Structural Analysis of Backfill Highway Subgrade on the Lower Bearing Capacity Foundation Using the Finite Element Method

Abstract

The present study is to investigate the stability of the backfill subgrade on the lower bearing capacity foundation. A finite element (FE) model was constructed to simulate the high-filled road subjected to the actual self-weight load. The strength reduction method was adopted to establish an analysis model of slope stability. At the same time, the sensitivity analysis of the factors affecting the slope stability was carried out through parametric studies, including the elastic modulus, cohesion, internal friction angle, and slope rate. The results showed that the slope stability analysis model established by the strength reduction method can characterize the stability of the slope by calculating the slope safety coefficient. The mutation point of the relationship curve between the displacement generated in the slope and the reduction coefficient can be used as the criterion. Under the condition of a given strength reduction coefficient, the calculated results obtained through FE modeling can show the development of the equivalent plastic zone in the form of cloud diagrams.

Towards Sustainable Construction Materials: A Comparative Study of Prediction Models for Green Concrete with Metakaolin

“…Cement-based materials refer to the materials with new properties obtained by combining cement with other materials [1][2][3]. Common cement base materials have concrete, mortar, and grouting materials [4][5][6][7][8][9][10][11][12]. Cement-based materials are widely used in construction, transportation, national defense, and other fields because of their advantages: high performance, convenient production, convenient construction, high compressive strength, good corrosion resistance, good durability, and so on [13][14][15][16][17].…”

Intelligent Design of Building Materials: Development of an AI-Based Method for Cement-Slag Concrete Design

“…The large amount of carbon dioxide emitted in the process of cement production is a serious burden and threat to the environment [ 5 , 6 , 7 ]. Finding new materials to replace some of the cement used in concrete is a necessary way to ensure the sustainable development of the concrete industry [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. At present, the use of fly ash, blast furnace slag, metakaolin, and other mineral admixtures to replace part of the cement used in concrete is the main solution to alleviate the large resource consumption and negative impact on the environment in the cement production process [ 17 , 18 , 19 , 20 , 21 , 22 ].…”

Prediction of the Compressive Strength for Cement-Based Materials with Metakaolin Based on the Hybrid Machine Learning Method

“…The above machine methods achieved good results in the performance prediction of cement-based materials, and machine learning methods were widely used in the prediction of cement-based materials [ 33 , 34 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ]. Machine learning technology has been widely used in the cement-based materials performance evaluation process, but these methods still have some limitations, such as uncertainty, time-consuming, and low efficiency [ 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 ]. Therefore, it is necessary to propose a more efficient and simple machine learning technology to predict the compressive strength of metakaolin cement-based materials.…”