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Research in the area of geotechnical construction has been conducted by a scientist from South Ural State University (SUSU) and his foreign colleagues. They proposed a hybrid intelligence system that can help Civil Engineers to get the most accurate pile bearing capacity data. The results of the work have been published in the highly rated journal “Artificial Intelligence Review” (Q1).
A research group, that is led by SUSU scientist Danial Jahed Armaghani, has presented a renewed way to estimate pile bearing capacity. This property reflects the maximum load that a pile can safely carry. Bearing capacity data is necessary for builders in designing deep foundations during construction. Nowadays, there are four ways to estimate this parameter; three of them need actual tests on the site that are expensive, difficult, and time-consuming. The other one is theoretical calculation and needs adjustment based on the actual data on the characteristics of the soil.
Scientists have suggested a new technique of an advance hybrid intelligence system based on the adaptive neuro-fuzzy inference system (ANFIS)-group method of data handling (GMDH) optimized by the imperialist competitive algorithm (ICA), ANFIS-GMDH-ICA for forecasting pile bearing capacity.
“In this advanced system, the imperialist competitive algorithm role is to optimize the membership functions obtained by ANFIS-GMDH technique for receiving a higher accuracy level and lower error. It receives more accurate predicted values of pile bearing capacity compared to those obtained by ANFIS and GMDH predictive models,” says Danial Jahed Armaghani, Ph.D., the senior researcher of the Town Planning, Engineering Systems and Networks department (Institute of Architecture and Construction, SUSU).
A research group, that is led by SUSU scientist Danial Jahed Armaghani, has presented a renewed way to estimate pile bearing capacity. This property reflects the maximum load that a pile can safely carry. Bearing capacity data is necessary for builders in designing deep foundations during construction. Nowadays, there are four ways to estimate this parameter; three of them need actual tests on the site that are expensive, difficult, and time-consuming. The other one is theoretical calculation and needs adjustment based on the actual data on the characteristics of the soil.
Scientists have suggested a new technique of an advance hybrid intelligence system based on the adaptive neuro-fuzzy inference system (ANFIS)-group method of data handling (GMDH) optimized by the imperialist competitive algorithm (ICA), ANFIS-GMDH-ICA for forecasting pile bearing capacity.
“In this advanced system, the imperialist competitive algorithm role is to optimize the membership functions obtained by ANFIS-GMDH technique for receiving a higher accuracy level and lower error. It receives more accurate predicted values of pile bearing capacity compared to those obtained by ANFIS and GMDH predictive models,” says Danial Jahed Armaghani, Ph.D., the senior researcher of the Town Planning, Engineering Systems and Networks department (Institute of Architecture and Construction, SUSU).
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