Impact of chopped basalt fibres on the mechanical proper- ties of concrete

Basalt fibre is a novel inorganic fibre which is produced from basalt rock. In this study the impact of chopped basalt fibres on the concrete workability, compressive and tensile strength, and concrete’s modulus of rupture at 7 and 28-days was investigated. The concrete used in this research was normal strength concrete with a target compressive strength of 30/37 MPa. In this re-search, fibre reinforced concrete samples were produced using basalt chopped fibres of two quantities (4 kg/m3 and 8 kg/m3) and three different fibre lengths, namely 25.4-mm, 12.7-mm, and 6.4-mm. The test findings revealed that slump decreased as the quantity of fibres increased and shorter fibres were used. The mechanical properties of concrete were affected by the fibre dosage and length. Overall, the results indicated that adding chopped basalt fibres improved the compressive, tensile, and flexural strength of concrete, particularly at early age, while slightly reducing the compressive strength at 28-days by an average of 3.9%. The results indicated that adding 4 kg/m3 of 25.4-mm long chopped basalt fibre into concrete provided the best performance of concrete in compressive and tensile strength, and modulus of ruptur

The impact of variation in chemical and physical properties of PFA and BPD semi-dry cement paste on strength properties

The effect of Pulverised Fuel Ash (PFA) and By-Pass-Dust (BPD) in ternary semi-dry cement pastes was reported. As well as this, the variability over 6 months in chemical composition and particle distribution was reviewed to determine impact on strength. The addition of BPD in ternary pastes resulted in a reduced strength when combined with PFA. PFA and BPD samples obtained over a 6 month period showed variability in both chemical composition and particle distribution. For PFA, it was reported that at 14 days the particle size distribution had greatest impact on strength and at 28 days the SiO2 content had greatest impact. The high variability in BPD particle size distribution resulted in finer particles achieving the greatest strength

Mechanical and GWP Assessment of Concrete Using Blast Furnace Slag, Silica Fume and Recycled Aggregate

Demolition waste and cement production is responsible for 36% of total waste produced on earth and 8% of the worlds CO2 emissions, respectively. Due to limited research on concrete mixes containing ternary cementitious mixes (Ground Granulated Blast-furnace Slag (GGBS) and Silica Fume (SF)) and demolition waste, the paper reviewed the mechanical properties of concrete, and structural performance of reinforced beams. Thereafter, life cycle analysis (LCA) was investigated to understand the true environmental impact, focusing on Global Warming Potential (GWP). Results show that recycled concrete aggregates (RCA) had no significant negative impact on the compressive strength, tensile strength, and modulus of rupture of concrete. The inclusion of GGBS and SF in mixes containing RCA eliminated any negative impact and for all mixes produced greater strengths in comparison to the control mix, due to the secondary reaction of Ca (OH)2 and pore refinement. The flexural behavior of the concrete beams with 0%, 25%, 50% and 100% RCA, 25% GGBS and 5% SF is similar. LCA results showed that replacing NA with 25%, 50% or 100% RCA has no significant impact on the GWP emissions. This is because of the similar emissions associated with manufacturing and processing of recycled and natural aggregates. However, replacing cement with 5% SF and 25% GGBS improves the GWP environmental response of concrete significantly. Additionally, natural aggregates have a higher GWP contribution than that of recycled concrete aggregates by almost 80% since the process of NA required quarry operation and transportation while the RCA are produced on site from an existing building waste

The impact of variation in chemical and physical properties of PFA and BPD semi-dry cement paste on strength properties

The effect of Pulverised Fuel Ash (PFA) and By-Pass-Dust (BPD) in ternary semi-dry cement pastes was reported. As well as this, the variability over 6 months in chemical composition and particle distribution was reviewed to determine impact on strength. The addition of BPD in ternary pastes resulted in a reduced strength when combined with PFA. PFA and BPD samples obtained over a 6 month period showed variability in both chemical composition and particle distribution. For PFA, it was reported that at 14 days the particle size distribution had greatest impact on strength and at 28 days the SiO2 content had greatest impact. The high variability in BPD particle size distribution resulted in finer particles achieving the greatest strength

Prediction of the cross-sectional capacity of cold-formed CHS using numerical modelling and machine learning

The use of circular hollow sections (CHS) have seen a large increase in usage in recent years mainly because of the distinctive mechanical properties and unique aesthetic appearance. The focus of this paper is the behaviour of cold-rolled CHS beam-columns made from normal and high strength steel, aiming to propose a design formula for predicting the ultimate cross-sectional load carrying capacity, employing machine learning. A finite element model is developed and validated to conduct an extensive parametric study with a total of 3410 numerical models covering a wide range of the most influential parameters. The ANN model is then trained and validated using the data obtained from the developed numerical models as well as 13 test results compiled from various research available in the literature, and accordingly a new design formula is proposed. A comprehensive comparison with the design rules given in EC3 is presented to assess the performance of the ANN model. According to the results and analysis presented in this study, the proposed ANN-based design formula is shown to be an efficient and powerful design tool to predict the cross-sectional resistance of the CHS beam-columns with a high level of accuracy and the least computational costs

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

Mechanical and GWP assessment of concrete using Blast Furnace Slag, Silica Fume and recycled aggregate

Data Availability: Data will be made available on request.Demolition waste and cement production is responsible for 36 % of total waste produced on earth and 8 % of the worlds CO2 emissions, respectively. Due to limited research on concrete mixes containing ternary cementitious mixes (Ground Granulated Blast-furnace Slag (GGBS) and Silica Fume (SF)) and demolition waste, the paper reviewed the mechanical properties of concrete, and structural performance of reinforced beams. Thereafter, life cycle analysis (LCA) was investigated to understand the true environmental impact, focusing on Global Warming Potential (GWP). Results show that recycled concrete aggregates (RCA) had no significant negative impact on the compressive strength, tensile strength, and modulus of rupture of concrete. The inclusion of GGBS and SF in mixes containing RCA eliminated any negative impact and for all mixes produced greater strengths in comparison to the control mix, due to the secondary reaction of Ca (OH)2 and pore refinement. The flexural behaviour of the concrete beams with 0 %, 25 %, 50 % and 100 % RCA, 25 % GGBS and 5 % SF is similar. LCA results showed that replacing NA with 25 %, 50 % or 100 % RCA has no significant impact on the GWP emissions. This is because of the similar emissions associated with manufacturing and processing of recycled and natural aggregates. However, replacing cement with 5 % SF and 25 % GGBS improves the GWP environmental response of concrete significantly. Additionally, natural aggregates have a higher GWP contribution than that of recycled concrete aggregates by almost 80 % since the process of NA required quarry operation and transportation while the RCA are produced on site from an existing building waste

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