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 voids ratio. Multi-layer perceptron training by the backpropagation algorithm was used in creating the network. It was found that both models can predict shear strength parameters for gypseous soils with good reliability. Sensitivity analysis of the first model indicated that dry unit weight and plasticity index have the most significant effect on the predicted cohesion. While in the second model, the results indicated that the gypsum content and plasticity index have the most significant effect on the predicted angle of internal friction.
In this research the relation between skin resistances and standard penetration test of over consolidated
clay soils has been studied. The research includes doing boreholes at Babil governorate in Iraq to get
undisturbed samples and standard penetration test. Determination skin friction from direct shear test between
smooth concrete and soil was explored in laboratory for design purposes and correlated with standard
penetration test values. In many foundation design problems, the shear strength between soil and
foundation materials were estimated or correlated without any direct methods for measurement.
Twelve strain controlled direct shear tests were performed simulate the shear strength interaction
between smooth c
This study is conducted to determine the activity of plant Vica faba and two isolated from arbuscular mycorrhizae fungi (A,B) in bioremediation of soil pollution by Nickel and Lead elements in north and south of Baghdad city. The results showed that the average of soil pollution by Nickel and Lead elements in north of Baghdad was less than the average of soil pollution in the south of Baghdad which recorded 29.0,9.0PPm and 42.0, 25.0PPm respectively. The results show that the isolate A from the polluted soil is more active from isolate B which isolate from unpolluted soil for bioremediation. Vica faba recorded more in accumulate the Lead element in shoot system which was 19.65PPm and in root system was 27.2PPm and for Nickel element 24.65
... Show MoreBacterial strains were isolated from oil-contaminated soil, in 2018, these isolates were identified, and with the aim of finding out the ability of these isolates to degrede the oil compounds, the color change of medium which added to it isolates was read by the method of Pacto Bushnell Hans. Then the change in the petroleum compounds was read by gas chromatography, for the most effective isolates.
The nine isolated bacterial showed different degrees of color change, and the isolates (Pseudomonas, Bacillus, Micrococcus) outperformed the color change amount (78, 78, 77) %, respectively, compared to the control, and the three isolates together showed the best color change of 90.7. % Compared to the control, and the
... Show MoreThis research was conducted to determine content levels of heavy metal pollution. Samples taken from Ishaqi River bank and adjacent agricultural soils area, in ten sites, distributed along 48 km of the Ishaqi River, north Baghdad. The evaluated metals were Zinc, Copper, Manganese, Iron, Cobalt, Nickel, Chromium, Cadmium, Vanadium and Lead. PH and Electric Conductivity (EC) were measured to evaluate the acidity and (EC). Results showed that most site were contaminated with metals evaluated. Among these metals, Zn, Mn, Fe and Ni were consistently higher in all the samples (both river bank and adjacent soil) followed by PB, CU, V, Cd, Co and Cr. The level concentrations of river bank were almost higher than that of adjacent soil. As will be re
... Show MoreThe current study included the isolation, purification and cultivation of blue-green alga Oscillatoria pseudogeminata G.Schmidle from soil using the BG-11liquid culture medium for 60 days of cultivation. The growth constant (k) and generation time (G) were measured which (K=0.144) and (G=2.09 days).
Microcystins were purified and determined qualitatively and quantitatively from this alga by using the technique of enzyme linked immunosorbent assay (Elisa Kits). The alga showed the ability to produce microcystins in concentration reached 1.47 µg/L for each 50 mg DW. Tomato plants (Lycopersicon esculentum) aged two months were irrigated with three concentrations of purified microcystins 0.5 , 3.0 and 6.0
... Show MoreNanopesticides are novel plant protection products offering numerous benefits. Because nanoparticles behave differently from dissolved chemicals, the environmental risks of these materials could differ from conventional pesticides. We used soil–earthworm systems to compare the fate and uptake of analytical‐grade bifenthrin to that of bifenthrin in traditional and nanoencapsulated formulations. Apparent sorption coefficients for bifenthrin were up to 3.8 times lower in the nano treatments than in the non‐nano treatments, whereas dissipation half‐lives of the nano treatments were up to 2 times longer. Earthworms in the nano treatments accumulated approximately 50% more b
In this paper, an algorithm is suggested to train a single layer feedforward neural network to function as a heteroassociative memory. This algorithm enhances the ability of the memory to recall the stored patterns when partially described noisy inputs patterns are presented. The algorithm relies on adapting the standard delta rule by introducing new terms, first order term and second order term to it. Results show that the heteroassociative neural network trained with this algorithm perfectly recalls the desired stored pattern when 1.6% and 3.2% special partially described noisy inputs patterns are presented.
In this article, we design an optimal neural network based on new LM training algorithm. The traditional algorithm of LM required high memory, storage and computational overhead because of it required the updated of Hessian approximations in each iteration. The suggested design implemented to converts the original problem into a minimization problem using feed forward type to solve non-linear 3D - PDEs. Also, optimal design is obtained by computing the parameters of learning with highly precise. Examples are provided to portray the efficiency and applicability of this technique. Comparisons with other designs are also conducted to demonstrate the accuracy of the proposed design.