Evaluation of toppling rock slopes using a composite cloud model with DEMATEL–CRITIC method

Safety evaluation of toppling rock slopes developing in reservoir areas is crucial. To reduce the uncertainty of safety evaluation, this study developed a composite cloud model, which improved the combination weights of the decision-making trial and evaluation laboratory (DEMATEL) and criteria importance through intercriteria correlation (CRITIC) methods. A safety evaluation system was developed according to in situ monitoring data. The backward cloud generator was used to calculate the numerical characteristics of a cloud model of quantitative indices, and different virtual clouds were used to synthesize some clouds into a generalized one. The synthesized numerical characteristics were calculated to comprehensively evaluate the safety of toppling rock slopes. A case study of a toppling rock slope near the Huangdeng Hydropower Station in China was conducted using monitoring data collected since operation of the hydropower project began. The results indicated that the toppling rock slope was moderately safe with a low safety margin. The composite cloud model considers the fuzziness and randomness of safety evaluation and enables interchange between qualitative and quantitative knowledge. This study provides a new theoretical method for evaluating the safety of toppling rock slopes. It can aid in the predication, control, and even prevention of disasters.

Newmark design spectra considering earthquake magnitudes and site categories

Newmark design spectra have been implemented in many building codes, especially in building codes for critical structures. Previous studies show that Newmark design spectra exhibit lower amplitudes at high frequencies and larger amplitudes at low frequencies in comparison with spectra developed by statistical methods. To resolve this problem, this study considers three suites of ground motions recorded at three types of sites. Using these ground motions, influences of the shear-wave velocity, earthquake magnitudes, source-to-site distances on the ratios of ground motion parameters are studied, and spectrum amplification factors are statistically calculated. Spectral bounds for combinations of three site categories and two cases of earthquake magnitudes are estimated. Site design spectrum coefficients for the three site categories considering earthquake magnitudes are established. The problems of Newmark design spectra could be resolved by using the site design spectrum coefficients to modify the spectral values of Newmark design spectra in the acceleration sensitive, velocity sensitive, and displacement sensitive regions.

A Novel Hybrid Intelligent Prediction Model for Valley Deformation: A Case Study in Xiluodu Reservoir Region, China

The narrowing deformation of reservoir valley during the initial operation period threatens the long-term safety of the dam,and an accurate prediction of valley deformation(VD)remains a challenging part of risk mitigation.In order to enhance the accuracy of VD prediction,a novel hybrid model combining Ensemble empirical mode decomposition based interval threshold denoising(EEMD-ITD),Differential evolutions—Shuffled frog leaping algorithm(DE-SFLA)and Least squares support vector machine(LSSVM)is proposed.The non-stationary VD series is firstly decomposed into several stationary subseries by EEMD;then,ITD is applied for redundant information denoising on special sub-series,and the denoised deformation is divided into the trend and periodic deformation components.Meanwhile,several relevant triggering factors affecting the VD are considered,from which the input features are extracted by Grey relational analysis(GRA).After that,DE-SFLA-LSSVM is separately performed to predict the trend and periodic deformation with the optimal inputs.Ultimately,the two individual forecast components are reconstructed to obtain the final predicted values.Two VD series monitored in Xiluodu reservoir region are utilized to verify the proposed model.The results demonstrate that:(1)Compared with Discrete wavelet transform(DWT),better denoising performance can be achieved by EEMD-ITD;(2)Using GRA to screen the optimal input features can effectively quantify the deformation response relationship to the triggering factors,and reduce the model complexity;(3)The proposed hybrid model in this study displays superior performance on some compared models(e.g.,LSSVM,Backward Propagation neural network(BPNN),and DE-SFLA-BPNN)in terms of forecast accuracy.