1: Magnetic Particles: Preparation, Properties and Applications: M. Ozaki. 2: Maghemite (gamma-Fe2O3): A Versatile Magnetic Colloidal Material C.J. Serna, M.P. Morales. 3: Dynamics of Adsorption and Oxidation of Organic Molecules on Illuminated Titanium Dioxide Particles Immersed in Water M.A. Blesa, R.J. Candal, S.A. Bilmes. 4: Colloidal Aggregation in Two-Dimensions A. Moncho-Jorda, F. Martinez-Lopez, M.A. Cabrerizo-Vilchez, R. Hidalgo Alvarez, M. Quesada-PMerez. 5: Kinetics of Particle and Protein Adsorption Z. Adamczyk.

Surface and Colloid Science

Remediation techniques for heavy metal-contaminated soils: Principles and applicability

Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition

Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN

Water, air and soil pollution

Axial bearing capacity (ABC) of piles is usually determined by static load test (SLT). However, conducting SLT is costly and time-consuming. High strain dynamic pile testing (HSDPT) which is provided by pile driving analyzer (PDA) is a more recent approach for predicting the ABC of piles. In comparison to SLT, PDA test is quick and economical. Implementing feed forward back-propagation artificial neural network (ANN) for solving geotechnical problems has recently gained attention mainly due to its ability in finding complex nonlinear relationships among different parameters. In this study, an ANN-based predictive model for estimating ABC of piles and its distribution is proposed. For network construction purpose, 36 PDA tests were performed on various concrete piles in different project sites. The PDA results, pile geometrical characteristics as well as soil investigation data were used for training the ANN models. Findings indicate the feasibility of ANN in predicting ultimate, shaft and tip bearing resistances of piles. The coefficients of determination, R², equal to 0.941, 0.936, and 0.951 for testing data reveal that the shaft, tip and ultimate bearing capacities of piles predicted by ANN-based model are in close agreement with those of HSDPT. By using sensitivity analysis, it was found that the length and area of the piles are dominant factors in the proposed predictive model.

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Application of artificial neural network for predicting shaft and tip resistances of concrete piles

In Geotechnical Engineering, determining the Unconfined Compressive Strength (UCS) of rock is of prime importance as their role in design and analysis of geotechnical problems is crucial. Although laboratory test is the most direct way for estimating the rock compressive strength but UCS determination in the laboratory would be problematic if the rock masses are weathered mainly because obtaining proper core segments for testing purpose is difficult in this situation. Hence, the use of index testing as an alternative for predicting UCS is investigated by researchers. It is well established that indirect tensile strength or Brazilian Tensile Strength (BTS) is related to UCS. In this paper, in order to develop a new correlation between UCS and BTS, some laboratory tests on dry limestone specimens including 20 Unconfined Compression Tests (UCT) and 20 Brazilian Tests (BT) have been conducted. The paper also reviews some recent correlations between UCS and BTS as well as investigates the reliability of these correlations. Findings show the results of the experimental tests are in good agreement with previous studies. Based on the results, new strong correlation with coefficient of determination of 0.9 is introduced for predicting the UCS of limestone core samples from its BTS. A critical review on the degree of accuracy of recent correlations between UCS and BTS shows that apart from Kahraman`s correlation, other correlations either overestimate or under estimate UCS of rock samples to a significant amount.

Correlation between unconfined compressive strength and indirect tensile strength of limestone rock samples

The purpose of this research is to determine the capability of (and the factors which affect the performance of) an enlarged base pier in resisting uplift capacity. Experiments were conducted in the reinforced bin box of an enlarged base pier with a shaft diameter ranging from 30 to 50 mm, base diameters between 75 and 150 mm and base angles of α = 30°, α = 45° and α = 60°. Tests were conducted in both loose and dense sand packing. A failure mechanism was studied in a glass box for loose and dense sand packing. A dry sand with a unit weight of γ
 d = 14.80 kN/m3 and γ
 d = 17. 0 kN/m3 was achieved for loose and dense packing, respectively. Increasing the bell angle and shaft diameter would result in a decrease of the net uplift capacity and failure displacement. This is due to the reduction in the amount of the sand column above the bell that resists the uplift of the pile. Failure displacements at a constant base diameter generally increased considerably with the increase of the embedment ratio but decreased with the increment of the sand density. It is thus apparent that the shaft diameter, bell diameter and bell angle are geometric factors which, together with the embedment ratio and the sand density, should be taken into account in the design of enlarged base piers.

The uplift load capacity of an enlarged base pier embedded in dry sand

In the recent past years, utilization of intelligent models for solving geotechnical problems has received considerable attention. This paper highlights the feasibility of adaptive neuro-fuzzy inference system (ANFIS) for predicting the bearing capacity of thin-walled foundations. For this reason, a data set comprising nearly 150 recorded cases of footing load tests was compiled from literature. Footing width, wall length-to-footing width ratio, internal friction angle, and unit weight of soil were set as inputs of the predictive model of bearing capacity. In addition, a pre-developed artificial neural network (ANN) model was utilized to estimate the bearing capacity of thin-walled foundations. The results recommend the workability of ANFIS in predicting the bearing capacity of thin-walled foundation. The coefficient of determination (R
 2) results of 0.933 and 0.875, and root mean square error (RMSE) results of 0.075 and 0.048 for training and testing data sets show higher accuracy and efficiency level of ANFIS in estimating bearing capacity of thin-walled spread foundations compared to the ANN model (R
 2 = 0.710, RMSE = 0.512 for train, R
 2 = 0.420, RMSE = 0.529 for test). Overall, findings of the study suggest utilization of ANFIS, as a feasible and quick tool, for predicting the bearing capacity of thin-walled spread foundations, though further study is still recommended to enhance the reliability of the proposed model.

Ramli Nazir

Papers

Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN

Application of artificial neural network for predicting shaft and tip resistances of concrete piles

Correlation between unconfined compressive strength and indirect tensile strength of limestone rock samples

The uplift load capacity of an enlarged base pier embedded in dry sand

Prediction of bearing capacity of thin-walled foundation: a simulation approach