Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks

Geotechnical investigation plays an indispensable role in site characterization and provides necessary data for various construction projects. However, geotechnical measurements are time-consuming, point-based, and invasive. Non-destructive geophysical measurements (seismic wave velocity) can complement geotechnical measurements to save project money and time. However, correlations between geotechnical and seismic wave velocity are crucial in order to maximize the benefit of geophysical information. In this work, artificial neural networks (ANNs) models are developed to forecast geotechnical parameters from seismic wave velocity. Specifically, published seismic wave velocity, liquid limit, plastic limit, water content, and dry density from field and laboratory measurements are used to develop ANN models. Due to the small number of data, models are developed with and without the validation step in order to use more data for training. The results indicate that the performance of the models is improved by using more data for training. For example, predicting seismic wave velocity using more data for training improves the R2 value from 0.50 to 0.78 and reduces the ASE from 0.0174 to 0.0075, and MARE from 30.75 to 18.53. The benefit of adding velocity as an input parameter for predicting water content and dry density is assessed by comparing models with and without velocity. Models incorporating the velocity information show better predictability in most cases. For example, predicting water content using field data including the velocity improves the R2 from 0.75 to 0.85 and reduces the ASE from 0.0087 to 0.0051, and MARE from 10.68 to 7.78. A comparison indicates that ANN models outperformed multilinear regression models. For example, predicting seismic wave velocity using field plus lab data has an ANN derived R2 value that is 81.39% higher than regression model

Performance Evaluation of Selected Ceramic Companies of Bangladesh

This paper applies performance evaluation of ceramic industry of Bangladesh and to test its financial soundness. The main aim is achieved through ratio analysis of four selected ceramic (Fu Wang, Monno, Shinepukur and Standard) companies in Bangladesh. Measurement of financial performance by ratio analysis helps identify organizational strengths and weaknesses by detecting financial anomalies and focusing attention on issues of organizational importance.  The financial performance of this industry is measured in terms of profitability, solvency, efficiency and liquidity analysis and to test the financial soundness, Multivariate Discriminate Analysis (MDA) is used, which was developed by Prof. Altman. The study covers four public sector ceramic companies listed on Dhaka Stock Exchange. The study has been undertaken for the period of five years from 2006-7 to 2010-2011 and the necessary data has been obtained from the audited annual report of the selected companies. The liquidity position was very weak in all the cases of the selected companies and thereby reflecting the difficulties in paying short-term obligation on due date. Financial stability of the selected companies has shown an upward trend. This study will help investors to identify the nature of financial performance of the ceramic industry of Bangladesh and will also help to take investment decision

Performance Evaluation of Selected Ceramic Companies of Bangladesh

This paper applies performance evaluation of ceramic industry of Bangladesh and to test its financial soundness. The main aim is achieved through ratio analysis of four selected ceramic (Fu Wang, Monno, Shinepukur and Standard) companies in Bangladesh. Measurement of financial performance by ratio analysis helps identify organizational strengths and weaknesses by detecting financial anomalies and focusing attention on issues of organizational importance. The financial performance of this industry is measured in terms of profitability, solvency, efficiency and liquidity analysis and to test the financial soundness, Multivariate Discriminate Analysis (MDA) is used, which was developed by Prof. Altman. The study covers four public sector ceramic companies listed on Dhaka Stock Exchange. The study has been undertaken for the period of five years from 2006-7 to 2010-2011 and the necessary data has been obtained from the audited annual report of the selected companies. The liquidity position was very weak in all the cases of the selected companies and thereby reflecting the difficulties in paying short-term obligation on due date. Financial stability of the selected companies has shown an upward trend. This study will help investors to identify the nature of financial performance of the ceramic industry of Bangladesh and will also help to take investment decision

FORECASTING GEOTECHNICAL PARAMETERS FROM ELECTRICAL RESISTIVITY AND SEISMIC WAVE VELOCITIES USING ARTIFICIAL NEURAL NETWORK MODELS

Geotechnical measurements of soil parameters used in the design of infrastructure provide information at a specific point of the ground. The use of limited point data may result in greater uncertainty and less reliability in design. Geophysical methods are non-invasive, less time-consuming, and provide continuous spatial information about the soil. However, geophysical information is not in terms of engineering parameters. Correlations between geotechnical parameters and geophysical parameters are needed to facilitate the use of geophysical information in geotechnical designs. The current research is focused on two geophysical methods; electrical resistivity (ER) and seismic wave velocity (S-wave and P-wave). Artificial neural network (ANN) models are developed using published data to predict geotechnical parameters from ER and seismic wave velocity. Results of ANN models from the published data show that ER can predict geotechnical parameters with moderate to good accuracy and also predict cation exchange capacity (CEC) better than saturation. Seismic wave velocity helps to predict water content and dry density. Overall, the performance of ANN is better than regression. Laboratory measurements are performed on proctor-compacted soil samples with varying clay, sand, and silt proportions applicable to earthen dam construction. ER, seismic wave velocity and various geotechnical parameters are measured on the same samples. Results show that ER is most sensitive to Atterberg limits, specific surface area, CEC, cohesion, water content and saturation. ANN models are in agreement with the Waxman-Smits formula. In comparison to ER, S-wave and P-wave velocities are more sensitive to dry density and void ratio. Combining ER and S-wave and P-wave velocities predicts water content, dry density, saturation, and void ratio more accurately than simply using individual geophysical parameters. The geophysical parameters in conjunction with the soil mix proportions allow for good to high accuracy predictions of multiple geotechnical parameters

Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks

Geotechnical investigation plays an indispensable role in site characterization and provides necessary data for various construction projects. However, geotechnical measurements are time-consuming, point-based, and invasive. Non-destructive geophysical measurements (seismic wave velocity) can complement geotechnical measurements to save project money and time. However, correlations between geotechnical and seismic wave velocity are crucial in order to maximize the benefit of geophysical information. In this work, artificial neural networks (ANNs) models are developed to forecast geotechnical parameters from seismic wave velocity. Specifically, published seismic wave velocity, liquid limit, plastic limit, water content, and dry density from field and laboratory measurements are used to develop ANN models. Due to the small number of data, models are developed with and without the validation step in order to use more data for training. The results indicate that the performance of the models is improved by using more data for training. For example, predicting seismic wave velocity using more data for training improves the R2 value from 0.50 to 0.78 and reduces the ASE from 0.0174 to 0.0075, and MARE from 30.75 to 18.53. The benefit of adding velocity as an input parameter for predicting water content and dry density is assessed by comparing models with and without velocity. Models incorporating the velocity information show better predictability in most cases. For example, predicting water content using field data including the velocity improves the R2 from 0.75 to 0.85 and reduces the ASE from 0.0087 to 0.0051, and MARE from 10.68 to 7.78. A comparison indicates that ANN models outperformed multilinear regression models. For example, predicting seismic wave velocity using field plus lab data has an ANN derived R2 value that is 81.39% higher than regression model

Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks

Geotechnical investigation plays an indispensable role in site characterization and provides necessary data for various construction projects. However, geotechnical measurements are time-consuming, point-based, and invasive. Non-destructive geophysical measurements (seismic wave velocity) can complement geotechnical measurements to save project money and time. However, correlations between geotechnical and seismic wave velocity are crucial in order to maximize the benefit of geophysical information. In this work, artificial neural networks (ANNs) models are developed to forecast geotechnical parameters from seismic wave velocity. Specifically, published seismic wave velocity, liquid limit, plastic limit, water content, and dry density from field and laboratory measurements are used to develop ANN models. Due to the small number of data, models are developed with and without the validation step in order to use more data for training. The results indicate that the performance of the models is improved by using more data for training. For example, predicting seismic wave velocity using more data for training improves the R2 value from 0.50 to 0.78 and reduces the ASE from 0.0174 to 0.0075, and MARE from 30.75 to 18.53. The benefit of adding velocity as an input parameter for predicting water content and dry density is assessed by comparing models with and without velocity. Models incorporating the velocity information show better predictability in most cases. For example, predicting water content using field data including the velocity improves the R2 from 0.75 to 0.85 and reduces the ASE from 0.0087 to 0.0051, and MARE from 10.68 to 7.78. A comparison indicates that ANN models outperformed multilinear regression models. For example, predicting seismic wave velocity using field plus lab data has an ANN derived R2 value that is 81.39% higher than regression model