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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.

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In this paper, silica fume and fly ash class F was used as a supplementary cementing material in cement mortars to evaluate several mechanical properties of SIFCON (Slurry Infiltrated Fibrous concrete) in the way of flexural, compressive and splitting strength which were determined using the standard test method, the impact load resistance was determined using a fabricated instrument described in body paper. plate specimens with dimension of 450mm×100mm×40mm were cast with micro steel fiber of 6% volume fraction were tested under impact load for assessment with references plates at 90 days of age. From the results of the outcome it was found that the performance of conventional mortar is much less than SIFCON specimens. The compressive, splitting, flexural strength and impact resistance for conventional mortar and SIFCON specimens were investigated, a significant development in impact resistance for SIFCON mix at first crack and failure, also the energy demand for the whole failure was which is increased by 9.97 times compared with the conventional mortar. Thus, this present paper displays that SIFCON can be utilized as a powerful option in different development purposes or where the concrete or natural steel fiber reinforce concrete can't execute surprisingly/needed or in situations where such greater strength is imperative and essential.

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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 more number of ropes in concrete section. This idea needs more future work.

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The current study concentrated on the groundwater chemistry in part of Najaf city area to evaluate the groundwater quality for knowing its relevance for agricultural purposes. The quality of groundwater significantly affected by Urbanization and agriculture activities. The (10) samples of groundwater were randomly raised from various wells Najaf farms region by pumps, then resolved for main anions. Anions dominant were Na, Ca and Mg, thence get a criterion index of water quality the ratio of sodium adsorption (SAR) in addition to test electrical conductivity (E.C.). Then, examine those samples of groundwater where used as an irrigation water supplied to several types from of Oryza satival seeds (Yassmin class, Dijla class, Amber 33 class, Alforat class, Amber Baraka class, Mashkhab class) to test germination of seeds. The results of germination of seeds with those samples of irrigation water was between (40%- 100%).

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The prediction of dry density play important role in projects or major or laboratory tests which required to estimates the value of dry density for cohesion less soils to decision of the solution of these soils or conducting tests. In this paper, an attempt utilized to predict the value cohesion less soil form known some physical and chemical properties of soil such as technical institute Laboratory which conducted for ninety nine cohesion less soil samples. These tests may classify aseasier, cheaper and low time consume when compared with laboratory dry density tests. The results show many correlation models depending on the independent variables constricted to estimate the dry density of soil. The highest coefficient of determination resulted from this study is 0.92 for multiple regression analysis. In this case nine soil property correlated to gathers to estimate the dry density. This value may decrease when the independent variable are decrease than nine soil property.

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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 R 2 = 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.

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Abstract: 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 R (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. 2