Theoretical calculations of the total and ionization cross sections for electron impacton some simple biomolecules

In this paper we report total cross sections TCS, QT, total elastic cross sections, Qel, and total ionization cross section, Qion for electron impact on water, formaldehyde, formic acid, and the formyl radical from circa 15 eV to 2 KeV. The results are compared where possible, with previous theoretical and experimental results and, in general, are found to be in good agreement. The total and elastic cross sections for HCHO, HCOOH, and CHO radical are reported

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

Toughness Performance of Recycled Aggregates for use in Road Pavement

Abstract The policy of driving organization such as Highways Agency is towards the use of performance related specifications. This policy and adoption of European wide aggregate standards on the one hand, and sustainable construction pressures on the other, all strongly emphasize on further need for more developments to specifications and performance assessment methodologies instead of creating barriers to the use of suitable materials. Performance related specifications for pavement foundations are being developed and are primarily based around in-situ control and compliance testing. Laboratory based tools for assessment of the performance of foundation materials and their durability under adverse conditions would be a key factor to the successful use of alternative materials. The toughness performance of recycled concrete aggregates (RCA) mixed with natural aggregates (NA) was evaluated based on the test specifications given in the NCHRP Report 598. For this purpose Los Angeles Abrasion and degradation test results were correlated with established Micro-Deval designations in NCHRP report 598.Three main factors involved in performance assessment; i.e. (a) traffic loading, (b) moisture levels in highway pavements and (c) the temperature conditions. The research study showed that the materials were appropriate for unbound subbase for medium traffic in non freezing condition from the standpoint of toughness. Also they are suitable for low traffic situations with low moisture and freezing weather

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

Physical and mechanical properties of foamed Portland cement composite containing crumb rubber from worn tires

The management of worn tires is a concern in industrialized countries. The application of crumb rubber as lightweight aggregate in cement based materials is a green alternative for reusing this material. High replacements of natural sand by crumb rubber were studied and an air-entraining agent was employed to ensure a cellular structure in the cement-based composite. The obtained results from tests in fresh state reveal an improvement in workability. The tests conducted on hardened composite show promise for constructive applications where thermal and acoustic properties are required. The minimum requirement of mechanical strength for masonry units was achieved, since compressive strengths varied between 1 and 10 MPa. Finally, potential applications as a construction material have been highlightedEiras Fernández, JN.; Segovia Rueda, F.; Borrachero Rosado, MV.; Monzó Balbuena, JM.; Bonilla Salvador, MM.; Paya Bernabeu, JJ. (2014). Physical and mechanical properties of foamed Portland cement composite containing crumb rubber from worn tires. Materials and Design. 59:550-557. doi:10.1016/j.matdes.2014.03.021S5505575