Robust prediction of workability properties for 3D printing with steel slag aggregate using bayesian regularization and evolution algorithm

Construction and Building Materials, Год журнала: 2024, Номер 431, С. 136470 - 136470

Опубликована: Май 10, 2024

Язык: Английский

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Trajectory Optimization for Adaptive Deformed Wheels to Overcome Steps Using an Improved Hybrid Genetic Algorithm and an Adaptive Particle Swarm Optimization

Mathematics, Год журнала: 2024, Номер 12(13), С. 2077 - 2077

Опубликована: Июль 2, 2024

Two-wheeled mobile robots with deformed wheels face low stability when climbing steps, and their success rate in overcoming steps is affected by the trajectory. To address these challenges, we propose an improved hybrid genetic adaptive particle swarm optimization (HGAPSO) algorithm to optimize wheels’ trajectory for steps. HGAPSO optimizes maximum minimum values of inertial weight learning factors utilizing region-wide search capabilities algorithm, which substantially improves convergence speed adaptability. Furthermore, analysis motion wheel examination potential interference during operation are used construct a wheel’s center-of-mass route based on fifth-order Bézier curves. Comparative simulation experiments trajectories optimized using different algorithms under same working conditions designed demonstrate efficacy proposed optimizing step. Simulation were conducted deformation various sizes. These then compared unoptimized ones. The results showed that HGAPSO-optimized significantly robot

Язык: Английский

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Predicting load–displacement of driven PHC pipe piles using stacking ensemble with Pareto optimization

Engineering Structures, Год журнала: 2024, Номер 316, С. 118574 - 118574

Опубликована: Июль 15, 2024

Язык: Английский

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An effective optimum design for passive viscous damping control using FVDs/VWDs in multi-story buildings

Structures, Год журнала: 2024, Номер 67, С. 107004 - 107004

Опубликована: Авг. 7, 2024

Язык: Английский

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Optimizing flexural strength of RC beams with recycled aggregates and CFRP using machine learning models

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Ноя. 19, 2024

This paper investigates the flexural bearing behavior of reinforced concrete beams through experimental analysis and advanced machine learning predictive models. The primary problem centers around understanding how varying compositions construction materials, particularly inclusion recycled aggregates carbon fiber-reinforced polymer (CFRP), affect structural performance beams. Eight beams, including those with natural aggregates, fly ash, CFRP, were tested. study employs state-of-the-art frameworks, Random Forest Regressor (RFR), XGBoost (XGB), LightGBM (LGBM). formation these models involved data acquisition from experiments, preprocessing key input features (such as rebars area, cement portion, aggregate masses, silica fume, compressive strength, CFRP presence), model selection, hyperparameter tuning using Pareto optimization. then evaluated metrics like Mean Squared Error (MSE), Absolute (MAE), coefficient determination (R2). Outputs focus on load-induced deflection mid-span displacement. With a dataset 4851 samples, optimized demonstrated excellent performance. results revealed substantial enhancements in both strength load-bearing capacity, notably observed incorporating 70% 10% fume. These exhibited remarkable increase up to 53.03% 7% boost capacity compared without aggregate. By integrating computational techniques, this advances eco-friendly materials their performance, shedding light intricate interactions between sustainable

Язык: Английский

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Rapid Evaluation Method to Vertical Bearing Capacity of Pile Group Foundation Based on Machine Learning

Sensors, Год журнала: 2025, Номер 25(4), С. 1214 - 1214

Опубликована: Фев. 17, 2025

With the continuous increase in bridge lifespans, rapid check and evaluation of vertical bearing capacity for pile foundations existing bridges have been greater demand. The usual practice is to carry out compression tests under static loads order obtain accurate ratio dynamic stiffness. However, it difficult costly conduct situ experiments each foundation. Herein, a method measure proposed. Firstly, 3D-bearing cap-pile group-soil interaction model was established simulate test foundation that subject loads, then numerical results were validated by loading on an abandoned pier with same group foundation; dataset machine learning constructed using results, finally, could be predicted rapidly. show following outcomes: can effectively foundations; intelligent prediction based predict stiffness thus rapidly evaluate residual designed ultimate capacity, allowing nondestructive testing bridges.

Язык: Английский

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Comprehensive Parametric Study of Hammer Impact in Pile Driving Based on a General Analytical Dynamic Model

Iranian Journal of Science and Technology Transactions of Mechanical Engineering, Год журнала: 2025, Номер unknown

Опубликована: Фев. 18, 2025

Язык: Английский

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Behavior of under-reamed piles under inclined uplift loads in sand

International Journal of Geo-Engineering, Год журнала: 2025, Номер 16(1)

Опубликована: Фев. 24, 2025

Abstract Under-reamed piles are deep bored cast-in-situ concrete with single or multiple bulbs formed by enlarging the pile shaft. Such best suited to soils significant ground movement as a result of seasonal variations, filled up ground, soft soil layers, and loose sand. These also improve bearing uplift capacities, well anchorage at greater depths. This paper aims investigate analyze behavior under-reamed under inclined tension loads, compare results regular piles. Single double bulb diameter ratios 2 3 were used model embedded in sand relative densities 30, 50, 80%. The load was applied zero eccentricity above surface inclination angles 15º, 30º, 45º, 60º, 75º, 90º respect horizontal direction. indicated that ultimate capacity increased density, number bulbs, increased. Furthermore, when angle decreased, due generated passive earth pressure front pile. component has more effect on axial capacity; this decreases increase density. Finally, theoretical equation proposed estimate pile’s capacity. correlated experimental R = 0.91: 0.96.

Язык: Английский

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Pile Foundation Engineering in Vietnam: An Overview of Recent Advancements

Lecture notes in civil engineering, Год журнала: 2025, Номер unknown, С. 447 - 454

Опубликована: Янв. 1, 2025

Язык: Английский

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Sustainable foundation design: Hybrid TLBO-XGB model with confidence interval enhanced load–displacement prediction for PGPN piles

Advanced Engineering Informatics, Год журнала: 2025, Номер 65, С. 103288 - 103288

Опубликована: Апрель 6, 2025

Язык: Английский

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