Application of artificial intelligence in geotechnical engineering: A state-of-the-art review

The prediction of the potential of soil liquefaction induced by the earthquake is a vital task in construction engineering and geotechnical engineering. To provide a possible solution to such problems, this paper proposes two support vector machine (SVM) models which are optimized by genetic algorithm (GA) and grey wolf optimizer (GWO) to predict the potential of soil liquefaction. Field observation data based on cone penetration test (CPT), standard penetration test (SPT) and shear wave velocity (VS) test (SWVT) are employed to verify the reliability of the GA–SVM model and the GWO–SVM model, the numbers of input variables of these three field testing data sets are 6, 12 and 8, respectively, and the output result is the potential of soil liquefaction. To verify whether the two optimization algorithms GA and GWO have significantly improved the performance of SVM model, an unoptimized SVM model is served as a reference in this study. And five performance metrics, including classification accuracy rate (ACC), precision rate (PRE), recall rate (REC), F1 score (F1) and AUC are used to evaluate the classification performance of the three models. Results of the study confirm that when CPT-based, SPT-based and SWVT-based test sets are input into three classification models, the highest classification accuracy of 0.9825, 0.9032 and 0.9231, respectively, is achieved with GWO–SVM. And based on these three data sets, the values of AUC obtained by GWO–SVM are all higher than those obtained by GA–SVM. Further, by comparing the other metrics of the three classification models, it is found that the classification performance of the two hybrid models is very similar and significantly better than the SVM, which indicates that GWO–SVM, like GA–SVM, can also be used as a reliable model for predicting soil liquefaction potential.

Performance evaluation of hybrid GA–SVM and GWO–SVM models to predict earthquake-induced liquefaction potential of soil: a multi-dataset investigation

Introduction: Poor solubility and dissolution of drugs are the major challenges in drug formulation and delivery. In order to improve the solubility and dissolution profile of drugs, various methods have been investigated so far. The cyclodextrin (CD) complexation and phospholipid (PL) complexation are among the exhaustively investigated methods employed for more precise improvement of the solubility and dissolution of poorly water-soluble drugs.Areas covered: The article discusses the CD and PL complexation techniques of solubility and dissolution enhancement. Various studies reporting the CD and PL complexation as the potential approaches to improve the dissolution, absorption and the bioavailability of the drugs have been discussed. The article critically reviews the physicochemical properties of CDs and PLs, eligibility of drugs for both the complexation, thermodynamics of complexation, methods of preparation, characterization, advantages, limitation and the meta-analysis of some studies for both the ...

Cyclodextrin and phospholipid complexation in solubility and dissolution enhancement: a critical and meta-analysis.

This study deals with the development of an artificial neural network (ANN) and a multiple regression (MR) model that can be employed for estimating the California bearing ratio (CBR) value of some Aegean sands. To achieve this, the results of CBR tests performed on the compacted specimens of nine different Aegean sands with varying soil properties were used in the development of the ANN and MR models. The results of the ANN and MR models were compared with those obtained from the experiments. It is found that the CBR values predicted from the ANN model matched the experimental values much better than the MR model. Moreover, several performance indices, such as coefficient of determination, root-mean-square error, mean absolute error, and variance, were used to evaluate the prediction performance of the ANN and MR models. The ANN model has shown higher prediction performance than the MR model based on the performance indices, which demonstrates the usefulness and efficiency of the ANN model. Thus, the ANN model can be used to predict CBR value of the Aegean sands included in this study as an inexpensive substitute for the laboratory testing, quite easily and efficiently.

Use of neural networks for the prediction of the CBR value of some Aegean sands

Novel pimozide-β-cyclodextrin-polyvinylpyrrolidone inclusion complexes for Tourette syndrome treatment

In this study, standard penetration test dependent bore-log charts of different boreholes were collected for selected locations in order to prepare the datasets. Datasets were applied to the Idriss and Boulanger method to evaluate liquefaction potential. Complete datasets were used for development of neural network and neuro-fuzzy models. Feed forward backpropagation algorithm with a multilayer perceptron network is utilized to analyze the liquefaction occurrence in different locations. To meet the objective, 159 sets of geotechnical data were collected, out of which 133 datasets were used for development of models and 26 datasets were used for validation. Neural network models were trained with six input vectors by optimum numbers of hidden layers, epoch, and suitable transfer functions. Neuro-fuzzy models have been developed using the Takagi–Sugeno–Kang reliant approach. The predicted values of liquefaction potential by artificial neural networks and neuro-fuzzy models were compared with an empirical method i.e., Idriss and Boulanger method. The compared values of liquefaction potential obtained by neural network and neuro-fuzzy models shows that trained artificial neural network models' prediction capability are better than that of neuro-fuzzy models.

Appraisal of liquefaction potential using neural network and neuro fuzzy approach

This study refers to the prediction of liquefaction potential of alluvial soil by artificial neural network models. To meet the objective 160 data sets from field and laboratory tests were collected for the development of ANN models. Initially these data sets were used to determine liquefaction parameters like cyclic resistance ratio and cyclic stress ratio by Idriss and Boulanger method to identify the liquefaction prone areas. Artificial neural network models were trained with six input vectors by optimum numbers of hidden layers, epoch and suitable transfer functions. Out of 160 data sets, 133 data sets were used for development of models and 27 datasets were used for validating the models. The predicted values of liquefaction potential by artificial neural networks models have been compared with Idriss and Boulanger method, which exhibits that trained artificial neural networks models are capable of predicting soils liquefaction potential adequately.

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Application of ANN to Predict Liquefaction Potential

Enhancement of Complexation Efficiency of Meloxicam Using Binary and Ternary Solid Systems: Formulation Considerations

Damages due to earthquakes in the 21st century have attracted many researchers and engineers on the seismic safety of densely populated cities. One of the fastest growing city Allahabad in Uttar Pradesh (India) is situated on the bank of river Ganga and Yamuna. Most part of river Ganga carries alluvial soil which is an important parameter that influences liquefaction potential of soil. Several factors that also can affect liquefaction behavior of soil that are local site condition, water table location etc. Present research work motivates on liquefaction potential of soil calculated by semi-empirical methods (like Modified Seed’s method, Idriss and Boulanger (I&B) method & Tokimatsu and Yoshimi (T&Y) method). The calculated liquefaction value had been used as output/target value for developing the soft computing models. Artificial Neural Network (ANN) & Adaptive Neuro Fuzzy Inference System (ANFIS) techniques have been adapted to the development of soft computing models. I&B method gives more conservative results than other one semi empirical methods. Article DOI: https://dx.doi.org/10.20319/mijst.2017.32.316327 This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

Soil liquefaction and its evaluation based on spt by soft-computing techniques

Weak soil is very problematic soil, and it has shrinking and swelling properties which can damage the structure constructed over it. Waste material on the daily basis is likely to become a problem for disposal. It creates environmental contamination and health risks. Hence, the utilization of waste material in the stabilization of weak soil effectively minimizes the negative effect on the environment. In this paper, the aim is to stabilize the weak soil using combinations of waste material. The waste materials used for the study are stone dust and solid waste from silica sand beneficiation plant. Stone dust is coming from polishing, cutting of stones, and cruising process during rock quarrying activities. Solid waste from silica sand beneficiation plant is a granular material, and it contains quartz and very less amount of clay, coal, and other minerals. Stone dust and solid waste from silica sand beneficiation plant are combined in different proportion with weak soil. Geotechnical properties of weak soil individually and in combination with varying proportion were investigated. The standard Proctor test and the California bearing ratio test have been performed. The results of these tests resemble that the combination of stone dust and solid waste from silica sand beneficiation plant is very effective for stabilizing the weak soil.

Soil Stabilization Using Combined Waste Material

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