Seismic Analysis of Earth Slope Using a Novel Sequential Hybrid Optimization Algorithm

Khajehzadeh, Mohammad; Keawsawasvong, Suraparb; Sarir, Payam; Khailany, Dlshad Khurshid

doi:10.3311/ppci.19356

Cited by 14 publications

(10 citation statements)

References 25 publications

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“…According to the statistical criteria (i.e., R, MAPE, RMSE, and VAF), the SVR method has higher accuracy than other methods. Because SVR uses the principle of minimizing structural risk and adapting the ability of the model to existing training data [49,50]. The size of inputs also affects the accuracy of the methods.…”

Section: Comparison Of Used Methodsmentioning

confidence: 99%

Prediction of Rock Tensile Strength Using Soft Computing and Statistical Methods

Zheng

Shen

Motahari

et al. 2023

Period. Polytech. Civil Eng.

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The tensile strength of the rocks is one of the effective factors in the rupture of structure foundations and underground spaces, the stability of rocky slopes, and the ability to drill and explode in rocks. This research was conducted to estimate tensile strength using methods such as simple regression (SR), multivariate linear regression (MVLR), support vector regression (SVR) with radial basis kernel function, multilayer feed-forward artificial neural network (MFF-ANN), Gaussian process regression (GPR) using squared exponential kernel (SEK) function, and adaptive neuro-fuzzy inference system (ANFIS) based on Gaussian membership function. For this purpose, petrography, and engineering features of the limestone, sandstone, and argillaceous limestone samples in the south of Iran, were assessed. The results obtained from this study were compared with those of previous research, revealing a strong correlation (R2=0.95 to 1.00) between our findings and the published works. To estimate Brazilian tensile strength (BTS), the index properties including water absorption by weight, point load index (PLI), porosity%, P-wave velocity (Vp), and density were considered as inputs. Methods were compared using various criteria. The SVR precision (R=0.96) was higher than MFF-ANN (R=0.92), ANFIS (R=0.95), GPR (R=0.945), and MVLR (R=0.89) to estimate the tensile strength. The average BTS measured in the laboratory and predicted by all 5 methods is 6.62 and 6.71 MPa, respectively, which shows the very high precision of the investigated methods. Analysis of model criteria using statistical analysis for developed relationships revealed that there is sufficient accuracy to use the empirical equations.

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Section: Comparison Of Used Methodsmentioning

confidence: 99%

Prediction of Rock Tensile Strength Using Soft Computing and Statistical Methods

Zheng

Shen

Motahari

et al. 2023

Period. Polytech. Civil Eng.

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“…There are other algorithms that are a combination or modification of algorithms in these four categories. Some of them are: modified particle swarm optimization [36], [37], modified harmony search algorithm [38], modified gravitational search algorithm [39], [40], modified ant colony optimization [41], modified sine cosine algorithm [42], [43], modified wild horse optimization [44], modified slime mould algorithm [45], hybrid genetic algorithm and particle swarm optimization [46], hybrid firefly algorithm [47], hybrid sperm swarm optimization and gravitational search algorithm [48], hybrid tunicate swarm algorithm and pattern search [49], hybrid arithmetic optimization algorithm and sine cosine algorithm [50].…”

Section: Related Workmentioning

confidence: 99%

Golden Search Optimization Algorithm

et al. 2022

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This study introduces an effective population-based optimization algorithm, namely the Golden Search Optimization Algorithm (GSO), for numerical function optimization. The new algorithm has a simple but effective strategy for solving complex problems. GSO starts with random possible solutions called objects, which interact with each other based on a simple mathematical model to reach the global optimum. To provide a fine balance between the explorative and exploitative behavior of a search, the proposed method utilizes a transfer operator in the adaptive step size adjustment scheme. The proposed algorithm is benchmarked with 23 unimodal, multimodal, and fixed dimensional functions and the results are verified by a comparative study with the well-known Gravitational Search Algorithm (GSA), Sine-Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Tunicate Swarm Algorithm (TSA). In addition, the nonparametric Wilcoxon's rank sum test is performed to measure the pair-wise statistical performance of the GSO and provide a valid judgment about the performance of the algorithm. The simulation results demonstrate that GSO is superior and could generate better optimal solutions when compared with other competitive algorithms.

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“…Therefore, a certain method is needed to obtain the optimal parameters. Studies on optimization cases have been conducted by previous researchers [16,17,18,19].…”

Section: Introductionmentioning

confidence: 99%

Optimum Design of Tuned Mass Damper Parameters to Reduce Seismic Response on Structure Using Genetic Algorithm

Suryadi

Prasetya²,

Daratha³

et al. 2023

AEJ

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A tuned mass damper (TMD) is a system to improve structural performance in reducing vibration response under seismic loads. This study optimizes TMD parameters using a genetic algorithm. In this study, the structure is modeled as a spring-mass system to obtain mathematical equations. Then, those equations are used to estimate vibration due to seismic loads. A genetic algorithm is used to find optimum parameters which correspond to minimum vibrations. The simulation result shows that the genetic algorithm can find values of parameters such as the ratio of mass, stiffness, and damping values which reduces vibrations on the structure. Based on the tests, it is found that the best combination of genetic algorithm parameters to produce the most optimal fitness value is population size 30, generation size 800, crossover probability 0.5, and mutation probability 0.5. Applying the genetic algorithm shows that the optimal parameter ratio values of TMD to reduce the vibration response are 10% mass, 10% stiffness, and 1% damping of mass, stiffness, and damping in the structure. The result of the vibration response analysis shows that the maximum amplitude value of the main structure without TMD is 0.24259 m reduced to 0.034385 m with the addition of TMD. This shows that the TMD successfully reduces vibrations in the main structure with a percentage of 85.94%. Where the TMD manages to dissipate the energy that should only be received by the structure to the TMD itself.

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Seismic Analysis of Earth Slope Using a Novel Sequential Hybrid Optimization Algorithm

Cited by 14 publications

References 25 publications

Prediction of Rock Tensile Strength Using Soft Computing and Statistical Methods

Prediction of Rock Tensile Strength Using Soft Computing and Statistical Methods

Golden Search Optimization Algorithm

Optimum Design of Tuned Mass Damper Parameters to Reduce Seismic Response on Structure Using Genetic Algorithm

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