Unconfined compressive strength (UCS) can be used to assess the applicability of geopolymer binders as ecologically friendly materials for geotechnical projects. Furthermore, soft computing technologies are necessary since experimental research is often challenging, expensive, and time-consuming. This article discusses the feasibility and the performance required to predict UCS using a Random Forest (RF) algorithm. The alkali activator studied was sodium hydroxide solution, and the considered geopolymer source material was ground-granulated blast-furnace slag and fly ash. A database with 283 clayey soil samples stabilized with geopolymer was considered to determine the UCS. The database was split into two sections for the development of the RF model: the training data set (80%) and the testing data set (20%). Several measures, including coefficient of determination (R), mean absolute error (MAE), and root mean square error (RMSE), were used to assess the effectiveness of the RF model. The statistical findings of this study demonstrated that the RF is a reliable model for predicting the UCS value of geopolymer-stabilized clayey soil. Furthermore, based on the obtained values of RMSE = 0.9815 and R2 = 0.9757 for the testing set, respectively, the RF approach showed to provide excellent results for predicting unknown data within the ranges of examined parameters. Finally, the SHapley Additive exPlanations (SHAP) analysis was implemented to identify the most influential inputs and to quantify their behavior of input variables on the UCS.

Progress in construction and buildings industry depends on so many parameters especially materials used. Concrete materials are cheap but still need minimization in their costs. Steel is one of the materials used in concrete structural members to support the concrete as reinforcement. In this study a polypropylene rope (PP ropes) were used to support the task of steel reinforcement especially in tension zones and to decrease the total cost of the concrete section. To know how this matter achieved seven concrete beams were casted one of them was without polypropylene ropes as control beam where as others were reinforced only with polypropylene ropes. The dimensions of the concrete beams were (200cm ×30cm ×20cm). Whole concrete tests were done to find out the most effective properties of concrete like compression, rupture modulus and tensile strength. Seven beams were exposed to monotonic load to find out the load at failure and corresponding deflection at mid span. Results show that if four ropes were used in tension zone of the concrete section the strength increased by about 9%. This ratio seems to be low but the cheap cost of these ropes encourages designers to use a greater number of ropes in concrete section. This idea needs more future work.

Expansive soils are the an un constant volume soil, because, they distend or swell when wetting & shrink when they are dried, therefore they affect the stability of the structures that are rested on it, such as light weight buildings & pavements and, then, damages appear because of the developments of heave and swelling which depend, mainly, on the clay content (less than 0.002mm particles size) which the soil has. The problem discussed her on an expansive soil area at the Iraqi west desert that the new [Wild Hajj] road passes over it, named AL-Bussita area. Stability tests such as maximum dry density, CBR, unconfined and triaxial shear test besides other tests such as swelling tests, (one dimensional oedometer tests), were performed on soil shales taken from this area. The study was continued with a sample from a same soil mixed with the fuel oil. The simulation and experimental results demonstrate that the (8% by weight) was the optimum percent of the fuel oil to minimize the swelling potential, and to get a good engineering soil property and the mixed soil became acceptable to use in earth works of roads.

Stone columns have been extensively advocated as a traditional approach to increase the undrained bearing capacity and reduce the settlement of footings sitting on cohesive ground. However, due to the complex interaction between the soil and the stone columns, there currently needs to be a commonly acknowledged approach that can be used to precisely predict the undrained bearing capacity of the system. For this reason, the bearing capacity of a sandy bed reinforced with geogrid and sitting above a collection of geogrid-encased stone columns floating in soft clay was studied in this research. Using a white-box machine learning (ML) technique called Multivariate Polynomial Regression (MPR), this work aims to develop a model for predicting the bearing capacity of the referred foundation system. For this purpose, two hundred and forty-five experimental results were collected from the literature. In addition, the model was compared to two other ML models, namely, a black-box model known as Random Forest (RF) and a white-box ML model called Linear Regression (LR). In terms of R2 (coefficient of determination) and RMSE (Root Mean Absolute Error) values, the newly proposed model outperforms the two other referred models and demonstrates robust estimation capabilities. In addition, a parametric analysis was carried out to determine the contribution of each input variable and its relative significance on the output.