A new design equation for prediction of ultimate bearing capacity of shallow foundation on granular soils

A major concern in design of structures is to provide precise estimations of ultimate bearing capacity of soil beneath their foundations. Direct determination of the bearing capacity of foundations requires performing expensive and time consuming laboratory tests. To cope with this issue, several numerical models have been presented by researchers. This paper presents the development of a new design equation for the prediction of the ultimate bearing capacity of shallow foundations on granular soils using linear genetic programming (LGP) methodology. The ultimate bearing capacity is formulated in terms of width of footing, footing geometry, depth of footing, unit weight of sand, and angle of shearing resistance. The LGP-based design equation is established using the results of several load tests on real sized foundations presented in the literature. Validity of the model is verified using a part of laboratory data that are not involved in the calibration process. The statistical measures of coefficient of determination, root mean squared error and mean absolute error are used to evaluate the performance of the model. Sensitivity and parametric analyses are conducted and discussed. The proposed model accurately characterizes the ultimate bearing capacity resulting in a very good prediction performance. The LGP model reaches a better prediction performance than the well-known prediction equations for the bearing capacity of shallow foundations

An engineered ML model for prediction of the compressive strength of Eco-SCC based on type and proportions of materials

Recently, various waste materials and industrial by-products such as supplementary cementitious materials (SCMs) have been proposed to improve the properties of self-compacting concrete (SCC). This profitable waste management strategy results in lowering the costs and carbon emission, and a more sustainable, cleaner and eco-friendly production of SCC (Eco-SCC). The properties of such a complex material are commonly measured through costly experiments. Researchers also proposed experimental data analysis and predictive modeling methods such as machine learning (ML) algorithms for prediction of the properties of concrete. However, proposed models commonly relate the properties to the proportion of constituents only and ignore the effect of their type and properties, and other influential factors. This paper aims to engineer the concept and develop a more efficient ML model for prediction of the 28-day uniaxial compressive strength (UCS28d) of SCC containing SCMs. A comprehensive dataset is collected through a precise literature survey. Some dimensionless ratios are proposed to reduce the dimensionality of variables and reflect the effects of considered influential factors in different ML models. Two separate datasets are considered to test the predictability of models where one has new proportions of materials only and the other contains new type of material with new properties. After validation and comparison between various ML models, Gaussian process regression (GPR) model proved to perform well on both considered Test datasets with R2, RMSE and MAE of around 0.96, 3.66 and 2.49 respectively. Sensitivity analysis results confirm the contribution and importance of considering type and properties of materials as model variables. This paper demonstrates and highlights that all influential factors must be considered to develop engineered ML models to use as universal tools for indirect estimation of properties of composite materials such as Eco-SCC

Development of ECO-UHPC utilizing gold mine tailings as quartz sand alternative

To ensure ultra-high performance, in terms strength and durability, coarse aggregate is typically avoided in UHPC (ultra-high performance concrete). Instead, very fine quartz sand is usually used as the only aggregate. However, excessive extraction of sand from natural resources and its grinding and refining processes to prepare very fine quartz-rich sand are not economically or environmentally lucrative. Limited number of studies sought to address this concern, and very few of these studies investigated the use of mine tailings (quartz based tailings and iron ore tailings) in UHPC as sand alternatives. In the present study, the possibility of utilizing gold mine tailings, sourced from a gold mine in Western Australia (WA), as conventional quartz sand substitute in UHPC has been investigated. Results suggest that UHPCs, made with up to 80% replacement of quartz sand by the tailings, exhibit compressive strengths comparable to or higher than that of the UHPC with 100% quartz sand. 28-day strength greater than 120 MPa is achievable up to 100% replacement. The water absorptions and the initial rate of absorptions of UHPCs with tailings are generally lower than those of the UHPC without tailings. The leachability of toxic metals from UHPC with up to 100% tailings content lies well below the regulatory thresholds. The combined material and transportation cost of UHPC can be reduced by up to 33.1% replacing quartz sand by the tailings, for construction near the mine site. The CO2 emission can be reduced by up to 12.1%. In the area near the mine site, utilization of the tailings can economically and environmentally, as well as in terms of durability, be a better option than quartz sand for construction works that require UHPC with 28-strength in excess of 120 MPa