Gene expression programming (GEP) is used in this research to develop an empirical model that predicts the bond strength between the concrete surface and carbon fiber reinforced polymer (CFRP) sheets under direct pull out. Therefore, a large and reliable database containing 770 test specimens is collected from the literature. The gene expression programming model is developed using eight parameters that predominantly control the bond strength. These parameters are concrete compressive strength, maximum aggregate size, fiber reinforced polymer (FRP) tensile strength, FRP thickness, FRP modulus of elasticity, adhesive tensile strength, FRP length, and FRP width. The model is validated using the experimental results and a statistical assessment is implemented to evaluate the performance of the proposed GEP model. Furthermore, the predicted bond results, obtained using the GEP model, are compared to the results obtained from several analytical models available in the literature and a parametric study is conducted to further ensure the consistency of the model by checking the trends between the input parameters and the predicted bond strength. The proposed model can reasonably predict the bond strength that is most fitting to the experimental database compared to the analytical models and the trends of the GEP model are in agreement with the overall trends of the analytical models and experimental tests.

The unconfined compressive strength (UCS) of soils is essential for both researchers and practitioner geotechnical engineers. UCS is well-understood and standardized for laboratory and field tests. Nevertheless, the large number of environmental and physical governing factors makes the reasonable prediction of UCS complicated. In this paper, a deep learning approach using the multilayer perceptron regressor (MLP) method along with the genetic expression programming (GEP) are used to assess nine variables that contribute to form a reflective multivariate formulation of the UCS. These variables include clay mineral percent (CF), specific gravity ($Gs$ ), dry unit weight (${\gamma }_d$ ), saturated unit weight (${\gamma }_{\mathrm{sat}}$ ), natural unit weight (${\gamma }_t$ ), moisture content (MC), void ratio ($e$ ), degree of saturation ($S$ ), and porosity ($n$ ). MLP and GEP are implemented to classify, correlate, rank, and reduce the number of variables that govern the UCS through the application of classification algorithms, importance analysis, interrelations and interdependency analysis of the variables, and functional indicators that shape the UCS. The changes of UCS in line with the variations of void ratio are analytically formulated according to both the critical state soil mechanics and the inverse proportionality between the voided area and soil strength. Moreover, the validity of the UCS principle is examined as opposed to the variation of the clay percent in the soil. Findings show that there shall be a breakpoint (clay percentage) after which the concept of UCS is radically changed due to the presence of a significant amount of frictional and drainable materials in the soil. The breakpoint appears to be centralized between 40% and 55%. The study is concluded by identifying the fundamental soil parameters, providing practical models to evaluate UCS, developing fundamental relationships between UCS and void ratio, and defining the breakpoint of clay content.