Forecasting of excavation problems for high-rise building in Vietnam using planet optimization algorithm

Sang-To, Thanh; Minh, Hoang-Le; Khatir, Samir; Mirjalili, Seyedali; Wahab, Magd Abdel; Cuong-Le, Thanh

doi:10.1038/s41598-021-03097-y

Cited by 25 publications

(8 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While the algorithms we used utilized well-known single and double-order optimizers such as the ADAM optimizer, future work could include recent, alternate optimization techniques. Some such optimization techniques include the planet optimization algorithm 41 , which uses an optimizer based on stochastic decisions and surrounding exploration, the Runga Kutta method-based RUN optimization 42 , which effectively implements and balances the exploration and exploitation in the search phase and the Hunger Games search 43 , which designs and employs an adaptive weight based on the concept of hunger on each search step.…”

Section: Discussionmentioning

confidence: 99%

Optimizing the input feature sets and machine learning algorithms for reliable and accurate estimation of continuous, cuffless blood pressure

Kasbekar

Clancy

et al. 2023

Sci Rep

View full text Add to dashboard Cite

The advent of mobile devices, wearables and digital healthcare has unleashed a demand for accurate, reliable, and non-interventional ways to measure continuous blood pressure (BP). Many consumer products claim to measure BP with a cuffless device, but their lack of accuracy and reliability limit clinical adoption. Here, we demonstrate how multimodal feature datasets, comprising: (i) pulse arrival time (PAT); (ii) pulse wave morphology (PWM), and (iii) demographic data, can be combined with optimized Machine Learning (ML) algorithms to estimate Systolic BP (SBP), Diastolic BP (DBP) and Mean Arterial Pressure (MAP) within a 5 mmHg bias of the gold standard Intra-Arterial BP, well within the acceptable limits of the IEC/ANSI 80601-2-30 (2018) standard. Furthermore, DBP’s calculated using 126 datasets collected from 31 hemodynamically compromised patients had a standard deviation within 8 mmHg, while SBP’s and MAP’s exceeded these limits. Using ANOVA and Levene’s test for error means and standard deviations, we found significant differences in the various ML algorithms but found no significant differences amongst the multimodal feature datasets. Optimized ML algorithms and key multimodal features obtained from larger real-world data (RWD) sets could enable more reliable and accurate estimation of continuous BP in cuffless devices, accelerating wider clinical adoption.

show abstract

Section: Discussionmentioning

confidence: 99%

Optimizing the input feature sets and machine learning algorithms for reliable and accurate estimation of continuous, cuffless blood pressure

Kasbekar

Clancy

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…For soft soil, the ratio is ordinarily in the range [170, 500], while it is in the range [500, 700] for stiff clay. [83][84][85][86][87][88] To cover all possible scenarios, the search space can be extended and modified. For sand, the stiffness parameter (E') is calculated using the results of the Standard Penetration Test (N spt ).…”

Section: Soil Layersmentioning

confidence: 99%

“…Much concentrated effort to back analyze the ratio of the stiffness and undrained shear strength was made by researchers. For soft soil, the ratio is ordinarily in the range [170, 500], while it is in the range [500, 700] for stiff clay 83–88 . To cover all possible scenarios, the search space can be extended and modified.…”

Section: Calibration Of Model Parameters Of Large‐scale Problemmentioning

confidence: 99%

A nonlinear optimization method for calibration of large‐scale deep cement mixing in very soft clay deep excavation

To,

Minh,

Huynh

et al. 2024

Num Anal Meth Geomechanics

Self Cite

View full text Add to dashboard Cite

This work proposes a novel technique to conduct back‐analysis of lateral displacement of deep cement mixing (DCM) columns in deep excavation construction. For the first time, we propose a process to investigate both soil and underground structure end‐to‐end automatically. The novel technique is a complex combination of three crucial factors: (1) a nature‐inspired optimization algorithm (O), (2) a three‐dimensional PLAXIS Geotechnical Engineering software (P) and (3) a modern Python language programming (P), hereinafter called the a nature‐inspired optimization algorithm (O), a three‐dimensional PLAXIS Geotechnical Engineering software (P) and a modern Python language programming (P) (OPP) technique. The novel meta‐heuristic algorithm simulated the co‐evolved partnership behavior of shrimps and goby fishes, termed Shrimp and Goby Association (SGA), which plays an important role in complex analyses. A series of exams to determine the SGA's performance is conducted on 38 benchmark test functions (IEEE Congress on Evolutionary Computation 2017 and 2019) and three real‐world engineering design problems to showcase its applicability. The metaheuristic and PLAXIS 3D analysis work well together, which makes the back‐analysis technique powerfully to determinate stiffness parameters instead of the traditional approaches. Based on the optimized parameters, the lateral deflection of DCM and soil are well predicted for excavation. This study proposes a technique to estimate efficiently the stiffness parameter for very soft soil. As a consequence of the optimization process, an equation to determine the stiffness parameter of DCM columns from laboratory test is also proposed. Based on the obtained results, this research provides a comprehensive methodology for predicting risk, enhancing safety, saving time and money, and effectively designing and constructing underground structures.

show abstract

“…Among the prediction models proposed by scholars, the grey model and BP neural network model prediction methods are most widely used to predict surface subsidence in underground mining areas [6][7][8] . In 1982, Professor Deng Julong, a Chinese scholar, proposed the grey system theory integrating automatic control and operations research, aiming at the in-depth exploration of grey problems.…”

Section: Introductionmentioning

confidence: 99%

Application of grey BP neural network model based on wavelet denoising to predict the residual settlement of goafs

Zhang

et al. 2022

Preprint

View full text Add to dashboard Cite

The residual settlement of goafs is a nonlinear process with time series. To study its settlement law and prediction model, we chose the Mentougou mining area in Beijing as a example. The wavelet threshold denoising method was used to optimize the measured data, and the Grey GM (1,1) and BP neural network models were combined in series. A grey BP neural network model based on wavelet denoising was proposed, the prediction accuracy of different models was calculated, and the prediction results were compared with the original data. The results showed that the prediction accuracy of the grey BP neural network combined model was higher than that of the single GM (1,1) model. Furthermore, the mean absolute percentage and root mean square errors of the grey BP neural network model after wavelet denoising were 0.35% and 16.05, respectively, both less than the errors of the combined prediction model using the original data. Thus, the combination model optimized by wavelet analysis had a high prediction accuracy, strong stability, and accorded with the law of change of measured data. It accurately reflected the goaf surface subsidence process, and therefore has a strong popularization and application value.

show abstract

Forecasting of excavation problems for high-rise building in Vietnam using planet optimization algorithm

Cited by 25 publications

References 25 publications

Optimizing the input feature sets and machine learning algorithms for reliable and accurate estimation of continuous, cuffless blood pressure

Optimizing the input feature sets and machine learning algorithms for reliable and accurate estimation of continuous, cuffless blood pressure

A nonlinear optimization method for calibration of large‐scale deep cement mixing in very soft clay deep excavation

Application of grey BP neural network model based on wavelet denoising to predict the residual settlement of goafs

Contact Info

Product

Resources

About