Vacuum Insulation Panels (VIPs) for building construction industry - A review of the contemporary developments and future directions

This is the post-print version of the final paper published in Applied Energy. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2011 Elsevier B.V.Demand for energy efficient buildings has increased drastically in recent years and this trend will continue in the future. Insulating building elements will play a key role in meeting this demand by reducing heat losses through the building fabric. Due to their higher thermal resistance, Vacuum Insulation Panels (VIPs) would be a more energy efficient alternative to conventional building insulation materials. Thus, efforts to develop VIPs with characteristics suitable for applications to new and existing buildings are underway. This paper provides a review of important contemporary developments towards producing VIPs using various materials such as glass fibre, foams, perlite and fibre/powder composites. The limitations of the materials currently used to fabricate VIPs have not been emphasised in detail in previous review papers published. Selection criteria, methods to measure important properties of VIPs and analytical and numerical models presented in the past have been detailed. Limitations of currently employed design tools along with potential future materials such as Nano/microcellular foams and SiOx/SiNx coatings for use in VIPs are also described

Theoretical calculations of the total and ionization cross sections for electron impact on 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

Carbonated Aggregates and Basalt Fiber-Reinforced Polymers: Advancing Sustainable Concrete for Structural Use

Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed at the corresponding author.In the transition towards a circular economy, redesigning construction materials for enhanced sustainability becomes crucial. To contribute to this goal, this paper investigates the integration of carbonated aggregates (CAs) and basalt fibre-reinforced polymers (BFRPs) in concrete infrastructures as an alternative to natural sand (NS) and steel reinforcement. CA is manufactured using accelerated carbonation that utilizes CO2 to turn industrial byproducts into mineralised products. The structural performance of CA and BFRP-reinforced concrete simply supported slab was investigated through conducting a series of experimental tests to assess the key structural parameters, including bond strength, bearing capacity, failure behavior, and cracking bbehaviour. Carbon footprint analysis (CFA) was conducted to understand the environmental impact of incorporating BFRP and CA. The results indicate that CA exhibits a higher water absorption rate compared to NS. As the CA ratio increased, the ultrasonic pulse velocity (UPV), compressive, tensile, and flexural strength decreased, and the absorption capacity of concrete increased. Furthermore, incorporating 25% CA in concrete has no significant effect on the bond strength of BFRP. However, the load capacity decreased with an increasing CA replacement ratio. Finally, integrating BFRP and 50% of CA into concrete slabs reduced the slab’s CFA by 9.7% when compared with steel-reinforced concrete (RC) slabs.This research received no external funding

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

Data availability: Data will be made available on request.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

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

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