Development of a cost-based design model for spread footings in cohesive soils

The use of cost-effective construction design approaches is an emerging concept in the field of sustainable environments. The design of the foundation for the construction of any infrastructure-related building entails three basic requirements, i.e., serviceability limit state (SLS), ultimate limit state (ULS), and economics. Engineering economy coupled with safety are the two main essentials for a successful construction project. The conventional design approaches are based on hit and trial methods to approach cost-effective design. Additionally, safety requirements are prioritized over the economic aspect of foundation design and do not consider safety requirements and cost simultaneously. This study presents a design approach that considers foundation construction costs while satisfying all the technical requirements of a shallow foundation design. This approach is called an optimization process in which the cost-based isolated foundation design charts were developed based on the field SPT N data. The design charts are the first of their kind for the robust design of foundations and can be used to compare the economic impact of different bearing capacity models. Furthermore, the design framework considers the quantitative impact of the different applied factors of safety values in terms of cost. The results show that Vesic’s equation yields higher values of bearing capacities than Terzaghi and Meyerhof. On the other hand, Vesic’s theory offers a 37.5% reduction in cost as compared to the conventional design approach of the foundation for isolated footing

Interface frictional anisotropy of dilative sand

Abstract Understanding direction-dependent friction anisotropy is necessary to optimize interface shear resistance across soil-structure. Previous studies estimated interface frictional anisotropy quantitatively using contractive sands. However, no studies have explored how sand with a high dilative tendency around the structural surface affects the interface shear response. In this study, a series of interface direct shear tests are conducted with selected French standard sand and snakeskin-inspired surfaces under three vertical stresses (50, 100, and 200 kPa) and two shearing directions (cranial → caudal or caudal → cranial). First, the sand-sand test observes a higher dilative response, and a significant difference between the peak and residual friction angles (ϕpeak − ϕres = 8°) is obtained at even a lower initial relative density Dr = 40%. In addition, the interface test results show that (1) shearing against the scales (cranial shearing) mobilizes a larger shear resistance and produces a dilative response than shearing along the scales (caudal shearing), (2) a higher scale height or shorter scale length exhibits a higher dilative tendency and produces a higher interface friction angle, and (3) the interface anisotropy response is more pronounced during cranial shearing in all cases. Further analysis reveals that the interface friction angle and dilation angle are decreased with the scale geometry ratio (L/H). For L/H values between 16.67 and 60, the interface dilation angle varies between 9° and 4° for cranial first shearing and 3.9°–2.6° for caudal first shearing. However, the difference in dilation angle within the same shearing direction is less than 1°

Study Using Machine Learning Approach for Novel Prediction Model of Liquid Limit

The liquid limit (LL) is considered the most fundamental parameter in soil mechanics for the design and analysis of geotechnical systems. According to the literature, the LL is governed by different particle sizes such as sand content (S), clay content (C), and silt content (M). However, conventional methods do not incorporate the effect of all the influencing factors because traditional methods utilize material passing through a # 40 sieve for LL determination (LL40), which may contain a substantial number of coarse particles. Therefore, recent advancements suggest that the LL must be determined using material passing from a # 200 sieve. However, determining the liquid limit using # 200 sieve material, referred to as LL200 in the laboratory, is a time-consuming and difficult task. In this regard, artificial-intelligence-based techniques are considered the most reliable and robust solutions to such issues. Previous studies have adopted experimental routes to determine LL200 and no such attempt has been made to propose empirical correlation for LL200 determination based on influencing factors such as S, C, M, and LL40. Therefore, this study presents a novel prediction model for the liquid limit based on soil particle sizes smaller than 0.075 mm (# 200 sieve) using gene expression programming (GEP). Laboratory experimental data were utilized to develop a prediction model. The results indicate that the proposed model satisfies all the acceptance requirements of artificial-intelligence-based prediction models in terms of statistical checks such as the correlation coefficient (R2), root-mean-square error (RMSE), mean absolute error (MAE), and relatively squared error (RSE) with minimal error. Sensitivity and parametric studies were also conducted to assess the importance of the individual parameters involved in developing the model. It was observed that LL40 is the most significant parameter, followed by C, M, and S, with sensitivity values of 0.99, 0.93, 0.88, and 0.78, respectively. The model can be utilized in the field with more robustness and has practical applications due to its simple and deterministic nature

Fig 8 -

Performance assessment of prediction model based on different criteria; (a) comparison of statistical parameters for training and validation data; (b) ± 5% error bound for prediction model; (c) comparison of experimental data, GEP model data and absolute error; (d) comparison of experimental data and GEP prediction model data against training and validation data.</p

Study Using Machine Learning Approach for Novel Prediction Model of Liquid Limit

The liquid limit (LL) is considered the most fundamental parameter in soil mechanics for the design and analysis of geotechnical systems. According to the literature, the LL is governed by different particle sizes such as sand content (S), clay content (C), and silt content (M). However, conventional methods do not incorporate the effect of all the influencing factors because traditional methods utilize material passing through a # 40 sieve for LL determination (LL40), which may contain a substantial number of coarse particles. Therefore, recent advancements suggest that the LL must be determined using material passing from a # 200 sieve. However, determining the liquid limit using # 200 sieve material, referred to as LL200 in the laboratory, is a time-consuming and difficult task. In this regard, artificial-intelligence-based techniques are considered the most reliable and robust solutions to such issues. Previous studies have adopted experimental routes to determine LL200 and no such attempt has been made to propose empirical correlation for LL200 determination based on influencing factors such as S, C, M, and LL40. Therefore, this study presents a novel prediction model for the liquid limit based on soil particle sizes smaller than 0.075 mm (# 200 sieve) using gene expression programming (GEP). Laboratory experimental data were utilized to develop a prediction model. The results indicate that the proposed model satisfies all the acceptance requirements of artificial-intelligence-based prediction models in terms of statistical checks such as the correlation coefficient (R2), root-mean-square error (RMSE), mean absolute error (MAE), and relatively squared error (RSE) with minimal error. Sensitivity and parametric studies were also conducted to assess the importance of the individual parameters involved in developing the model. It was observed that LL40 is the most significant parameter, followed by C, M, and S, with sensitivity values of 0.99, 0.93, 0.88, and 0.78, respectively. The model can be utilized in the field with more robustness and has practical applications due to its simple and deterministic nature

General procedure of mutation and crossover.

General procedure of mutation and crossover.</p

Sensitivity analysis of prediction model based on sensitivity of individual input parameter.

Sensitivity analysis of prediction model based on sensitivity of individual input parameter.</p

Process of Genetic Programming (GP).

Process of Genetic Programming (GP).</p

Steps involved in developing prediction model using artificial intelligence techniques.

Steps involved in developing prediction model using artificial intelligence techniques.</p

Development of a Cost-Based Design Model for Spread Footings in Cohesive Soils

The use of cost-effective construction design approaches is an emerging concept in the field of sustainable environments. The design of the foundation for the construction of any infrastructure-related building entails three basic requirements, i.e., serviceability limit state (SLS), ultimate limit state (ULS), and economics. Engineering economy coupled with safety are the two main essentials for a successful construction project. The conventional design approaches are based on hit and trial methods to approach cost-effective design. Additionally, safety requirements are prioritized over the economic aspect of foundation design and do not consider safety requirements and cost simultaneously. This study presents a design approach that considers foundation construction costs while satisfying all the technical requirements of a shallow foundation design. This approach is called an optimization process in which the cost-based isolated foundation design charts were developed based on the field SPT N data. The design charts are the first of their kind for the robust design of foundations and can be used to compare the economic impact of different bearing capacity models. Furthermore, the design framework considers the quantitative impact of the different applied factors of safety values in terms of cost. The results show that Vesic&rsquo;s equation yields higher values of bearing capacities than Terzaghi and Meyerhof. On the other hand, Vesic&rsquo;s theory offers a 37.5% reduction in cost as compared to the conventional design approach of the foundation for isolated footing