Two-Stepped Evolutionary Algorithm and Its Application to Stability Analysis of Slopes

Yang, Chengxiang; Tham, LG; Feng, Xia‐Ting; Wang, Y. J.; Lee, P. K. K.

doi:10.1061/(asce)0887-3801(2004)18:2(145)

Cited by 67 publications

(22 citation statements)

References 25 publications

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“…(iii) Genetic programming is attractive to recognize the structures of nonlinear rock mechanics models which are represented by mathematical equations. Some intelligent search algorithms, such as genetic algorithms and particle swarm optimization, can be used to determine the coefficients of mechanical models [26].…”

Section: Characters Of Isdmentioning

confidence: 99%

Intelligent Stability Design of Large Underground Hydraulic Caverns: Chinese Method and Practice

Quan

Feng

2011

Energies

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Abstract:The global energy shortage has revived the interest in hydroelectric power, but extreme geological condition always pose challenges to the construction of hydroelectric power stations with large underground caverns. To solve the problem of safe design of large underground caverns, a Chinese-style intelligent stability design, representing recent developments in Chinese techniques for the construction of underground hydropower systems is presented. The basic aim of this method is to help designers improve the stability and design efficiency of large underground hydropower cavern groups. Its flowchart consists of two parts, one is initial design with an ordinal structure, and the other is dynamic design with a closed loop structure. In each part of the flowchart, analysis techniques, analysis content and design parameters for caverns' stability are defined, respectively. Thus, the method provides designers with a bridge from the basic information of objective engineering to reasonable design parameters for managing the stability of hydraulic cavern groups. Application to two large underground caverns shows that it is a scientific and economical method for safely constructing underground hydraulic caverns.

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Section: Characters Of Isdmentioning

confidence: 99%

Intelligent Stability Design of Large Underground Hydraulic Caverns: Chinese Method and Practice

Quan

Feng

2011

Energies

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“…another group of AI techniques, are based on statistical learning theory [30,31]; they are found to be better than the ANN model for some geotechnical engineering problems [32]. In the recent past, Genetic Programming (GP), which is a biologically inspired AI method, has been used as an AI technique to model di cult geotechnical engineering problems [33][34][35][36][37][38]. Alavi et al [39] found that a hybrid computational model (coupling of GP and simulated annealing) provided a better prediction performance than GP did, by predicting the uplift capacity of suction caisson using the above database (Rahman et al [26]).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Uplift Capacity of Suction Caisson in Clay Using Functional Network and Multivariate Adaptive Regression Spline

2017

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Abstract. Suction caissons are extensively used as anchors for o shore foundation structures. The uplift capacity of suction caisson is an important factor with respect to e ective design. In this paper, two recently developed AI techniques, i.e. Functional Network (FN) and Multivariate Adaptive Regression Spline (MARS), have been used to predict the uplift capacity of suction caisson in clay. The performances of the developed models are compared with those of other AI techniques: arti cial neural network, support vector machine, relevance vector machine, genetic programming, extreme learning machine, and Group Method of Data Handling with Harmony Search (GMDH-HS). The model's inputs include the aspect ratio of the caisson, undrained shear strength of soil at the depth of the caisson tip, relative depth of the lug to which the caisson force is applied, load inclination angle, and load rate parameter. The results of the above AI techniques are comparatively analysed via di erent statistical performance criteria: correlation coe cient (R), root mean square error, Nash-Sutcli e coe cient of e ciency, and log-normal distribution of ratio of the predicted load capacity to observed load capacity, with a ranking system to determine the best predictive model. The FN and MARS models are found to be comparably e cient which can outperform other AI techniques.

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“…Later, the differential evolution algorithm (DE) is a new global optimization algorithm brought up by Rainer Storm and Ken Price in 1995, which has no requirement for the initial values and possesses advantages, such as quick convergence speed, strong adaptability for various functions, and parallel calculation. Especially, it is applicable to the optimization of complex problems [8][9][10][11]. In order to master the stability of tunnel in which the vehicle load and environmental conditions change constantly in normal operation period and dynamically follow as well as evaluate the safety status of the tunnel, this paper developed a real-time monitoring system for tunnel vault based on OTDR distributed sensor.…”

Section: Introductionmentioning

confidence: 99%

Inversion Calculation Analysis of Operational Tunnel Structure Based on the Distributed Optical-Fiber Sensing System

Zhao

Feng

Ren

2017

Advances in Civil Engineering

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In combination of the actual project in Dalian Baiyun Mountain Tunnel, this paper introduces the principle of fiber optic sensor monitoring system based on optical time domain reflectometer. Then, based on the orthogonal design and even design scheme, this paper carries out a numerical experiment on the tunnel surrounding rock and establishes a regression model of the mapping relation between surrounding rock parameters of operation tunnel and the monitored displacement in order to set the difference between the monitored displacement and the calculated displacement as the fitness function. In the end, this paper carries out parameter identification based on the differential evolution algorithm. Achievements of the study proved that real-time safety warning could be realized inside the tunnel by monitoring the deformation parameters of tunnel vault at real time relying on the optical-fiber sensing system of the optical time domain reflectometer (OTDR). Parameters identification was carried out on the structure with differential evolution according to measured data and selected parameters, and great coincidence was obtained between the measured displacement and the identified parameters displacement, which proved the strong adaptability of the method.

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Two-Stepped Evolutionary Algorithm and Its Application to Stability Analysis of Slopes

Cited by 67 publications

References 25 publications

Intelligent Stability Design of Large Underground Hydraulic Caverns: Chinese Method and Practice

Intelligent Stability Design of Large Underground Hydraulic Caverns: Chinese Method and Practice

Prediction of Uplift Capacity of Suction Caisson in Clay Using Functional Network and Multivariate Adaptive Regression Spline

Inversion Calculation Analysis of Operational Tunnel Structure Based on the Distributed Optical-Fiber Sensing System

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