Volume 17, Issue 4 (2017)
MCEJ 2017, 17(4): 277-287 |
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Hashemi Jokar M, mirasi S, Rahnema H. Determination of clayey soil compression index (Cc) using adaptive neuro-fuzzy inference system. MCEJ 2017; 17 (4) :277-287
URL: http://mcej.modares.ac.ir/article-16-6911-en.html
URL: http://mcej.modares.ac.ir/article-16-6911-en.html
1- phd. candidate
Abstract: (5390 Views)
Construction of buildings and structures causes to compact of soil particles and soil settlement. Hence, determination and prediction of soil settlement in the stability of structures, resulting from the applied loads, is necessary before construction. As a result of consolidation test that is relatively time-consuming and costly testing, compression index (Cc) is used to get the amount of settlement. In fact, soil settlement can cause extensive damage to a project in some cases. In order To prevent these damages, correct prediction can be useful for safe designing of structures. Cc may be as a function of various parameters such as initial void ratio of soil, moisture of liquid limit, moisture of plastic limit, plasticity index, relative density, and so on. By considering the longtime of consolidation test, researchers have tried to find relationship between these parameters and Cc from the past until now. For this reason they tried to connect Cc to other physical measurable properties of the soil.
In the past, some researchers have indirectly tried to measure these parameters. In this regard, several empirical single-parameter approaches are proposed to estimate Cc. Due to non-linear relationship between Cc and relevant parameters, Adaptive Neuro-Fuzzy Inference System (ANFIS) has found as an application to solve such non-linear problems and cases where an accurate understanding of the problem is required. ANFIS is a multilayer feed forward networks that is combination of Fuzzy Inference System (FIS) and Neural Network (NN). NN has ability to learn the input and output data and FIS is also capable for map the input space to the output space. ANFIS is a powerful tool to solve complex and nonlinear problems using the two mentioned features and also language power of FIS and numerical power of adaptive nervous system.
In this paper, Compression index (Cc) is modeled by ANFIS. Two ANFIS model were created by subtractive clustering (SC) and Fuzzy c-means clustering (FCM), respectively, and then trained. By data clustering, collection of training data is divided into a number of fuzzy clusters and each cluster representing the system behavior. The data were collected from the Soil Mechanics Laboratory of Mashhad city. ANFIS input parameters are taken according to the same parameters that commonly chosen in most of empirical models for determining Cc that easily determined in the laboratory. These input parameters include initial void ratio (e0), liquid limit (LL) and plastic limit (PL).
The number of required iterations for training (Epochs) in two ANFIS model, neighborhood radius (ra) in SC and number of clusters (NC) in FCM are optimized using trial and error method. After the end of solving and optimization of ANFIS models, the SC-FIS model was found in ra = 0.25 and NC =18 and the FCM-FIS model was obtained in NC = 20 with highest accuracy for prediction. Results showed both ANFIS model have a high capacity and appropriate forecasting for Cc prediction with chosen inputs parameters. Compared to the SC-FIS model, FCM-FIS is conducted prediction with higher accuracy. Using presented ANFIS models, can be predict the Cc of soils whose characteristics are within the specifications soils that used in this modeling with high accuracy and do not need to conduct consolidation tests that are very time consuming and costly.
In the past, some researchers have indirectly tried to measure these parameters. In this regard, several empirical single-parameter approaches are proposed to estimate Cc. Due to non-linear relationship between Cc and relevant parameters, Adaptive Neuro-Fuzzy Inference System (ANFIS) has found as an application to solve such non-linear problems and cases where an accurate understanding of the problem is required. ANFIS is a multilayer feed forward networks that is combination of Fuzzy Inference System (FIS) and Neural Network (NN). NN has ability to learn the input and output data and FIS is also capable for map the input space to the output space. ANFIS is a powerful tool to solve complex and nonlinear problems using the two mentioned features and also language power of FIS and numerical power of adaptive nervous system.
In this paper, Compression index (Cc) is modeled by ANFIS. Two ANFIS model were created by subtractive clustering (SC) and Fuzzy c-means clustering (FCM), respectively, and then trained. By data clustering, collection of training data is divided into a number of fuzzy clusters and each cluster representing the system behavior. The data were collected from the Soil Mechanics Laboratory of Mashhad city. ANFIS input parameters are taken according to the same parameters that commonly chosen in most of empirical models for determining Cc that easily determined in the laboratory. These input parameters include initial void ratio (e0), liquid limit (LL) and plastic limit (PL).
The number of required iterations for training (Epochs) in two ANFIS model, neighborhood radius (ra) in SC and number of clusters (NC) in FCM are optimized using trial and error method. After the end of solving and optimization of ANFIS models, the SC-FIS model was found in ra = 0.25 and NC =18 and the FCM-FIS model was obtained in NC = 20 with highest accuracy for prediction. Results showed both ANFIS model have a high capacity and appropriate forecasting for Cc prediction with chosen inputs parameters. Compared to the SC-FIS model, FCM-FIS is conducted prediction with higher accuracy. Using presented ANFIS models, can be predict the Cc of soils whose characteristics are within the specifications soils that used in this modeling with high accuracy and do not need to conduct consolidation tests that are very time consuming and costly.
Keywords: ANFIS, compression index, Prediction, Subtractive clustering, Fuzzy c-means clustering, Adaptive Neuro-Fuzzy Inference System
Article Type: Original Manuscript |
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Received: 2015/11/16 | Accepted: 2017/04/26 | Published: 2017/10/23
Received: 2015/11/16 | Accepted: 2017/04/26 | Published: 2017/10/23
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