Shear strength assessment of reinforced recycled aggregate concrete beams without stirrups using soft computing techniques

This paper presents a study to predict the shear strength of reinforced recycled aggregate concrete beams without stirrups using soft computing techniques. The methodology involves the development of a Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR) and Gene Expression Programming (GEP) models. The input variables considered are the longitudinal reinforcement ratio, recycled coarse aggregate ratio, beam cross-section dimensions, and concrete compressive strength. Data collected from the literature were used to train and validate the models. The results showed that the MOGA-EPR and GEP models can accurately predict the shear strength of beams without stirrups. The models also performed better than equations from the codes and literature. This study provides an alternative approach to accurately predict the shear strength of reinforced recycled aggregate concrete beams without stirrups

Soft computing models for assessing bond performance of reinforcing bars in concrete at high temperatures

The bond between steel and concrete in reinforced concrete structures is a multifaceted and intricate phenomenon that plays a vital role in the design and overall performance of such structures. It refers to the adhesion and mechanical interlock between the steel reinforcement bars and the surrounding concrete matrix. Under elevated temperatures, the bond is more complex under higher temperatures, yet having an accurate estimate is an important factor in design. Therefore, this paper focuses on using data-driven models to explore the performance of the concrete-steel bond under high temperatures using a Gene Expression Programming (GEP) soft computing model. The GEP models are developed to simulate the bond performance in order to understand the effect of high temperatures on the concrete-steel bond. The results were compared to the multi-objective evolutionary polynomial regression analysis (MOGA-EPR) models for different input variables. The new model would help the designers with strength predictions of the bond in fire. The dataset used for the model was obtained from experiments conducted in a laboratory setting that gathered a 316-point database to investigate concrete bond strength at a range of temperatures and with different fibre contents. This study also investigates the impact of the different variables on the equation using sensitivity analysis. the results show that the GEP models are able to predict bond performance with different input variables accurately. This study provides a useful tool for engineers to better understand the concrete-steel bond behaviour under high temperatures and predict concrete-steel bond performance under high temperatures

Bond behaviour of rebar in concrete at elevated temperatures:a soft computing approach

This paper assesses the capability of using a new data-driven approach to predict the bond strength between steel rebar and concrete subjected to high temperatures. The analysis has been conducted using a novel evolutionary polynomial regression analysis (EPR-MOGA) that employs soft computing techniques, and new correlations have been proposed. The proposed correlations provide better predictions and enhanced accuracy than existing approaches, such as classical regression analysis. Based on this novel approach, the resulting correlations have achieved a lower mean absolute error (𝑀𝐴𝐸), and root mean square error (𝑅𝑀𝑆𝐸), a mean (𝜇) close to the optimum value (1.0) and a higher coefficient of determination (R2) compared to available correlations, which use classical regression analysis. Based on their enhanced performance, the proposed correlations can be used to obtain better optimised and more robust design calculations

The influence of electronic waste and attapulgite clay on lightweight polyester concrete

Natural aggregate consumption for producing concrete depletes the natural aggregate, necessitating the development of alternative materials that do not cause a burden on natural resources. Electronic plastic waste (EPW) like digital video discs (DVDs) and compact discs (CDs) are becoming an extreme burden to the environment due to the high quantities generated, which pose serious harm to both the environment and its inhabitants. This study presents the concept of recycling EPW and converting it into construction materials with high specifications. Using 100% EPW in place of sand and 4% unsaturated polyester resin with 20% high reactivity attapulgite (HRA) as a filler, the study generated lightweight polyester concrete (LWPC). The HRA was used after calcination at three temperatures (300, 600 and 900 °C), and for comparison, without calcination, various concentrations of the concrete components were used to produce LWPC using EPW with the optimum polyester resin percentage and HRA burning temperature. The study assessed the physical and mechanical properties of 24 mixtures of LWPC and showed the possibility of producing a novel type of high-strength, sustainable, LWPC with high properties (rapid-set, followability and ductility). The results showed that reducing the concrete’s density to below 1385 kg/m 3 and, when optimal quantities of polyester resin, EPW, and HRA were used, enhanced the workability, flowability, and mechanical properties of fresh and hardened concrete.</p

The production of novel sustainable lightweight mortar from the electronic plastic waste

Electronic plastic waste (EPW) like Compact Discs, also known as CDs or Digital Video Disc DVDs, is considered a massive challenge because of the low biodegradability of the material and exists in large quantities. Thus, using friendly alternative disposal methods for waste is becoming a significant research issue. This paper aims to explore producing lightweight mortar with fine EPW as fine aggregate to produce sustainable lightweight mortar (SLWM). To address the aim, the effect of a high percentage (50 %, 75 %) of electronic plastic waste EPW as a replacement material with sand (by weight of the sand) is compared with standard mortar (NM). Physical and mechanical characteristics such as water absorption, ultrasonic pulse velocity, bulk density, and compressive strength were measured on all specimens at 7, 28, and 90 days. The results show that there is a possibility of producing SLWM from EPW, and the 50 % replacement of sand by weight gives better results than 75 % and has a mechanical strength adequate for lightweight materials

An optimized prediction of FRP bars in concrete bond strength employing soft computing techniques

The precise estimation of the bonding strength between concrete and fiber-reinforced polymer (FRP) bars holds significant importance for reinforced concrete structures. This study introduces a new methodology that utilizes soft computing methods to enhance the prediction of FRP bars’ bonding strength. A significant compilation of experimental bond strength tests is assembled, covering various variables. Significant variables that affect bonding strength are found in the study of this database. The prediction process is optimized using soft computing methods, particularly Gene Expression Programming (GEP) and the Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR).The proposed soft computing approaches accommodate complex relationships and optimize prediction accuracy depending on the input variables. Results demonstrate its effectiveness in predicting bond strength and comparing it with existing codes and other models from the literature. The results have shown that the MOGA-EPR and the GEP models have high R2 values between 0.91 and 0.94. The proposed new models enhance the reliability and efficiency of designing and assessing FRP-reinforced concrete

Application of soft computing in predicting the compressive strength of self-compacted concrete containing recyclable aggregate

Self-compacting concrete (SCC) is a type of concrete known for its environmental benefits and improved workability. In this study, data-driven approaches were used to anticipate the compressive strength (CS) of self-compacting concrete (SCC) containing recycled plastic aggregates (RPA). A database of 400 experimental data sets was used to assess the capabilities of Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR) and Gene Expression Programming (GEP). The analysis results indicated that the proposed equations provided more accurate CS predictions than traditional approaches such as the Linear Regression model (LRM). The proposed equations achieved lower mean absolute error (MAE) and root mean square error (RMSE) values, a mean close to the optimum value (1.0), and a higher coefficient of determination (R2) than the LRM. As such, the proposed approaches can be utilized to obtain more reliable design calculations and better predictions of CS in SCC incorporating RPA

Optimized punching shear design in steel fiber-reinforced slabs:machine learning vs. evolutionary prediction models

This research paper focuses on utilizing Artificial Neural Networks (ANN), Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR), and Gene Expression Programming (GEP) to predict the punching shear strength of Steel Fibre-Reinforced Concrete (SFRC) slabs.In order to formulate predictions, research and analysis were carried out making use of a dataset, this dataset included several parameters that impact on punching shear strength, including SFRC slabs longitudinally and transversely, using ANN, GEP, and MOGA-EPR methods. The developed models exhibited very good performance, as the soft computing techniques (GEP and MOGA-EPR) achieved R² values of 0.91 to 0.93, while the ANN technique was higher at 0.95. Furthermore, two case studies were incorporated to carry out cost analyses of the models in real-world applications. It was shown that the efficiency of the Machine Learning (ML) models in reducing the costs of materials is relatively high, as they were capable of better predictions than the standard methods employed by the codes

Multiscale soft computing-based model of shear strength of steel fibre-reinforced concrete beams

Concrete is weak in tension, so steel fibres are added to the concrete members to increase shear capability. The shear capacity of steel fibre-reinforced concrete (SFRC) beams is crucial when building reinforced concrete structures. Creating a precise equation to determine the shear resistance of SFRC beams is challenging since many factors can influence the shear capacity of these beams. In addition, the precision available equations to predict the shear capacity are examined. The current research aims to examine the available equations and propose novel and more accurate model to predict the shear capacity of SFRC beams. An innovative evolutionary polynomial regression analysis (EPR- MOGA) is utilized to propose the new equation. The proposed equation offered improved prediction and increased accuracy compared to available equations, where it scored a lower mean absolute error (MAE) and root mean square error (RMSE), a mean (μ) close to the optimum value of 1.0 and a higher coefficient of determination (R 2) when a comparison with literature was conducted. Therefore, the new equation can be employed to assure more resilient and optimized design calculations due to their improved performance.</p

An optimized prediction of FRP bars in concrete bond strength employing soft computing techniques

The precise estimation of the bonding strength between concrete and fiber-reinforced polymer (FRP) bars holds significant importance for reinforced concrete structures. This study introduces a new methodology that utilizes soft computing methods to enhance the prediction of FRP bars' bonding strength. A significant compilation of experimental bond strength tests is assembled, covering various variables. Significant variables that affect bonding strength are found in the study of this database. The prediction process is optimized using soft computing methods, particularly Gene Expression Programming (GEP) and the Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR).The proposed soft computing approaches accommodate complex relationships and optimize prediction accuracy depending on the input variables. Results demonstrate its effectiveness in predicting bond strength and comparing it with existing codes and other models from the literature. The results have shown that the MOGA-EPR and the GEP models have high R2 values between 0.91 and 0.94. The proposed new models enhance the reliability and efficiency of designing and assessing FRP-reinforced concrete