Using UCS as a Surrogate Performance Standard at the NCSU NPL Site

A Systematic Review of Machine Learning Techniques and Applications in Soil Improvement Using Green Materials

“…Climate change and environmental impacts are currently challenging topics for the Earth's survival; waste management became a trending issue. Reusing the waste materials in different applications is one of the solutions that help in waste management, where one of the uses is in soil improvement applications, as it is a frequently used material in this industry [1][2][3][4][5][6][7][8]. Some industrial wastes have cementitious properties that could help replace traditional soil-reinforcing materials such as cement, where cement is a direct reason for climate change by a 5% of the carbon dioxide CO 2 emissions through its production [9][10][11].…”

“…In particular, it has great potential for improving soil improvement using green materials, which is a critical area of research for sustainable and environmentally friendly construction practices. Machine learning is one of the trending topics that was used in several geotechnical applications such as prediction of retaining wall deflection [15], excavation [16,17], soil behavior [2], Earth retaining structures [18], bearing capacity [19][20][21][22][23][24], settlement of structures [8,18,20,[24][25][26][27][28], liquefaction assessment [29], slope stability and landslide [30,31], and soil characterization [2,3,7,[32][33][34][35][36][37]. That helps geotechnical engineers to control and predict the soil properties and behavior under various loading conditions.…”

“…That helps geotechnical engineers to control and predict the soil properties and behavior under various loading conditions. In addition, it is used to predict the stress-strain behavior and shear resistance of the after-treatment using waste materials [3,7,15,38,39], which helps in controlling the used ratios of waste materials [5,21]. However, minimizing the error of the prediction model is one of the challenging problems which depends on the algorithm used for predicting [40].…”

“…Indeed, the applications of machine learning in geotechnical engineering are many, such as prediction of settlement [8,20,27], compressive strength [3,38,39,[51][52][53], bearing capacity [22,24], stress-strain behavior [2], and compaction performance [7]. Several studies classified and reviewed machine learning applications in geotechnical engineering for predicting landslide, strength, settlements, bearing capacity, liquefaction, and slope stability [40,[54][55][56].…”