Soil types mapping and the spatial variation of soil classes are essential concerns in both geotechnical and geoenvironmental engineering. Because conventional soil mapping systems are time-consuming and costly, alternative quick and cheap but accurate methods need to be developed. In this paper, a new optimized multi-output generalized feed forward neural network (GFNN) structure using 58 piezocone penetration test points (CPTu) for producing a digital soil types map in the southwest of Sweden is developed. The introduced GFNN architecture is supported by a generalized shunting neuron (GSN) model computing unit to increase the capability of nonlinear boundaries of classified patterns. The comparison conducted between known soil type classification charts, CPTu interpreting procedures, and the outcomes of the GFNN model indicates acceptable accuracy in estimating complex soil types. The results show that the predictability of the GFNN system offers a valuable tool for the purpose of soil type pattern classifications and providing soil profiles.

Landslides as major geo-hazards in Sweden adversely impact on nearby environments and socio-economics. In this paper, a landslide susceptibility map using a proposed subdivision approach for a large area in southwest Sweden has been produced. The map has been generated by means of an artificial neural network (ANN) model developed using fourteen causative factors extracted from topographic and geomorphologic, geological, land use, hydrology and hydrogeology characteristics. The landslide inventory map includes 242 events identified from different validated resources and interpreted aerial photographs. The weights of the causative factors employed were analyzed and verified using accepted mathematical criteria, sensitivity analysis, previous studies, and actual landslides. The high accuracy achieved using the ANN model demonstrates a consistent criterion for future landslide susceptibility zonation. Comparisons with earlier susceptibility assessments in the area show the model to be a cost-effective and potentially vital tool for urban planners in developing cities and municipalities.

Application of artificial neural networks (ANN) in various aspects of geotechnical engineering problems such as site characterization due to have difficulty to solve or interrupt through conventional approaches has demonstrated some degree of success. In the current paper a developed and optimized five layer feed-forward back-propagation neural network with 4-4-4-3-1 topology, network error of 0.00201 and R2 = 0.941 under the conjugate gradient descent ANN training algorithm was introduce to predict the clay sensitivity parameter in a specified area in southwest of Sweden. The close relation of this parameter to occurred landslides in Sweden was the main reason why this study is focused on. For this purpose, the information of 70 piezocone penetration test (CPTu) points was used to model the variations of clay sensitivity and the influences of direct or indirect related parameters to CPTu has been taken into account and discussed in detail. Applied operation process to find the optimized ANN model using various training algorithms as well as different activation functions was the main advantage of this paper. The performance and feasibility of proposed optimized model has been examined and evaluated using various statistical and analytical criteria as well as regression analyses and then compared to in situ field tests and laboratory investigation results. The sensitivity analysis of this study showed that the depth and pore pressure are the two most and cone tip resistance is the least effective factor on prediction of clay sensitivity.

Soil liquefaction as a transformation of granular material from solid to liquid state is a type of ground failure commonly associated with moderate to large earthquakes and refers to the loss of strength in saturated, cohesionless soils due to the build-up of pore water pressures and reduction of the effective stress during dynamic loading. In this paper, assessment and prediction of liquefaction potential of soils subjected to earthquake using two different artificial neural network models based on mechanical and geotechnical related parameters (model A) and earthquake related parameters (model B) have been proposed. In model A the depth, unit weight, SPT-N value, shear wave velocity, soil type and fine contents and in model B the depth, stress reduction factor, cyclic stress ratio, cyclic resistance ratio, pore pressure, total and effective vertical stress were considered as network inputs. Among the numerous tested models, the 6-4-4-2-1 structure correspond to model A and 7-5-4-6-1 for model B due to minimum network root mean square errors were selected as optimized network architecture models in this study. The performance of the network models were controlled approved and evaluated using several statistical criteria, regression analysis as well as detailed comparison with known accepted procedures. The results represented that the model A satisfied almost all the employed criteria and showed better performance than model B. The sensitivity analysis in this study showed that depth, shear wave velocity and SPT-N value for model A and cyclic resistance ratio, cyclic stress ratio and effective vertical stress for model B are the three most effective parameters on liquefaction potential analysis. Moreover, the calculated absolute error for model A represented better performance than model B. The reasonable agreement of network output in comparison with the results from previously accepted methods indicate satisfactory network performance for prediction of liquefaction potential analysis.

Shear wave velocity (VS) is an important geotechnical characteristic for determining dynamic soil properties. When no direct measurements are available, V-S can be estimated based on correlations with common in situ tests, such as the piezocone penetration test (CPTu). In the current paper, three modified equations to predict the V-S of soft clays based on a comprehensive provided CPTu database and related geotechnical parameters for southwest of Sweden were presented. The performance of the obtained relations were examined and investigated by several statistical criteria as well as graph analyses. The best performance was observed by implementing of corrected cone tip resistance (q(t)) and pore pressure ratio (B-q) which directly can be found from CPTu data. The introduced modifications were developed and validated for available soft clays of the studied area in southwest of Sweden, and thus, their applicability for proper prediction in other areas with different characteristics should be controlled. However, the used method as a suitable tool can be employed to investigate.

Site response spectrum is one of the key factors to determine the maximum acceleration and displacement, as well as structure behavior analysis during earthquake vibrations. The main objective of this paper is to develop an optimized model based on artificial neural network (ANN) using five different training algorithms to predict nonlinear site response spectrum subjected to Silakhor earthquake vibrations is. The model output was tested for a specified area in west of Iran. The performance and quality of optimized model under all training algorithms have been examined by various statistical, analytical and graph analyses criteria as well as a comparison with numerical methods. The observed adaptabilities in results indicate a feasible and satisfactory engineering alternative method for predicting the analysis of nonlinear site response.

Although there are many proposed relations for different rock types to predict the uniaxial compressive strength (UCS) as a function of P-wave velocity (V-P) and point load index (Is), only a few of them are focused on marlstones. However, these studies have limitations in applicability since they are mainly based on local studies. In this paper, an attempt is therefore made to present updated relations for two previous proposed correlations for marlstones in Iran. The modification process is executed through multivariate regression analysis techniques using a provided comprehensive database for marlstones in Iran, including UCS, V-P and Is from publications and validated relevant sources comprising 119 datasets. The accuracy, appropriateness and applicability of the obtained modifications were tested by means of different statistical criteria and graph analyses. The conducted comparison between updated and previous proposed relations highlighted better applicability in the prediction of UCS using the updated correlations introduced in this study. However, the derived updated predictive models are dependent on rock types and test conditions, as they are in this study.

The correlation between Standard and Cone Penetration Tests (SPT and CPT) as two of the most used in-situ geotechnical tests is of practical interest in engineering designs. In this paper, new SPT-CPT correlations for southwest of Sweden are proposed and developed using an artificial neural networks (ANNs) approach. The influences of soil type, depth, cone tip resistance, sleeve friction, friction ratio and porewater pressure on obtained correlations has been taken into account in optimized ANN models to represent more comprehensive and accurate correlation functions. Moreover, the effect of particle mean grain size and fine content were investigated and discussed using graph analyses. The validation of ANN based correlations were tested using several statistical criteria and then compared to existing correlations in literature to quantify the uncertainty of the correlations. Using the sensitivity analyses, the most and least effective factors on CPT-SPT predictions were recognized and discussed. The results indicate the ability of ANN as an attractive alternative method regarding to conventional statistical analyses to develop CPT-SPT relations.