Applying a novel slime mould algorithm- based artificial neural network to predict the settlement of a single footing on a soft soil reinforced by rigid inclusions

Zhang, Jiamin; Dias, Daniel; An, Lu; Li, Chuanqi

doi:10.1080/15376494.2022.2114048

Cited by 11 publications

(9 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Kriging optimization [i.e., Equation (7)] effect is often poor in high-nonlinear issues, resulting in an unacceptable modeling error. [50][51][52][53] Given the superior global optimization performance and the broad applicability, the slime mold algorithm (SMA) 54 (proposed by Li et al in 2020 54 ) is introduced, and it has been successfully applied in various fields, such as parameter tuning for machine learning models, 55,56 engineering design, 57,58 and multi-objective optimization problems. 59,60…”

Section: Optimization Algorithmmentioning

confidence: 99%

“…Given the superior global optimization performance and the broad applicability, the slime mold algorithm (SMA) 54 (proposed by Li et al. in 2020 54 ) is introduced, and it has been successfully applied in various fields, such as parameter tuning for machine learning models, 55,56 engineering design, 57,58 and multi‐objective optimization problems 59,60

$$\begin{equation}\left\{ \def\eqcellsep{&}\begin{array}{ll} {\rm find} & {{\bm \theta }} = ({\theta }_1,{\theta }_2,\ldots,{\theta }_m)\\[3pt] \min & ML({{\bm \theta }}) = {\sigma }^2({\bm{\theta }})|{{\bm R}}({\bm{\theta }}){|}^{1/m}\\[3pt] {\rm s.t.}

…”

Section: Adaptive Optimized Kriging Combining Efficient Sampling Aok‐esmentioning

confidence: 99%

See 1 more Smart Citation

AOK‐ES: Adaptive optimized Kriging combining efficient sampling for structural reliability analysis

Huang,

Zhang,

Wang

et al. 2024

Quality & Reliability Eng

View full text Add to dashboard Cite

The pivotal problem in reliability analysis is how to use as few actual assessments as possible to obtain an accurate failure probability. Although adaptive Kriging provides a viable method to address this problem, unsatisfied Kriging surrogate accuracy and reliability modeling samples often lead to an unacceptable computing burden. In this paper, an adaptive optimized Kriging combining efficient sampling (AOK‐ES) is proposed: first, to enhance the Kriging approximation ability, a high‐fidelity optimized Kriging model (OKM) is established; further, to ensure the samples quality of OKM modeling and reliability calculation, an improved Latin hypercube sampling method and an optimized importance sampling approach are developed correspondingly. Six different types of case studies demonstrate the superiority of the proposed AOK‐ES. The analysis results demonstrate that the proposed AOK‐ES holds the potential to reduce computing cost while ensuring accuracy.

show abstract

Section: Optimization Algorithmmentioning

confidence: 99%

$$\begin{equation}\left\{ \def\eqcellsep{&}\begin{array}{ll} {\rm find} & {{\bm \theta }} = ({\theta }_1,{\theta }_2,\ldots,{\theta }_m)\\[3pt] \min & ML({{\bm \theta }}) = {\sigma }^2({\bm{\theta }})|{{\bm R}}({\bm{\theta }}){|}^{1/m}\\[3pt] {\rm s.t.}

…”

Section: Adaptive Optimized Kriging Combining Efficient Sampling Aok‐esmentioning

confidence: 99%

AOK‐ES: Adaptive optimized Kriging combining efficient sampling for structural reliability analysis

Huang,

Zhang,

Wang

et al. 2024

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…In addition, the mean absolute error (MAE) can further reflect the real situation of error. These statistical indices have been considered to verify the performance of different prediction models for solving the regression problem [ 57 , 58 , 59 , 60 , 61 , 62 ].

where T indicates the number of used samples.…”

Section: Performance Evaluationmentioning

confidence: 99%

Application of the Improved POA-RF Model in Predicting the Strength and Energy Absorption Property of a Novel Aseismic Rubber-Concrete Material

et al. 2023

Self Cite

View full text Add to dashboard Cite

The application of aseismic materials in foundation engineering structures is an inevitable trend and research hotspot of earthquake resistance, especially in tunnel engineering. In this study, the pelican optimization algorithm (POA) is improved using the Latin hypercube sampling (LHS) method and the Chaotic mapping (CM) method to optimize the random forest (RF) model for predicting the aseismic performance of a novel aseismic rubber-concrete material. Seventy uniaxial compression tests and seventy impact tests were conducted to quantify this aseismic material performance, i.e., strength and energy absorption properties and four other artificial intelligence models were generated to compare the predictive performance with the proposed hybrid RF models. The performance evaluation results showed that the LHSPOA-RF model has the best prediction performance among all the models for predicting the strength and energy absorption property of this novel aseismic concrete material in both the training and testing phases (R2: 0.9800 and 0.9108, VAF: 98.0005% and 91.0880%, RMSE: 0.7057 and 1.9128, MAE: 0.4461 and 0.7364; R2: 0.9857 and 0.9065, VAF: 98.5909% and 91.3652%, RMSE: 0.5781 and 1.8814, MAE: 0.4233 and 0.9913). In addition, the sensitive analysis results indicated that the rubber and cement are the most important parameters for predicting the strength and energy absorption properties, respectively. Accordingly, the improved POA-RF model not only is proven as an effective method to predict the strength and energy absorption properties of aseismic materials, but also this hybrid model provides a new idea for assessing other aseismic performances in the field of tunnel engineering.

show abstract

“…Based on existing research, the current methods for predicting ground settlement mainly include purely theoretical calculation methods based on soil consolidation principles [17][18][19], various curvefitting methods based on measured settlement data commonly used in engineering [20][21], and emerging dynamic prediction methods represented by artificial neural network analysis [22][23][24]. In the process of settlement prediction, purely theoretical calculation methods based on ground consolidation settlement principles are overly complex and involve numerous parameters, making them less convenient to use.…”

Section: Introductionmentioning

confidence: 99%

Prediction of dredged soil settlement based on improved BP neural network

Li,

Liu

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Based on the measured settlement data from the Reclamation Project of Eastern Hengsha Shoal, two improved BP neural networks optimized by the particle swarm optimization (PSO) algorithm and genetic algorithm (GA) respectively are employed to predict the long-term consolidation settlement of the dredged soil in the reclamation area. By comparing the prediction results of the improved neural networks with those obtained from standard BP neural network and various curve fitting methods including hyperbola method, Hoshino method, and Asaoka method, as well as field-measured data, the improved neural network methods are validated to have a better predictive performance. The error analysis results indicate that compared with the standard BP neural network and curve-fitting prediction methods, the improved BP neural networks exhibit smaller prediction errors in terms of MAE, MSE, RMSE, MAPE, etc., and have a closer match between the predicted settlement values and the actual values. The selected GA-BP and PSO-BP neural network prediction methods in this study demonstrate higher prediction accuracy. Meanwhile, GA-BP demonstrates higher prediction accuracy and generalization ability than PSO-BP, but PSO-BP shows faster convergence in terms of prediction speed. The preferred methods presented in this paper exhibit good robustness and high reliability, which can achieve accurate prediction of dredged soil settlement. Additionally, unlike the traditional curve-fitting methods, the selected methods can effectively reduce the influence of subjective factors during the settlement prediction, and provide a new approach for settlement prediction, which can be effectively utilized as a substitute for long-term settlement monitoring and give reference for the design of settlement control of foundation of further construction projects.

show abstract

Applying a novel slime mould algorithm- based artificial neural network to predict the settlement of a single footing on a soft soil reinforced by rigid inclusions

Cited by 11 publications

References 48 publications

AOK‐ES: Adaptive optimized Kriging combining efficient sampling for structural reliability analysis

AOK‐ES: Adaptive optimized Kriging combining efficient sampling for structural reliability analysis

Application of the Improved POA-RF Model in Predicting the Strength and Energy Absorption Property of a Novel Aseismic Rubber-Concrete Material

Prediction of dredged soil settlement based on improved BP neural network

Contact Info

Product

Resources

About