This work investigates experimentally the effect of using a skirt with a square foundation of 100 mm width resting on dry gypseous soil (i.e., loose soil with 33% relative density), and subjected to an inclined load. Previous works did not study the use square skirted foundation rested on gypseous soil and subjected to inclined load. The investigated soil was brought from Tikrit city with 59% gypsum content. Standard physical and chemical tests on selected soil were carried out. Model laboratory tests were carried out to determine the effect of using a skirt with a square foundation on the load-settlement behavior of gypseous soil and subjected to inclined load with various Skirt depth (Ds) to foundation width (B) ratio

The shear strength of soil is one of the most important soil properties that should be identified before any foundation design. The presence of gypseous soil exacerbates foundation problems. In this research, an approach to forecasting shear strength parameters of gypseous soils based on basic soil properties was created using Artificial Neural Networks. Two models were built to forecast the cohesion and the angle of internal friction. Nine basic soil properties were used as inputs to both models for they were considered to have the most significant impact on soil shear strength, namely: depth, gypsum content, passing sieve no.200, liquid limit, plastic limit, plasticity index, water content, dry unit weight, and initial

ABSTRACT: Ultimate bearing capacity of soft ground reinforced with stone column was recently predicted using various artificial intelligence technologies such as artificial neural network because of all the advantages that they can offer in minimizing time, effort and cost. As well as, most of applied theories or predicted formulas deduced analytically from previous studies were feasible only for a particular testing environment and do not match other field or laboratory datasets. However, the performance of such techniques depends largely on input parameters that really affect the target output and missing of any parameter can lead to inaccurate results and give a false indicator. In the current study, data were collected from previous rel

In the case where a shallow foundation does not satisfy with design requirements alone, the addition of a pile may be suitable to improve the performance of the foundation design. The lack of in-situ data and the complexity of the issues caused by lagging in the research area of pile foundations are notable. In this study, different types of piles were used under the same geometric conditions to determine the load-settlement relationships with various sandy soil relative densities. The ultimate pile capacity for each selected pile is obtained from a modified California Bearing Ratio (CBR) machine to be suitable for axial pile loading. Based on the results, the values of Qu for close-ended square pile were increased by 15

Large quantities of petroleum-contaminated soil are generated with increased global energy consumption and crude oil production. This theoretical study evaluates the treatment of 1 ton of petroleum-contaminated soil using seven methods: incineration, physical washing, chemical washing, thermal pyrolysis, Fenton-oxidation-pyrolysis, the biological treatment, and asphaltenes. Data were based on experimental results from the Nahran Bin Omar oil lake in Basra Governorate, Iraq, (2019–2021). The methods were compared by waste generation, treatment cost, and duration. Results indicate that using petroleum-contaminated soil as a raw material for asphalt manufacturing is most beneficial since it is sold as a raw material. Incineration is faster a

Generally, direct measurement of soil compression index (Cc) is expensive and time-consuming. To save time and effort, indirect methods to obtain Cc may be an inexpensive option. Usually, the indirect methods are based on a correlation between some easier measuring descriptive variables such as liquid limit, soil density, and natural water content. This study used the ANFIS and regression methods to obtain Cc indirectly. To achieve the aim of this investigation, 177 undisturbed samples were collected from the cohesive soil in Sulaymaniyah Governorate in Iraq. Results of this study indicated that ANFIS models over-performed the Regression method in estimating Cc with R² of 0.66 and 0.48 for both ANFIS and Regre

Experiments research is done to determine how saturated stiff clayey soil responds to a single impulsive load. Models made of saturated, stiff clay were investigated. To supply the single pulse energy, various falling weights from various heights were tested using the falling weight deflectometer (FWD). Dynamic effects can range from the major failure of a sensitive sensor or system to the apparent destruction of structures. This study examines the response of saturated stiff clay soil to a single impulsive load (vertical displacement at the soil surface below and beside the bearing plates). Such reactions consist of displacements, velocities, and accelerations caused by the impact occurring at the surface depth induced by the impact loads

In this study, multi-objective optimization of nanofluid aluminum oxide in a mixture of water and ethylene glycol (40:60) is studied. In order to reduce viscosity and increase thermal conductivity of nanofluids, NSGA-II algorithm is used to alter the temperature and volume fraction of nanoparticles. Neural network modeling of experimental data is used to obtain the values of viscosity and thermal conductivity on temperature and volume fraction of nanoparticles. In order to evaluate the optimization objective functions, neural network optimization is connected to NSGA-II algorithm and at any time assessment of the fitness function, the neural network model is called. Finally, Pareto Front and the corresponding optimum points are provided and