TA759 : predicting lateral displacement of Geosynthetic Reinforced Soil–Integrated WallSystem Using Extreme Learning Machine
Thesis > Central Library of Shahrood University > Civil & Architectural Engineering > MSc > 2023
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The progress in the design and implementation of construction projects in the last century has been very impressive and increasing, and at every point in time, the design and implementation methods have been used, more accurate and easier than before. Construction projects have a direct impact on the quantity and quality of human life, that's why engineers around the world are engaged in numerous researches so that they can reduce the time and cost of projects by inventing new methods, and on the other hand, doing the work which way that is easier for contractors to complete the project with the highest level of accuracy and security. One of the basic parameters that are of great interest to engineers in a construction project such as excavation, foundation, etc., is the amount of lateral displacement of the project under loading. It is obvious that too much lateral displacement causes the destruction of the project and causing human and financial losses, and for this reason, the regulations emphasize this parameter. In this research, firstly, the explanation about the geosynthetic reinforced soil–integrated wall System was explained. this type of wall is usually used in the foundations of bridges and it is known by the Geosynthetic Reinforced Soil–Integrated Bridge System (GRS-IBS). using this type of wall reduces the implementation time and cost, and its implementation is easy due to simple steps and no need for professional technician. The modeling of this wall was done in Plexis software and after verification, 155 tests with different data were taken to obtain the lateral displacement. In the continuation of the research, by programming in the extreme learning machine system or so-called artificial intelligence method, and training 70% of the data as primary data, the amount of lateral displacement of the GRS-IBS wall was estimated by the extreme learning machine method. The minimum mean square error in this programming was evaluated between 1 and 5 mm, which is a good accuracy for estimating lateral displacement.
Keywords:
#Geosynthetics 2) Reinforced soil 3) Finite element analysis 4) extreme learning machine 5) artificial intelligence 6) Geosynthetic Reinforced Soil Keeping place: Central Library of Shahrood University
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