Buckling resistance of hot‐finished CHS beam‐columns using FE modelling and machine learning

The use of circular hollow sections (CHS) has increased in recent years owing to its excellent mechanical behaviour including axial compression and torsional resistance as well as its aesthetic appearance. They are popular in a wide range of structural members including beams, columns, trusses and arches. The behaviour of hot-finished CHS beam-columns made from normal and high strength steel is the main focus of this paper. A particular attention is given to predict the ultimate buckling resistance of CHS beam-columns using the recent advancement of the artificial neural network (ANN). FE models were established and validated to generate an extensive parametric study. The ANN model is trained and validated using a total of 3439 data points collected from the generated FE models and experimental tests available in the literature. A comprehensive comparative analysis with the design rules in Eurocode 3 is conducted to evaluate the performance of the developed ANN model. It is shown that the proposed ANN based design formula provides a reliable means for predicting the buckling resistance of the CHS beam-columns. This formula can be easily implemented in any programming software, providing an excellent basis for engineers and designers to predict the buckling resistance resistance of the CHS beam-columns with a straightforward procedure in an efficient and sustainable manner with least computational time

Machine learning for optimal design of circular hollow section stainless steel stub columns: A comparative analysis with Eurocode 3 predictions

Stainless steel has many advantages when used in structures, however, the initial cost is high. Hence, it is essential to develop reliable and accurate design methods that can optimize the material. As novel, reliable soft computation methods, machine learning provided more accurate predictions than analytical formulae and solved highly complex problems. The present study aims to develop machine learning models to predict the cross-section resistance of circular hollow section stainless steel stub column. A parametric study is conducted by varying the diameter, thickness, length, and mechanical properties of the column. This database is used to train, validate, and test machine learning models, Artificial Neural Network (ANN), Decision Trees for Regression (DTR), Gene Expression Programming (GEP) and Support Vector Machine Regression (SVMR). Thereafter, results are compared with finite element models and Eurocode 3 (EC3) to assess their accuracy. It was concluded that the EC3 models provided conservative predictions with an average Predicted-to-Actual ratio of 0.698 and Root Mean Square Error (RMSE) of 437.3. The machine learning models presented the highest level of accuracy. However, the SVMR model based on RBF kernel presented a better performance than the ANN, GEP and DTR machine learning models, and RMSE value for SVMR, ANN, GEP and DTR is 22.6, 31.6, 152.84 and 29.07, respectively. The GEP leads to the lowest level of accuracy among the other three machine learning models, yet, it is more accurate than EC3. The machine learning models were implemented in a user-friendly tool, which can be used for design purposes

Machine learning-driven web-post buckling resistance prediction for high-strength steel beams with elliptically-based web openings

The use of periodical elliptically-based web (EBW) openings in high strength steel (HSS) beams has been increasingly popular in recent years mainly because of the high strength-to-weight ratio and the reduction in the floor height as a result of allowing different utility services to pass through the web openings. However, these sections are susceptible to web-post buckling (WPB) failure mode and therefore it is imperative that an accurate design tool is made available for prediction of the web-post buckling capacity. Therefore, the present paper aims to implement the power of various machine learning (ML) methods for prediction of the WPB capacity in HSS beams with (EBW) openings and to assess the performance of existing analytical design model. For this purpose, a numerical model is developed and validated with the aim of conducting a total of 10,764 web-post finite element models, considering S460, S690 and S960 steel grades. This data is employed to train and validate different ML algorithms including Artificial Neural Networks (ANN), Support Vector Machine Regression (SVR) and Gene Expression Programming (GEP). Finally, the paper proposes new design models for WPB resistance prediction. The results are discussed in detail, and they are compared with the numerical models and the existing analytical design method. The proposed design models based on the machine learning predictions are shown to be powerful, reliable and efficient design tools for capacity predictions of the WPB resistance of HSS beams with periodical (EBW) openings

Machine learning-driven web-post buckling resistance prediction for high-strength steel beams with elliptically-based web openings

Data availability: Data will be made available on request.Copyright © 2024 The Authors.. The use of periodical elliptically-based web (EBW) openings in high strength steel (HSS) beams has been increasingly popular in recent years mainly because of the high strength-to-weight ratio and the reduction in the floor height as a result of allowing different utility services to pass through the web openings. However, these sections are susceptible to web-post buckling (WPB) failure mode and therefore it is imperative that an accurate design tool is made available for prediction of the web-post buckling capacity. Therefore, the present paper aims to implement the power of various machine learning (ML) methods for prediction of the WPB capacity in HSS beams with (EBW) openings and to assess the performance of existing analytical design model. For this purpose, a numerical model is developed and validated with the aim of conducting a total of 10,764 web-post finite element models, considering S460, S690 and S960 steel grades. This data is employed to train and validate different ML algorithms including Artificial Neural Networks (ANN), Support Vector Machine Regression (SVR) and Gene Expression Programming (GEP). Finally, the paper proposes new design models for WPB resistance prediction. The results are discussed in detail, and they are compared with the numerical models and the existing analytical design method. The proposed design models based on the machine learning predictions are shown to be powerful, reliable and efficient design tools for capacity predictions of the WPB resistance of HSS beams with periodical (EBW) openings

Machine learning-driven web-post buckling resistance prediction for high-strength steel beams with elliptically-based web openings

The use of periodical elliptically-based web (EBW) openings in high strength steel (HSS) beams has been increasingly popular in recent years mainly because of the high strength-to-weight ratio and the reduction in the floor height as a result of allowing different utility services to pass through the web openings. However, these sections are susceptible to web-post buckling (WPB) failure mode and therefore it is imperative that an accurate design tool is made available for prediction of the web-post buckling capacity. Therefore, the present paper aims to implement the power of various machine learning (ML) methods for prediction of the WPB capacity in HSS beams with (EBW) openings and to assess the performance of existing analytical design model. For this purpose, a numerical model is developed and validated with the aim of conducting a total of 10,764 web-post finite element models, considering S460, S690 and S960 steel grades. This data is employed to train and validate different ML algorithms including Artificial Neural Networks (ANN), Support Vector Machine Regression (SVR) and Gene Expression Programming (GEP). Finally, the paper proposes new design models for WPB resistance prediction. The results are discussed in detail, and they are compared with the numerical models and the existing analytical design method. The proposed design models based on the machine learning predictions are shown to be powerful, reliable and efficient design tools for capacity predictions of the WPB resistance of HSS beams with periodical (EBW) openings

Identifying Key Factors for Accelerating the Transition to Animal-Testing-Free Medical Science through Co-Creative, Interdisciplinary Learning between Students and Teachers

Even with the introduction of the replacement, reduction, refinement (the three Rs) approach and promising technological developments in animal-testing-free alternatives over the past two decades, a significant number of animal tests are still performed in medical science today. This article analyses which factors could accelerate the transition to animal-free medical science, applying the multi-level perspective (MLP) framework. The analysis was based on qualitative research, including a desk study (literature review and document analysis), lectures from experts, and nine online focus group sessions with experts on 26 July 2021. These were undertaken as part of an honours project between May and September 2021 to identify barriers, levers, and opportunities for accelerating this transition. The MLP framework identifies required changes at three levels: innovations and new practices (niche level), the current (bio)medical research system (regime level), and larger societal forces (landscape level). All three levels interact in a non-linear fashion. The model enabled us to identify many relevant factors influencing the transition to animal-testing-free medical science and enabled priority setting. Our findings supported the formulation of six "focus areas" to which stakeholders could devote efforts in order to accelerate the transition to animal-testing-free medical science: (1) thorough and translatable new approach methods (NAMs) for human-relevant medical research; (2) open science and sharing data; (3) targeted funding for NAMs; (4) implementing and modernising legislation for NAMs; (5) interdisciplinary education on animal-testing-free medical science; and (6) facilitating a shift in societal views, as this would be of benefit to both animals and humans. It is proposed that these focus areas should be implemented in parallel

Pontiac fever: an operational definition for epidemiological studies

BACKGROUND: Pontiac fever is usually described in epidemic settings. Detection of Pontiac fever is a marker of an environmental contamination by Legionella and should thereby call for prevention measures in order to prevent outbreak of Legionnaire's disease. The objective of this study is to propose an operational definition of Pontiac fever that is amenable to epidemiological surveillance and investigation in a non epidemic setting. METHODS: A population of 560 elderly subjects residing in 25 nursing homes was followed during 4 months in order to assess the daily incidence of symptoms associated, in the literature, with Pontiac fever. The water and aerosol of one to 8 showers by nursing home were characterized combining conventional bacterial culture of Legionella and the Fluorescence In Situ Hybridization (FISH) technique that used oligonucleotides probes specific for Legionellaceae. A definition of Pontiac fever was devised based on clinical symptoms described in epidemic investigations and on their timing after the exposure event. The association between incidence of Pontiac fever and shower contamination levels was evaluated to test the relevance of this definition. RESULTS: The proposed definition of Pontiac fever associated the following criteria: occurrence of at least one symptom among headache, myalgia, fever and shivers, possibly associated with other 'minor' symptoms, within three days after a shower contaminated by Legionella, during a maximum of 8 days (minimum 2 days). 23 such cases occurred during the study (incidence rate: 0.125 cases per person-year [95% CI: 0.122–0.127]). A concentration of Legionella in water equal to or greater than 10(4).L(-1 )(FISH method) was associated with a significant increase of incidence of Pontiac fever (p = 0.04). CONCLUSION: Once validated in other settings, the proposed definition of Pontiac fever might be used to develop epidemiological surveillance and help draw attention on sources of Legionella