Int J Performability Eng ›› 2021, Vol. 17 ›› Issue (7): 647656.doi: 10.23940/ijpe.21.07.p9.647656
Rahul Ray^{*}, Shiva Shankar Choudhary, and Lal Bahadur Roy
Contact:
* Email address: rahulr.phd19.ce@nitp.ac.in
Rahul Ray, Shiva Shankar Choudhary, and Lal Bahadur Roy. Reliability Analysis of Layered Soil Slope Stability using ANFIS and MARS Soft Computing Techniques [J]. Int J Performability Eng, 2021, 17(7): 647656.
Add to citation manager EndNoteReference ManagerProCiteBibTeXRefWorks
1. Phoon K.K.,Potential application of reliabilitybased design to geotechnical engineering. In: In Proceedings of 4th Colombian Geotechnical Seminar, Medellin. pp. 122, November 2018. 2. Christian J.T., Ladd C.C. and Baecher G.B.,Reliability applied to slope stability analysis. 3. Liang R., Nusier O. and Malkawi A., A reliability based approach for evaluating the slope stability of embankment dams. 4. Cheng Y.,Location of critical failure surface and some further studies on slope stability analysis. 5. Sivakumar Babu,G.L. and Srivastava, A., Reliability Analysis of Earth Dams. 6. Reale C., Xue J., andPan Z., Deterministic and probabilistic multimodal analysis of slope stability. 7. Zeroual A., Fourar A., andDjeddou M., Predictive modeling of static and seismic stability of small homogeneous earth dams using artificial neural network. 8. Kumar R., Samui P., andKumari S., Reliability Analysis of Infinite Slope Using Metamodels. 9. Karimi I.,Application of NeuroFuzzy systems in estimating the response of sedimentfilled valleys.10th Int. Fuzzy System Assoc. Congress, 2003. 10. Roger Jang,J.S., ANFIS?: Adap tiveNe tworkBased Fuzzy Inference System., 1993. 11. Zadeh L.A.,Fuzzy sets. Inf. Control. 8, pp. 338353, June 1965. 12. Zadeh L.A.,Outline of a New Approach to the Analysis of Complex Systems and Decision Processes. 13. P. Werbos., Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences.Ph.D. Diss. Harvard Univ. Cambridge, 1974. 14. Abraham A., andSteinberg D., Is neural network a reliable forecaster on earth? A MARS query! In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp. 679686. Springer Verlag, 2001. 15. Friedman J.,Multivariate adaptive regression splines. JSTOR. 19, pp. 167, 1991. 16. Sharda V.N., Prasher S.O., Patel R.M., Ojasvi P.R., andPrakash C., Performance of multivariate adaptive regression splines (MARS) in predicting runoff in midHimalayan microwatersheds with limited data. 17. Sephton P.,Forecasting recessions: Can we do better on mars. 18. Adamowski J., Chan H.F., Prasher S.O., OzgaZielinski, B., and Sliusarieva, A., Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada. 19. Craven P., andWahba G.,Smoothing noisy data with spline functions  Estimating the correct degree of smoothing by the method of generalized crossvalidation. 20. Griffiths D. V., Huang J., andFenton G.A., Risk Assessment in Geotechnical Engineering: Stability Analysis of Highly Variable Soils. In: GeoCongress 2012, 2012. 21. Jain S.K., andSudheer K.P., Fitting of Hydrologic Models: A Close Look at the NashSutcliffe Index. 22. Kisi O., Shiri J., andTombul M.,Modeling rainfallrunoff process using soft computing techniques. 23. Alvarez Grima,M., and Babuška, R., Fuzzy model for the prediction of unconfined compressive strength of rock samples. 24. Gokceoglu C.,A fuzzy triangular chart to predict the uniaxial compressive strength of Ankara agglomerates from their petrographic composition. 25. Yılmaz I., andYuksek A.G., An Example of Artificial Neural Network (ANN) Application for Indirect Estimation of Rock Parameters. 26. Sivakumar Babu, G.L., and Srivastava, A., Reliability analysis of allowable pressure on shallow foundation using response surface method. 27. Kung G.T., Juang C.H., Hsiao and E.C., Hashash, Y.M., Simplified Model for Wall Deflection and GroundSurface Settlement Caused by Braced Excavation in Clays. 28. Prasomphan S.,Machine and S.M., Generating prediction map for geostatistical data based on an adaptive neural network using only nearest neighbors. 29. D. N. Moriasi, J. G. Arnold, M. W.Van Liew, R. L. Bingner, R. D. Harmel and T. L. Veith, Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. 30. Srinivasulu, S. and Jain, A., A comparative analysis of training methods for artificial neural network rainfallrunoff models. 31. Armstrong, J.S. and Collopy, F., Error Measures for Generalizing About Forecasting Methods: Empirical Comparisons. 32. Ray R., Kumar D., Samui P., Roy L.B., Goh A.T.C. and Zhang, W., Application of soft computing techniques for shallow foundation reliability in geotechnical engineering. 33. Willmott C.J.,On the Validation of Models. 34. Willmott C.J.,Some Comments on the Evaluation of Model Performance. Bull. 35. Willmott C.J.,On the Evaluation of Model Performance in Physical Geography. In: Spatial Statistics and Models, pp. 443460. Springer Netherlands, Dordrecht, 1984. 36. RaventosDuran, T., Camredon, M., Valorso, R., MouchelVallon, C. and Aumont, B., Structureactivity relationships to estimate the effective Henry's law constants of organics of atmospheric interest. Atmos. Chem. Phys., 10, pp. 76437654, August 2010. 37. Legates D.R. andMcCabe, G.J., Evaluating the use of “goodnessoffit” Measures in hydrologic and hydroclimatic model validation. 38. Legates, D.R. and McCabe, G.J., A refined index of model performance: a rejoinder. 39. Gueymard C.,A review of validation methodologies and statistical performance indicators for modeled solar radiation data: Towards a better bankability of solar projects. 40. Behar O., Khellaf A., Mohammedi K., Comparison of solar radiation models and their validation under Algerian climate  The case of direct irradiance. 41. Stone R.J.,Improved statistical procedure for the evaluation of solar radiation estimation models. 42. Viscarra Rossel, R.A., McGlynn, R.N. and McBratney, A.B., Determining the composition of mineralorganic mixes using UVvisNIR diffuse reflectance spectroscopy. Geoderma, 137, pp. 7082, December 2006. 43. USACE, Riskbased analysis in geotechnical engineering for support of planning studies, engineering and design.Dept. Army, USACE Washington, DC., 1997. 44. Taylor K.E.,Summarizing multiple aspects of model performance in a single diagram. 45. Fawcett T.,An introduction to ROC analysis. 46. Mann, H.B. and Whitney, D.R., On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other. 47. Anderson, T.W. and Darling, D.A., Asymptotic Theory of Certain "Goodness of Fit" Criteria Based on Stochastic Processes. 
[1]  Surbhi Gupta, H.D. Aroraa, Anjali Naithania, Anil Chandrab. Reliability Assessment of the Planning and Perception Software Competencies of SelfDriving Cars [J]. Int J Performability Eng, 2021, 17(9): 779786. 
[2]  Tyler D. Ridder and Ram M. Narayanan. Radar Detection Performability under Graceful Degradation [J]. Int J Performability Eng, 2021, 17(8): 666675. 
[3]  YiChun Cheng, YiKuei Lin, and PingChen Chang. Reliability Evaluation for a Multistate Network with Time Attribute and Periodical Maintenance [J]. Int J Performability Eng, 2021, 17(8): 676685. 
[4]  YiFan Chen, YiKuei Lin, and ChengFu Huang. Using Deep Neural Networks to Evaluate the System Reliability of Manufacturing Networks [J]. Int J Performability Eng, 2021, 17(7): 600608. 
[5]  Youssef Bassir, Achraf Wahid, Abdelkarim Kartouni, and Mohamed ELghorba. Estimation of Wire Rope Reliability by Two Analytical Approach [J]. Int J Performability Eng, 2021, 17(7): 619626. 
[6]  YiHao Chiu, YiKuei Lin, and ThiPhuong Nguyen. Network Reliability of a Stochastic OnlineFood Delivery System with Space and Time Constraints [J]. Int J Performability Eng, 2021, 17(5): 433443. 
[7]  Saurav Shekhar Kar and Lal Bahadur Roy. Reliability Analysis of a Finite Slope Considering the Effects of Soil Uncertainty [J]. Int J Performability Eng, 2021, 17(5): 473483. 
[8]  Redondin Maxime, Bouillaut Laurent, and Daucher Dimitri. EM Approach for Weibull Analysis in a Strongly Censored Data Context  Application to Road Markings [J]. Int J Performability Eng, 2021, 17(4): 333342. 
[9]  Yang TaeJin. Comparative Study on the Performance Attributes of NHPP Software Reliability Model based on Weibull Family Distribution [J]. Int J Performability Eng, 2021, 17(4): 343353. 
[10]  Tyler D. Ridder and Ram M. Narayanan. Operational Reliability Metric to Characterize Radar Detection Performability [J]. Int J Performability Eng, 2021, 17(4): 354363. 
[11]  Bouzouada Abdallah, Yssaad Benyssaad, Daoud Mohamed, Bekkouche Benaissa, and Yagoubi Benabdellah. Maintenance Optimization for Complex System using Evolutionary Algorithms under Reliability Constraints within the Context of the ReliabilityCenteredMaintenance [J]. Int J Performability Eng, 2021, 17(1): 113. 
[12]  Priti Kumari and Parmeet Kaur. Connected Data Setbased Virtual Machine Replication in Cloud Computing [J]. Int J Performability Eng, 2020, 16(9): 13511361. 
[13]  Achraf Wahid, Youssef Bassir, Nadia Mouhib, Hamid Chakir, and Mohamed Elghorba. Analytical Approach for Damage Reliability Assessment of Wire Rope [J]. Int J Performability Eng, 2020, 16(8): 11511158. 
[14]  Pan Liu and Wulan Huang. Incremental Data Miningbased Software Failure Detection [J]. Int J Performability Eng, 2020, 16(8): 12791288. 
[15]  Murgayya S B, Suresh H N, Madhusudhan N, and Saravanabavan D. Optimization of High Speed RotorBearings System to Assess the Reliability using XLrotor [J]. Int J Performability Eng, 2020, 16(7): 991998. 
