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Concrete, the second most used material in the world, surpassed only by water, relies on a vast amount of cement. The process of cement production emits substantial amounts of carbon dioxide (CO₂). Consequently, it is crucial to search for cement alternatives. Geopolymer concrete (GC) uses industrial by-product material instead of traditional cement, which not only reduces CO₂ emissions but also enhances concrete durability. On the other hand, the disposal of concrete waste in the landfills represents a significant environmental challenge, emphasising the urgent need for sustainable solutions. This study aimed to investigate waste concrete's best form and rate as the alternative aggregates in self-compacting and ambient-cured GC to preserve natural resources, reduce construction and demolition waste and decrease pertinent CO₂ emissions. The binding material employed in this research encompasses fly ash, slag, micro fly ash and anhydrous sodium metasilicate as an alkali activator. It also introduces the best treatment method to improve the recycled concrete aggregate (RCA) quality.

A total of25%, 50% and 100% of coarse aggregates are replaced with RCAs to cast self-compacting geopolymer concrete (SCGC) and assess the impact of RCA on the fresh, hardened and water absorption properties of the ambient-cured GC. Geopolymer slurry was used for coating RCAs and the authors examined the effect of one-day and seven-day cured coated RCA. The mechanical properties (compressive strength, splitting tensile strength and modulus of elasticity), rheological properties (slump flow, T500 and J-ring) and total water absorption of RCA-based SCGC were studied. The microstructural and chemical compositions of the concrete mixes were studied by the methods of energy dispersive X-Ray and scanning electron microscopy.

It is evident from the test observations that 100% replacement of natural aggregate with coated RCA using geopolymer slurry containing fly ash, slag, micro fly ash and anhydrous sodium metasilicate cured for one day before mixing enhances the concrete's quality and complies with the flowability requirements. Assessment is based on the fresh and hardened properties of the SCGC with various RCA contents and coating periods. The fresh properties of the mix with a seven-day curing time for coated RCA did not meet the requirements for self-compacting concrete, while this mix demonstrated better compressive strength (31.61 MPa) and modulus of elasticity (15.39 GPa) compared to 29.36 MPa and 9.8 GPa, respectively, for the mix with one-day cured coated RCA. However, incorporating one-day-cured coated RCA in SCGC demonstrated better splitting tensile strength (2.32 MPa) and water absorption (15.16%).

A potential limitation of this study on SCGC with coated RCAs is the focus on the short-term behaviour of this concrete. This limited time frame may not meet the long-term requirements for ensuring the sustained durability of the structures throughout their service life.

This paper highlights the treatment technique of coating RCA with geopolymer slurry for casting SCGC.

The purpose of this paper is to investigate the effect of the replacement of natural coarse aggregate (NCA) with different percentages of recycled coarse aggregate (RCA) on properties of low calcium fly ash (FA)-based geopolymer concrete (GPC) cured at oven temperature. Further, this paper aims to study the effect of partial replacement of FA by ground granulated blast slag (GGBS) in GPC made with both NCA and RCA cured under ambient temperature curing.

M25 grade of ordinary Portland cement (OPC) concrete was designed according to IS: 10262-2019 with 100% NCA as control concrete. Since no standard guidelines are available in the literature for GPC, the same mix proportion was adopted for the GPC by replacing the OPC with 100% FA and W/C ratio by alkalinity/binder ratio. All FA-based GPC mixes were prepared with 12 M of sodium hydroxide (NaOH) and an alkalinity ratio, i.e. sodium hydroxide to sodium silicate (NaOH:Na₂SiO₃) of 1:1.5, subjected to 90^°C temperature for 48 h of curing. The NCA were replaced with 50% and 100% RCA in both OPC and GPC mixes. Further, FA was partially replaced with 15% GGBS in GPC made with the above percentages of NCA and RCA, and they were given ambient temperature curing with the same molarity of NaOH and alkalinity ratio.

The workability, compressive strength, split tensile strength, flexural strength, water absorption, density, volume of voids and rebound hammer value of all the mixes were studied. Further, the relationship between compressive strength and other mechanical properties of GPC mixes were established and compared with the well-established relationships available for conventional concrete. From the experimental results, it is found that the compressive strength of GPC under ambient curing condition at 28 days with 100% NCA, 50% RCA and 100% RCA were, respectively, 14.8%, 12.85% and 17.76% higher than those of OPC concrete. Further, it is found that 85% FA and 15% GGBS-based GPC with RCA under ambient curing shown superior performance than OPC concrete and FA-based GPC cured under oven curing.

The scope of the present paper is limited to replace the FA by 15% GGBS. Further, only 50% and 100% RCA are used in place of natural aggregate. However, in future study, the replacement of FA by different amounts of GGBS (20%, 25%, 30% and 35%) may be tried to decide the optimum utilisation of GGBS so that the applications of GPC can be widely used in cast in situ applications, i.e. under ambient curing condition. Further, in the present study, the natural aggregate is replaced with only 50% and 100% RCA in GPC. However, further investigations may be carried out by considering different percentages between 50 and 100 with the optimum compositions of FA and GGBS to enhance the use of RCA in GPC applications. The present study is further limited to only the mechanical properties and a few other properties of GPC. For wider use of GPC under ambient curing conditions, the structural performance of GPC needs to be understood. Therefore, the structural performance of GPC subjected to different loadings under ambient curing with RCA to be investigated in future study.

The replacement percentage of natural aggregate by RCA may be further enhanced to 50% in GPC under ambient curing condition without compromising on the mechanical properties of concrete. This may be a good alternative for OPC and natural aggregate to reduce pollution and leads sustainability in the construction.

When the reinforced concrete beams are reinforced by bonding steel plates to the bottom, excessive use of steel plates will make the reinforced concrete beams become super-reinforced beams, and there are security risks in the actual use of super-reinforced beams. In order to avoid the occurrence of this situation, the purpose of this paper is to study the calculation method of the maximum number of bonded steel plates to reinforce reinforced concrete beams.

First of all, when establishing the limit failure state of the reinforced member, this paper comprehensively considers the role of the tensile steel bar and steel plate and takes the load effect before reinforcement as the negative contribution of the maximum number of bonded steel plates that can be used for reinforcement. Through the definition of the equivalent tensile strength, equivalent elastic modulus and equivalent yield strain of the tensile steel bar and steel plate, a method to determine the relative limit compression zone height of the reinforced member is obtained. Second, based on the maximum ratio of (reinforcement + steel plate), the relative limit compression zone height and the equivalent tensile strength of the tensile steel bar and steel plate of the reinforced member, the calculation method of the maximum number of bonded steel plates is derived. Then, the static load test of the test beam is carried out and the corresponding numerical model is established, and the reliability of the numerical model is verified by comparison. Finally, the accuracy of the calculation method of the maximum number of bonded steel plates is proved by the numerical model.

The numerical simulation results show that when the steel plate width is 800 mm and the thickness is 1–4 mm, the reinforced concrete beam has a delayed yield platform when it reaches the limit state, and the failure mode conforms to the basic stress characteristics of the balanced-reinforced beam. When the steel plate thickness is 5–8 mm, the sudden failure occurs without obvious warning when the reinforced concrete beam reaches the limit state. The failure mode conforms to the basic mechanical characteristics of the super-reinforced beam failure, and the bending moment of the beam failure depends only on the compressive strength of the concrete. The results of the calculation and analysis show that the maximum number of bonded steel plates for reinforced concrete beams in this experiment is 3,487 mm². When the width of the steel plate is 800 mm, the maximum thickness of the steel plate can be 4.36 mm. That is, when the thickness of the steel plate, the reinforced concrete beam is still the balanced-reinforced beam. When the thickness of the steel plate, the reinforced concrete beam will become a super-reinforced beam after reinforcement. The calculation results are in good agreement with the numerical simulation results, which proves the accuracy of the calculation method.

This paper presents a method for calculating the maximum number of steel plates attached to the bottom of reinforced concrete beams. First, based on the experimental research, the failure mode of reinforced concrete beams with different number of steel plates is simulated by the numerical model, and then the result of the calculation method is compared with the result of the numerical simulation to ensure the accuracy of the calculation method of the maximum number of bonded steel plates. And the study does not require a large number of experimental samples, which has a certain economy. The research result can be used to control the number of steel plates in similar reinforcement designs.

This paper aims to study the influence of the presence of steel and polyolefin (PO) fibers on the mechanical and durability properties of fiber and hybrid fiber-reinforced concrete (FRC and HFRC).

Hooked-end steel fibers having a length of 35 mm were applied at four different fiber content 1.0%, 1.5%, 2.0% and 2.5%, respectively. PO fibers having the length of 45 mm were also replaced with steel fibers at three different fiber content, 0.6%, 0.8% and 1.0%, to provide HFRC. The compressive, indirect tensile and flexural strengths; electrical resistivity; and water absorption were evaluated in this study.

The results showed that the addition of both steel and PO fibers led to improvements in the mechanical properties of FRC and HFRC. However, the replacement of steel fibers with PO fibers led to a slight loss in mechanical properties. Also, it was concluded that the addition of various types of fibers to concrete decreased both the electrical resistivity and water absorption compared with the control sample. Finally, distance-based approach analysis was used to select the most optimal mix designs.

According to this method, the HFRC specimen including 1.2% of steel and 0.8% of PO fibers was the most optimal mix design among all fiber-reinforced mix designs.

Three-dimensional (3D) printing is highly dependent on printing process parameters for achieving high mechanical strength. It is a time-consuming and expensive operation to experiment with different printing settings. The current study aims to propose a regression-based machine learning model to predict the mechanical behavior of ulna bone plates.

The bone plates were formed using fused deposition modeling (FDM) technique, with printing attributes being varied. The machine learning models such as linear regression, AdaBoost regression, gradient boosting regression (GBR), random forest, decision trees and k-nearest neighbors were trained for predicting tensile strength and flexural strength. Model performance was assessed using root mean square error (RMSE), coefficient of determination (R²) and mean absolute error (MAE).

Traditional experimentation with various settings is both time-consuming and expensive, emphasizing the need for alternative approaches. Among the models tested, GBR model demonstrated the best performance in predicting both tensile and flexural strength and achieved the lowest RMSE, highest R2 and lowest MAE, which are 1.4778 ± 0.4336 MPa, 0.9213 ± 0.0589 and 1.2555 ± 0.3799 MPa, respectively, and 3.0337 ± 0.3725 MPa, 0.9269 ± 0.0293 and 2.3815 ± 0.2915 MPa, respectively. The findings open up opportunities for doctors and surgeons to use GBR as a reliable tool for fabricating patient-specific bone plates, without the need for extensive trial experiments.

The current study is limited to the usage of a few models. Other machine learning-based models can be used for prediction-based study.

This study uses machine learning to predict the mechanical properties of FDM-based distal ulna bone plate, replacing traditional design of experiments methods with machine learning to streamline the production of orthopedic implants. It helps medical professionals, such as physicians and surgeons, make informed decisions when fabricating customized bone plates for their patients while reducing the need for time-consuming experimentation, thereby addressing a common limitation of 3D printing medical implants.

Punching can trigger catastrophic failures in flat slabs because of its sudden nature resulting from exceeding the shear capacity of slabs. Effect of using recycled aggregate, as an environmental-friendly alternative to traditional RC structures, on punching behavior of these slabs was not sufficiently investigated in the literature. Hence, this paper aims to experimentally study the effect of using recycled coarse aggregate (RCA) on the punching shear capacity (PSC) of RC flat slabs. The RCA is produced by crushing of waste of concrete standard cubes obtained from compression tests.

A total of 12 slab-column connection specimens with different slab thicknesses (140, 160 and 200 mm) and different RCA percentages (0%, 30% and 70%) were prepared and tested under a central point load, to test its effect on the behavior of flat slabs. The punching failure loads of the tested specimens were compared with those obtained according to the provisions of different international building codes.

Compared with natural aggregate concrete, mixes with 30% and 70% RCA experienced reductions in the compressive that did not exceed 4% and 21%, while reductions of 4% and 13% were observed for the tensile strength, respectively. The increase in the amount of RCA reduced the PSC by 0%–7%, 0%–4% and 4%–10% for slabs with a thickness of 140, 160 and 200 mm, respectively. For slabs with punching shear reinforcement (PSR), ACI 318 provided the closest estimation for the PSC by 9%, whereas EURO 2 overestimated the PSC by 25% and ECP 203 underestimated the PSC by 41%.

The provided conclusions are obtained from the conducted experimental work where a constant W/C ratio, aggregate type and a maximum aggregate size of 19 mm for the RCA were adopted.

Enhancement in the behavior of flat slabs with various thicknesses and amounts of RCA because of introducing PSR is experimentally evaluated. The failure loads of the tested slabs with recycled and normal coarse aggregates were compared against different code provisions.

The purpose of this study is to experimentally investigate the large deformation compression characteristics of fused deposition modelling (FDM)-printed poly lactic acid (PLA), considering the combined effect of infill density and strain rate, and to develop a constitutive viscoplastic model that can incorporate the infill density to predict the experimental result.

The experimental approach focuses on strain rate-dependent (2.1 × 10⁻⁴, 2.1 × 10⁻³, and 2.1 × 10⁻² s⁻¹) compression testing for varied infill densities. Scanning electron microscopy (SEM) imaging of compressed materials is used to investigate deformation processes. A hyperelastic-viscoplastic constitutive model is constructed that can predict mechanical deformations at different strain rates and infill densities.

The yield stress of PLA increased with increase in strain rate and infill density. However, higher degree of strain-softening response was witnessed for the strain rate corresponding to 2.1 × 10⁻² s⁻¹. While filament splitting and twisting were identified as the damage mechanisms at higher strain rates, matrix crazing was observed as the primary deformation mechanism for higher infill density (95%). The developed constitutive model captured yield stress and post-yield softening behaviour of FDM build PLA samples with a high R² value of 0.99.

This paper addresses the need to analyse and predict the mechanical response of FDM print polymers (PLA) undergoing extensive strain-compressive loading through a hyperelastic-viscoplastic constitutive model. This study links combined effects of the printing parameter (infill density) with the experimental parameter (strain rate).

The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets.

In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models.

The five statistical indicators like coefficient of determination (R²), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable.

The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.

The use of recycled coarse aggregate in concrete structures promotes environmental sustainability; however, performance of these structures might be negatively impacted when it is used as a replacement to traditional aggregate. This paper aims to simulate recycled concrete beams strengthened with carbon fiber-reinforced polymer (CFRP), to advance the modeling and use of recycled concrete structures.

To investigate the performance of beams with recycled coarse aggregate concrete (RCAC), finite element models (FEMs) were developed to simulate 12 preloaded RCAC beams, strengthened with two CFRP strengthening schemes. Details of the modeling are provided including the material models, boundary conditions, applied loads, analysis solver, mesh analysis and computational efficiency.

Using FEM, a parametric study was carried out to assess the influence of CFRP thickness on the strengthening efficiency. The FEM provided results in good agreement with those from the experiments with differences and standard deviation not exceeding 11.1% and 3.1%, respectively. It was found that increasing the CFRP laminate thickness improved the load-carrying capacity of the strengthened beams.

The developed models simulate the preloading and loading up to failure with/without CFRP strengthening for the investigated beams. Moreover, the models were validated against the experimental results of 12 beams in terms of crack pattern as well as load, deflection and strain.

The use of waste polyethylene terephthalate (PET) plastics derived from shredded bottles in concrete is not formalized yet, especially in reinforced members such as beams and columns. The disposal of plastic wastes in concrete is a viable alternative to manage those wastes while minimizing the environmental impacts associated to recycling, carbon dioxide emissions and energy consumption.

This paper evaluates the suitability of 2D deterministic and stochastic finite element (FE) modeling to predict the shear strength behavior of reinforced concrete (RC) beams without stirrups. Different concrete mixtures prepared with 1.5%–4.5% PET additions, by volume, are investigated.

Test results showed that the deterministic and stochastic FE approaches are accurate to assess the maximum load of RC beams at failure and corresponding midspan deflection. However, the crack patterns observed experimentally during the different stages of loading can only be reproduced using the stochastic FE approach. This later method accounts for the concrete heterogeneity due to PET additions, allowing a statistical simulation of the effect of mechanical properties (i.e. compressive strength, tensile strength and Young’s modulus) on the output FE parameters.

Data presented in this paper can be of interest to civil and structural engineers, aiming to predict the failure mechanisms of RC beams containing plastic wastes, while minimizing the experimental time and resources needed to estimate the variability effect of concrete properties on the performance of such structures.