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Showing 2 results for Fathollahy

Dr Mohammad Fathollahy, Mr. Habib Rahimi Menbar, Dr. Gholamreza Shoaei,
Volume 16, Issue 3 (Autumn 2022)
Abstract

Shear strength parameters are important for assessing the stability of structures, and are costly to calculate using conventional methods. In this research, simple geotechnical techniques and artificial intelligence were used to calculate the angle of internal friction and soil cohesion without the need for more complex testing. To this end, intact samples from 14 boreholes in Bandar Abbas, which had undergone primary geotechnical testing and direct cutting, were selected and used to train neural networks.  195 networks were trained in in this research. To achieve the best performance, feedforward neural networks were first trained in single and double layer modes with a low number of neurons in the middle layer, and the TRAIN BR function was selected due to the high ratio of R (0.97). Then, by incorporating additional layers, the Median model was trained using configurations of 3, 4, and 5 layers, each with varying numbers of neurons in the intermediate layer (50, 40, 30, 20, and 10). The results show that the four-layer MLP network gives the best results, for this mode R training 1, the test R is 0.90 and the total R is 0.98. Finally, to validate the neural network, 15 samples were selected and the input parameters of the network were trained in the optimal states of 2, 3, and 4 layers, then the output of the network was evaluated. For cohesion prediction, the neural network in 4-layer mode (R2=0.99) and 2, 3 and 4-layer networks (R2=0.99) have the best output for the friction angle.

Miss Faeze Majidi, Dr Mohammad Fathollahy, Engineer Habib Rahimi Menbar,
Volume 17, Issue 3 (Autumn 2023)
Abstract

Aggregate is the main component of concrete and plays an essential role in the quality of concrete. Alkaline silicate reaction (ASR) is one of the most important reactions in concrete that can lead to concrete destruction. Aggregates containing active silica are responsible for this reaction, and the higher the amount, the greater the expected volume of reactions. The rate of increase of the reactions with changes in the amount of silica aggregates is part of the subject of this research. In this regard, a material was selected as the base material from the mountain quarry, and the necessary tests were performed on it by adding silica aggregates, 5, 10, 15, and 20 percent, the ASR test was performed on them according to the ASTM C1260 standard; The results showed that the expansion of the samples will increase by 0.01, 0.02, 0.04 and 0.06% respectively. Next, for the effect of microsilica on ASR, 5, 10, 15, and 20% were added to the materials and the results showed that microsilica reduced the expansion of the samples by 0.009, 0.014, 0.022, and 0.032 respectively and the increase of 20% of microsilica has reduced the expansion of the samples by 50%.


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