: Teaching-Learning Based Optimization of Functional Link Neural Networks for Stock Closing Price Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 522-532 ◽  
Author(s):  
Sarat Chandra Nayak ◽  
Subhranginee Das ◽  
Mohammad Dilsad Ansari
Keyword(s):  
Neural Networks ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Fine Tuning ◽  
Functional Link ◽  
Price Prediction ◽  
Closing Price ◽  
Teaching Learning Based Optimization ◽  
Artificial Neural ◽  
Teaching Learning

Background and Objective: Stock closing price prediction is enormously complicated. Artificial Neural Networks (ANN) are excellent approximation algorithms applied to this area. Several nature-inspired evolutionary optimization techniques are proposed and used in the literature to search the optimum parameters of ANN based forecasting models. However, most of them need fine-tuning of several control parameters as well as algorithm specific parameters to achieve optimal performance. Improper tuning of such parameters either leads toward additional computational cost or local optima. Methods: Teaching Learning Based Optimization (TLBO) is a newly proposed algorithm which does not necessitate any parameters specific to it. The intrinsic capability of Functional Link Artificial Neural Network (FLANN) to recognize the multifaceted nonlinear relationship present in the historical stock data made it popular and got wide applications in the stock market prediction. This article presents a hybrid model termed as Teaching Learning Based Optimization of Functional Neural Networks (TLBO-FLN) by combining the advantages of both TLBO and FLANN. Results and Conclusion: The model is evaluated by predicting the short, medium, and long-term closing prices of four emerging stock markets. The performance of the TLBO-FLN model is measured through Mean Absolute Percentage of Error (MAPE), Average Relative Variance (ARV), and coefficient of determination (R2); compared with that of few other state-of-the-art models similarly trained and found superior.

Sign in / Sign up

Export Citation Format

Share Document