Issue 53

A. Namdar, Frattura ed Integrità Strutturale, 53 (2020) 285-294; DOI: 10.3221/IGF-ESIS.23.22 285 Forecasting the bearing capacity of the mixed soil using artificial neural network Abdoullah Namdar Faculty of Architecture and Civil Engineering, Huaiyin Institute of Technology, Huai’an, China Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam abdoullah.namdar@tdtu.edu.vn A BSTRACT . The bearing capacity of soil changes owing to the mechanical properties of the soil and it influences the structural stability. In most of the geotechnical engineering projects, there are several soil mechanic experiments, that need interpretation before application. The mechanical properties of soil interaction make the prediction of soil bearing capacity complex. However, the enhancement of construction project safety needs the interpretation of soil experiments and design results for proper application in a geotechnical engineering project. In this study, artificial neural network is proposed for the evaluation of the mixed soil characteristics to forecast the safe bearing capacity of soil due to the mechanical properties of the soil interaction phenomenon. The results for prediction of the safe bearing capacity reveal that the R 2 and RMSE for all mechanical properties effects on safe bearing capacity are 0.98 and 0.02, these values can provide a suitable accuracy for the prediction of the safe bearing capacity of the mixed soil. The higher inaccuracy is obtained when only the influence of single mechanical property on the mixed soil is considered in the prediction of the safe bearing capacity. This study supports the enhancement of geotechnical engineering design quality through the prediction of safe bearing capacity from characterized mechanical properties of the soil. K EYWORDS . Bearing Capacity; Mechanical Properties; Artificial Neural Network; Mixed Soil. Citation: Namdar, A., Forecasting the bearing capacity of the mixed soil using artificial neural network, Frattura ed Integrità Strutturale, xx (2020) 285-294. Received: 26.04.2020 Accepted: 09.05.2020 Published: 01.07.2020 Copyright: © 2020 This is an open access article under the terms of the CC-BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. I NTRODUCTION aboratory experimental activity was performed for the characterization of red soil in soil mixture process and the assessment of soil mixture mechanical properties was performed for developing acceptable safe bearing capacity [1]. The bearing capacity of mixed soil foundation was investigated by changing the concrete footing dimensions and the soil mechanical properties systematically [2-3]. To study the bearing capacity of subsoil in the coastal region, the tsunami behavior was numerically simulated and mangrove for enhancement of subsoil was proposed [4]. The bearing capacity of soil foundation also was related to the seismic displacement of the soil foundation [5]. The bearing capacity of the embankment soil foundation was enhanced by developing dense zones in the subsoil [6]. The bearing capacity of the L