TY  - JOUR
TT  - Optimum design of space truss tower using teaching-learning based optimization
AU  - Artar, Musa
PY  - 2016
DA  - December
JF  - Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi
JO  - DUJE
PB  - Dicle University
WT  - DergiPark
SN  - 1309-8640
SP  - 471
EP  - 480
VL  - 7
IS  - 3
KW  - Uzay kafes kule
KW  - Öğretme-öğrenme esaslı optimizasyon
KW  - MATLAB-SAP2000 OAPI
KW  - Optimum boyutlandırma.
N2  - Minimum weight design of steel structures is an important research subject in structural engineering. Main purpose in this subject is to reduce steel consumption. Steel structures include discrete design variables and meta-heuristic algorithm methods are very suitable for optimum design of them. In this study, optimum design of 942-bar steel space truss tower is investigated by using teaching learning based optimization which has been developed in recent years. As in the other stochastic algorithm techniques such as genetic algorithm, harmony search algorithm, ant colony optimization, artificial bee algorithm, simulated annealing, tabu search algorithm, particle swarm optimization etc.; teaching-learning based optimization mimics environmental events. Analyses in this method are conducted by a class including students. Each student in class represents a structural model and information level of each student is tried to increase by teaching and learning phases. So, the best solution obeying the constraints and having minimum weight can be obtained after a specific number of iterations. This novel algorithm technique was developed by Rao et al. (2011) and comprised of two main phases such as teaching phase and learning phase. The number of students in class presents population size. Initial class is randomly created and the best solution in class is selected as teacher. In teaching phase, other students in class are updated to take information from teacher. If new student provides a better design solution, it is replaced with old student. In the learning phase, students in class are updated to get information among them. This phase is very similar to teaching phase. If new student produces a better solution, it is replaced with old students. 942-bar space truss tower investigated in this study is taken from literature. This example was designed by different algorithm methods in literature. The stress constraints according to AISC-ASD (American Institute of Steel Construction- Allowable Stress Design), displacement constraints and geometric size constraints for vertical members are imposed. Design profiles are selected from a specified list which is prepared in SAP2000 software and includes 128W profiles taken from AISC. A program was coded in MATLAB software which is automatically incorporated with SAP2000 OAPI (Open Application Programming Interface) to conduct practically optimization processes. 942-bar space truss tower is modeled in SAP2000 software. The structural model is continuously updated to analyze by this program. 942-bar space truss tower is an example in large scale because its members are collected into 59 different size variables. In this study, population size is selected as 20. Therefore, analyses are conducted with a class determined as 20x59 matrix. Analyses are performed along 600 iterations. Figure 3 shows the variation of the minimum steel weight with iteration steps. As seen in this figure, while steel weight in first iterations is 2000 ton, minimum steel weight is reduced to 169.868 ton after 600 iterations. The solution results obtained in this study are very close to the previous ones in literatures obtained by different algorithms. The minimum weights 172.214 ton, 171.437 ton, 171.261 ton are calculated by Hasançebi and Erbatur (2002, simulated annealing: SA), Hasançebi (2008, evolution strategies: ESs), Hasançebi et al.(2013, bat-inspired algorithm: BI), respectively. In this study, although additional constraints such as geometric size (area and dept of cross sections) for vertical members are used, the minimum weight is calculated as 169.868 ton. This weight is nearly 1% lighter than other results. These successful results show that teaching-learning based optimization is an efficient method for optimum designs of steel structures with discrete design variables. Moreover, this new method is practically applied by using MATLAB-SAP2000 OAPI. So, various structural optimization problems can be carried out by using teaching-learning based optimization
UR  - https://dergipark.org.tr/en/pub/dumf/issue//312786
L1  - https://dergipark.org.tr/en/download/article-file/302810
ER  -