Rakenteiden Mekaniikka

Tekninen selvitys: Kommentteja aiempiin teknisiin selvityksiin

2024-04-15T11:38:05+03:00

Evaluation of machine learning techniques for capacity prediction of cold-formed steel beams subjected to bending

2024-07-01T12:51:22+03:00

Stiffened Cold-Formed Steel (CFS) sections often exhibit intricate nonlinear behaviors attributable to factors such as flexure effects and excessive slenderness. Traditional design methodologies, including the direct stiffness method, may inadequately capture these subtleties, potentially resulting in conservative or suboptimal designs. This study aimed to evaluate the performance of various machine learning algorithms, including simple and ensemble models, to predict the bending capacity of stiffened and unstiffened cold-formed beams in pure bending. A parametric study was conducted based on verified finite element analysis, and the machine learning algorithms were utilized to develop a unified capacity prediction method. The performance of six classical machine learning algorithms and four ensemble models were compared. The findings demonstrate that ensemble models, including AdaBoost, Gradient Boosting, Random Forest, and Extra Trees, outperform simple machine learning models in predicting the bending capacity of CFS beams. Moreover, introducing the stacking ensemble technique, using six different base models selectively, resulted in better performance than the individual baseline models. The approach addressed the nonlinearity pattern in the dataset caused by the flexure effect and excessive slenderness. The study suggests that adopting the proposed numerical and machine learning techniques could be a reliable method for predicting the structural behaviour and conducting cost-effective design of CFS beams, compared to the traditional analytical methods.

Numerical investigation of box shape effects on soil direct shear test

2024-05-31T18:38:30+03:00

The direct shear test is a fundamental method in geotechnical engineering that provides crucial soil shear strength parameters, including cohesion (c) and the angle of internal friction (ϕ). These parameters play a pivotal role in structural design, slope stability assessment, and soil stability evaluation. However, achieving a uniform normal stress distribution within the shear box remains a challenging task, which can result in inaccuracies in test results. This study investigates the impact of shear box shape, specifically comparing circular and square configurations, on the outcomes of the direct shear test. The findings reveal that the choice of lower or upper box movement has a minimal effect on test results. Moreover, circular boxes demonstrate superior normal stress distribution, leading to reduced variations in comparison to square boxes. Wall friction effects lead to lower shear capacity measurements, with circular boxes yielding higher shear levels when contrasted with square boxes. Additionally, the soil along the sides and corners of the specimen experiences diminished shear stress due to reduced normal stress. This research contributes significantly to our comprehension of how shear box shape influences the determination of shear strength parameters in direct shear tests, ultimately enhancing the reliability of geotechnical engineering assessments.