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41.
Bharti Jayanti Prabha Mishra Pratishtha moorthy Usha Sathishkumar V. E. Cho Yongyun Samui Pijush 《Geotechnical and Geological Engineering》2021,39(5):3741-3752
Slopes in geotechnical and mining engineering are the most crucial geo-structure. Predicting or forecasting the stability or instability of the slope and then classifying the slope accordingly helps in mitigating the risks and enhancing the design by maximizing the safety. Computing techniques have overpowered the analytical and statistical models used for predicting the stability of the slopes. To reduce the uncertainties and ambiguity of the previously used models, lately, researchers have come up with the novel techniques for Slope Stability Classification (SSC) which are Random Forest, Gradient Boosting Machine, Extreme Gradient Boosting, Boosted Trees and Classification and Regression Trees. These computational algorithms are employed in this research paper and the slope details are taken from a literature i.e. 221 input datasets are used and slopes are classified accordingly using the mentioned models. The relation between the inputs such as height (H), slope angle (β), cohesion (c), pore water pressure ratio (ru), unit weight (γ), angle of internal friction (φ) and slope stability (output) is established and slopes are categorized according to their failure and stability. Performance analysis is done thereafter to analyses and compare different models and let the readers and researchers know that which model sufficed and fitted best to the study.
相似文献42.
Ravinesh C. Deo Pijush Samui Dookie Kim 《Stochastic Environmental Research and Risk Assessment (SERRA)》2016,30(6):1769-1784
The forecasting of evaporative loss (E) is vital for water resource management and understanding of hydrological process for farming practices, ecosystem management and hydrologic engineering. This study has developed three machine learning algorithms, namely the relevance vector machine (RVM), extreme learning machine (ELM) and multivariate adaptive regression spline (MARS) for the prediction of E using five predictor variables, incident solar radiation (S), maximum temperature (T max), minimum temperature (T min), atmospheric vapor pressure (VP) and precipitation (P). The RVM model is based on the Bayesian formulation of a linear model with appropriate prior that results in sparse representations. The ELM model is computationally efficient algorithm based on Single Layer Feedforward Neural Network with hidden neurons that randomly choose input weights and the MARS model is built on flexible regression algorithm that generally divides solution space into intervals of predictor variables and fits splines (basis functions) to each interval. By utilizing random sampling process, the predictor data were partitioned into the training phase (70 % of data) and testing phase (remainder 30 %). The equations for the prediction of monthly E were formulated. The RVM model was devised using the radial basis function, while the ELM model comprised of 5 inputs and 10 hidden neurons and used the radial basis activation function, and the MARS model utilized 15 basis functions. The decomposition of variance among the predictor dataset of the MARS model yielded the largest magnitude of the Generalized Cross Validation statistic (≈0.03) when the T max was used as an input, followed by the relatively lower value (≈0.028, 0.019) for inputs defined by the S and VP. This confirmed that the prediction of E utilized the largest contributions of the predictive features from the T max, verified emphatically by sensitivity analysis test. The model performance statistics yielded correlation coefficients of 0.979 (RVM), 0.977 (ELM) and 0.974 (MARS), Root-Mean-Square-Errors of 9.306, 9.714 and 10.457 and Mean-Absolute-Error of 0.034, 0.035 and 0.038. Despite the small differences in the overall prediction skill, the RVM model appeared to be more accurate in prediction of E. It is therefore advocated that the RVM model can be employed as a promising machine learning tool for the prediction of evaporative loss. 相似文献
43.
R. P. Samui 《Journal of Earth System Science》1999,108(4):309-316
A study was conducted with four traditional photosensitive and one high yielding rice varieties grown during the kharif season
under rainfed conditions. The curvilinear technique was used to examine the influence of meteorological parameters on the
yield of rice. Rice varieties grown in different agroclimatic regions performed differently to climatic parameters. The maximum
yield was observed when rainfall ranged between 100 and 115 cm. Maximum and minimum temperature ranges of 29–32°C and 23–25°C
respectively appear ideal for optimum yield. Photoinsensitive high yielding variety performed well even at low light intensity
(250-350 hours of bright sunshine). 相似文献
44.
Pijush Samui 《地震工程与工程振动(英文版)》2007,6(4):331-336
Determining the liquefaction potential of soil is important in earthquake engineering. This study proposes the use of the Relevance Vector Machine (RVM) to determine the liquefaction potential of soil by using actual cone penetration test (CPT) data. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The results are compared with a widely used artifi cial neural network (ANN) model. Overall, the RVM shows good performance and is proven to be more accurate than the ANN model. It also provides probabilistic output. The model provides a viable tool for earthquake engineers to assess seismic conditions for sites that are susceptible to liquefaction. 相似文献
45.
Pijush Samui 《Geotechnical and Geological Engineering》2013,31(1):249-253
This article presents multivariate adaptive regression spline (MARS) for determination of elastic modulus (Ej) of jointed rock mass. MARS is a technique to estimate general functions of high-dimensional arguments given sparse data. It is a nonlinear and non-parametric regression methodology. The input variables of model are joint frequency (Jn), joint inclination parameter (n), joint roughness parameter (r), confining pressure (σ3) and elastic modulus (Ei) of intact rock. The developed MARS gives an equation for determination of Ej of jointed rock mass. The results from the developed MARS model have been compared with those of artificial neural networks (ANNs) using average absolute error. The developed MARS gives a robust model for determination of Ej of jointed rock mass. 相似文献
46.
Support vector machine applied to settlement of shallow foundations on cohesionless soils 总被引：4，自引：0，他引：4
The determination of settlement of shallow foundations on cohesionless soil is an important task in geotechnical engineering. Available methods for the determination of settlement are not reliable. In this study, the support vector machine (SVM), a novel type of learning algorithm based on statistical theory, has been used to predict the settlement of shallow foundations on cohesionless soil. SVM uses a regression technique by introducing an ε – insensitive loss function. A thorough sensitive analysis has been made to ascertain which parameters are having maximum influence on settlement. The study shows that SVM has the potential to be a useful and practical tool for prediction of settlement of shallow foundation on cohesionless soil. 相似文献
47.
Pijush Pal Roy Chhangte Sawmliana Rakesh Kumar Singh 《Geotechnical and Geological Engineering》2016,34(3):901-909
Directives from the Hon’ble Supreme Court of India led to the banning of mining activities within a radius of 2 km of the Sri Jambunatheswara ancient temple in Hospet taluk of Karnataka State of India. On recommendation of the Department of Archaeology & Museums, Government of Karnataka, CSIR-CIMFR undertook extensive investigations wherein the ground vibration and air overpressure due to blasting in nearby iron ore mines were monitored to assess their damage and annoyance potentials. The magnitudes of blast-induced ground vibration and air-overpressure recorded in the temple were found to be within the standard safe limits stipulated by the Directorate General of Mines Safety, India when trial blasts were carried out at a distance greater than 290 m from the temple. When blasts were conducted at a distance of beyond 845 m from the temple, neither vibration nor sound of blasting could be recorded or heard at the temple premises, indicating it a safe zone for blasting. After thorough analyses of the recorded data, precise blast design parameters were recommended for blasting at distances beyond 200 m from the temple and allowing this distance to be demarcated as the safe zone where controlled blasting could ensure safety of the ancient temple. 相似文献
48.
Two algorithms are outlined, each of which has interesting features for modeling of spatial variability of rock depth. In
this paper, reduced level of rock at Bangalore, India, is arrived from the 652 boreholes data in the area covering 220 sq⋅km.
Support vector machine (SVM) and relevance vector machine (RVM) have been utilized to predict the reduced level of rock in
the subsurface of Bangalore and to study the spatial variability of the rock depth. The support vector machine (SVM) that
is firmly based on the theory of statistical learning theory uses regression technique by introducing ε-insensitive loss function has been adopted. RVM is a probabilistic model similar to the widespread SVM, but where the training
takes place in a Bayesian framework. Prediction results show the ability of learning machine to build accurate models for
spatial variability of rock depth with strong predictive capabilities. The paper also highlights the capability of RVM over
the SVM model. 相似文献
49.
Pijush Pal Roy 《Geotechnical and Geological Engineering》1998,16(2):151-166
The author considers the structural response of various types of surface structures along with their resonance characteristics as well as magnification and attenuation problems. Experimental data from 20 different mines in India, are compiled and analysed to develop simple general equations for use in field conditions. Interpretation of low frequency waves and their active role in enhancing damage probability of structures is also discussed. Particular situations are examined where low and high dominant band frequencies could obtain. It is suggested that a response analysis be undertaken before establishing damage threshold values of any type of structure. © Rapid Science Ltd. 1998 相似文献
50.
The determination of seismic attenuation (s) (dB/cm) is a challenging task in earthquake science. This article employs genetic programming (GP) and minimax probability machine regression (MPMR) for prediction of s. GP is developed based on genetic algorithm. MPMR maximizes the minimum probability of future predictions being within some bound of the true regression function. Porosity (n) (%), permeability (k) (millidarcy), grain size (d) (μm), and clay content (c) (%) have been considered as inputs of GP and MPMR. The output of GP and MPMR is s. The developed GP gives an equation for prediction of s. The results of GP and MPMR have been compared with the artificial neural network. This article gives robust models based on GP and MPMR for prediction of s. 相似文献