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Optimum cost design of frames using genetic algorithms

ArticleJournal paper
Chulin Chen, Salim Taib Yousif, Rabi Muyad Najem, Ali Abavisani, Binh Thai Pham, Karzan Wakil, Edy Tonnizam Mohamad and Majid Khorami
Chen, Chulin, Salim Taib Yousif, Rabi’ Muyad Najem, Ali Abavisani, Binh Thai Pham, Karzan Wakil, Edy Tonnizam Mohamad, and Majid Khorami. “Optimum Cost Design of Frames Using Genetic Algorithms.” Steel and Composite Structures 30, no. 3 (February 10, 2019): 293–304. doi:10.12989/SCS.2019.30.3.293.
Publication year: 2019

Abstract

The optimum cost of a reinforced concrete plane and space frames have been found by using the Genetic Algorithm (GA) method. The design procedure is subjected to many constraints controlling the designed sections (beams and columns) based on the standard specifications of the American Concrete Institute ACI Code 2011. The design variables have contained the dimensions of designed sections, reinforced steel and topology through the section. It is obtained from a predetermined database containing all the single reinforced design sections for beam and columns subjected to axial load, uniaxial or biaxial moments. The designed optimum beam sections by using GAs have been unified through MATLAB to satisfy axial, flexural, shear and torsion requirements based on the designed code. The frames\’ functional cost has contained the cost of concrete and reinforcement of steel in addition to the cost of the frames\’ formwork. The results have found that limiting the dimensions of the frame\’s beams with the frame\’s columns have increased the optimum cost of the structure by 2%, declining the re-analysis of the optimum designed structures through GA.

Keywords

  • Optimum design
  • Genetic algorithm;
  • Space frame
  • Reinforced concrete

Developing a hybrid adoptive neuro-fuzzy inference system in predicting safety of factors of slopes subjected to surface eco-protection techniques

ArticleJournal paper
Puteri Azura Sariو Meldi Suhatril, Normaniza Osman, M. A. Mu’azu, Javad Katebi, Ali Abavisani, Naser Ghaffari, Esmaeil Sadeghi Chahnasir, Karzan Wakil, Majid Khorami, Dalibor Petkovic
Sari, P.A., Suhatril, M., Osman, N. et al. Engineering with Computers (2019). https://doi.org/10.1007/s00366-019-00768-3
Publication year: 2019

Abstract

This study predicts the investigation of surface eco-protection techniques for cohesive soil slopes along the selected Guthrie Corridor Expressway stretch by way of analyzing a new set of probabilistic models using a hybrid technique of artificial neural network and fuzzy inference system namely adaptive neuro-fuzzy inference system (ANFIS). Soil erosion and mass movement which induce landslides have become one of the disasters faced in Selangor, Malaysia causing enormous loss affecting human lives, destruction of property and the environment. Establishing and maintaining slope stability using mechanical structures are costly. Hence, biotechnical slope protection offers an alternative which is not only cost effective but also aesthetically pleasing. A parametric study was carried out to discover the relationship between various eco-protection techniques, i.e., application of grasses, shrubs and trees with different soil properties as well as slope angles. Then the data have been used to develop a new hybrid ANFIS technique for prediction of factor of safety (FOS) of slopes. Four inputs were considered in relation to the different vegetation types, i.e., slope angle (θ), unit weight (γ), effective cohesion (c′), effective friction angle (ø′). Then, many hybrid ANFIS models were constructed, trained and tested using various parametric studies. Eventually, a hybrid ANFIS model with a high performance prediction and a low system error was developed and introduced for solving problem of slope stability.

Keywords

  • ANFIS;
  • ANN
  • Fuzzy
  • Eco-engineering
  • Factor of safety
  • Slope stability