Slope Stability Prediction of Road Embankment using Artificial Neural Network Combined with Genetic Algorithm

Mamat, Rufaizal Che; Ramli, Azuin; Yazid, Muhamad Razuhanafi Mat; Kasa, Anuar; Razali, Siti Fatin Mohd; Bastam, Mukhlis Nahriri

doi:10.17576/jkukm-2022-34(1)-16

Cited by 8 publications

(4 citation statements)

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“…Additionally, other metrics such as the correlation coefficient (R), Willmot index (WI), and Nash coefficient (NSE) have been utilized to gauge the accuracy of the predictions and determine how well they align with the measured values. the mathematical expressions of the applied metrics are provided below (Eq.10 to Eq.16) [76][77][78] :…”

Section: Statistical Metricsmentioning

confidence: 99%

Investigating a Hybrid Extreme Learning Machine Coupled with Dingo Optimization Algorithm for Liquefaction Triggering in Sand-Silt Mixtures

Hameed,

Masood,

Srivast

et al. 2024

Preprint

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Liquefaction is a devastating consequence of earthquakes that occur in loose, saturated soil deposits, resulting in catastrophic ground failure. Accurate prediction of such geotechnical parameters is crucial for mitigating hazards, assessing risks, and advancing geotechnical engineering. This study introduces a novel predictive model that combines the Extreme Learning Machine (ELM) with the Dingo Optimization Algorithm (DOA) to estimate strain energy-based liquefaction resistance. The hybrid model (ELM-DOA) is compared with classical ELM, Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM model), and Sub-clustering (ANFIS-Sub model). Also, two data pre-processing scenarios are employed, namely traditional linear and non-linear normalization. The results demonstrate that non-linear normalization significantly enhances the prediction performance of all models by approximately 25% compared to linear normalization. Furthermore, the ELM-DOA model achieves the most accurate predictions, exhibiting the lowest root mean square error (484.286 J/m3), mean absolute percentage error (24.9%), mean absolute error (404.416 J/m3), and the highest correlation of determination (0.935). Additionally, a Graphical User Interface (GUI) has been developed, specifically tailored to the ELM-DOA model, to aid engineers and researchers in effectively utilizing the predictive model. The GUI provides a user-friendly platform for easy input of data and accessing the model's predictions, enhancing its practical applicability. Overall, the results strongly support the proposed hybrid model with GUI serving as an effective tool for assessing soil liquefaction resistance in geotechnical engineering, aiding in predicting and mitigating liquefaction hazards.

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Section: Statistical Metricsmentioning

confidence: 99%

Investigating a Hybrid Extreme Learning Machine Coupled with Dingo Optimization Algorithm for Liquefaction Triggering in Sand-Silt Mixtures

Hameed,

Masood,

Srivast

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Generally, many optimization method can be used for identifying the location of critical failure surface, such as the genetic algorithm (GA), 39,40 particle swarm optimization algorithm 41,42 , and so on 1,29 . The GA has many advantages, such as easy to implement, high search efficiency, global optimization, and so forth 43,44 . Therefore, it is adopted to find the critical failure surface in the present work, and the implementation processes are as follows: Step 1 : The ranges of seven optimization parameters are selected.…”

Section: Generation Of the Critical 3d Failure Surfacementioning

confidence: 99%

“…1,29 The GA has many advantages, such as easy to implement, high search efficiency, global optimization, and so forth. 43,44 Therefore, it is adopted to find the critical failure surface in the present work, and the implementation processes are as follows:…”

Section: Finding a Failure Surface With Maximum Sdmentioning

confidence: 99%

Identification of arbitrarily shaped critical 3D failure surface of a slope based on the sliding direction

Sun,

Yu,

Dong

2023

Num Anal Meth Geomechanics

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The determination of critical failure surface and stability evaluation of 3D slopes have been a hot and difficult problem in geotechnical engineering. The conventional method defines the critical failure surface using the safety factor (SF) whereas it is powerless for some special problems, such as two different failure surfaces with equal SFs. So far, many studies on the critical failure surface of a 3D slope have mainly focused on rotational ellipsoid, while less research has been done on the arbitrary shape. To solve the above problems, we develop a model for constructing a arbitrarily shaped failure surface and ascertain the critical failure surface by the sliding direction (SD). To verify rationality of the introduced theory, the critical failure surfaces of two benchmark symmetric slopes and corresponding SFs are reanalyzed, and the results are compared with the reference solutions. Meanwhile, the results obtained by the proposed method are also consistent with the reference solutions for the complex slope with the asymmetric failure surface. The parametric analysis explores the relationships between the mechanical properties of soil and the SD as well as the SF. Furthermore, influence factors of the spatial characteristics of sliding mass are also revealed. The proposed method is used as a helpful complement to the existing methods.

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“…Some researchers have developed hybrid models based on natureinspired algorithms that are effective and adaptable in forecasting drought. The meta-heuristic algorithms can help produce models with high prediction accuracy by training or optimizing the parameters of ML models, which substantially affects the model performance [48,49]. Notably, fourteen meta-algorithms have been intensively used to improve the ML model's performance in drought forecasting [24,[50][51][52][53][54].…”

Section: Introductionmentioning

confidence: 99%

Machine learning models development for accurate multi-months ahead drought forecasting: Case study of the Great Lakes, North America

Hameed,

Razali,

Mohtar

et al. 2023

PLoS ONE

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The Great Lakes are critical freshwater sources, supporting millions of people, agriculture, and ecosystems. However, climate change has worsened droughts, leading to significant economic and social consequences. Accurate multi-month drought forecasting is, therefore, essential for effective water management and mitigating these impacts. This study introduces the Multivariate Standardized Lake Water Level Index (MSWI), a modified drought index that utilizes water level data collected from 1920 to 2020. Four hybrid models are developed: Support Vector Regression with Beluga whale optimization (SVR-BWO), Random Forest with Beluga whale optimization (RF-BWO), Extreme Learning Machine with Beluga whale optimization (ELM-BWO), and Regularized ELM with Beluga whale optimization (RELM-BWO). The models forecast droughts up to six months ahead for Lake Superior and Lake Michigan-Huron. The best-performing model is then selected to forecast droughts for the remaining three lakes, which have not experienced severe droughts in the past 50 years. The results show that incorporating the BWO improves the accuracy of all classical models, particularly in forecasting drought turning and critical points. Among the hybrid models, the RELM-BWO model achieves the highest level of accuracy, surpassing both classical and hybrid models by a significant margin (7.21 to 76.74%). Furthermore, Monte-Carlo simulation is employed to analyze uncertainties and ensure the reliability of the forecasts. Accordingly, the RELM-BWO model reliably forecasts droughts for all lakes, with a lead time ranging from 2 to 6 months. The study’s findings offer valuable insights for policymakers, water managers, and other stakeholders to better prepare drought mitigation strategies.

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Slope Stability Prediction of Road Embankment using Artificial Neural Network Combined with Genetic Algorithm

Cited by 8 publications

References 0 publications

Investigating a Hybrid Extreme Learning Machine Coupled with Dingo Optimization Algorithm for Liquefaction Triggering in Sand-Silt Mixtures

Investigating a Hybrid Extreme Learning Machine Coupled with Dingo Optimization Algorithm for Liquefaction Triggering in Sand-Silt Mixtures

Identification of arbitrarily shaped critical 3D failure surface of a slope based on the sliding direction

Machine learning models development for accurate multi-months ahead drought forecasting: Case study of the Great Lakes, North America

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