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Utilising industrial waste, such as fly ash (FA) and bagasse ash (BA), reduces waste management and increases mechanical strength. Concrete is modified with FA and BA in the cool bonded method of concrete preparation.

The study used to partially replace cement with BA powder at proportions 0, 5, 10, 15, 20 and 25% and coarse aggregates are replaced with FA aggregates made with FA and cement using a cold-bonded technique at proportions 0–25%. FA aggregates were made at 10:90, 15:85, 20:80 and 25:75 proportions of cement and FA. The FA aggregates at the best proportion 15:85 was selected as a coarse aggregate by conducting tests like specific gravity, crushing value, impact value and water absorption tests.

The addition of 30% content decreases porosity by 21% and increases strength significantly at 28 days. Microstructure evolution is carried out to identify material behaviour.

Mechanical and durable properties such as flexural strength, tensile strength, water absorption test, acid and alkaline tests are conducted on M50 grade concrete after 3–28 days of curing.

The purpose of this paper is to investigate the potential use of construction and demolition waste materials (C&DWM) as an alternative for natural fine aggregates (NFA), in view to solve the disposal problems caused due to landfills. In addition, to evaluate its suitability as a sustainable material, mechanical and durability properties have been performed on different proportions of concrete blending and the results recorded were compared with the reference concrete values.

In this research, the NFA were replaced at the proportion of 25%, 50%, 75% and 100% of C&DWM with a constant slump range of 130 mm–150 mm. This parameter will assess the consistency of the fresh concrete during transportation process. The characteristics of the end product was evaluated through various tests conducted on hardened concrete samples, namely, compressive strength, flexural strength, depth of penetration of water under pressure, rapid chloride penetration test, carbonation test and ultrasonic pulse velocity (UPV) test. All results recorded were compared with the reference concrete values.

The results demonstrated that the use of C&DWM in concrete portrayed prospective characteristics that could eventually change the concept of sustainable concrete. It was noted that the compressive and flexural strength decreased with the addition of C&DWM, but nevertheless, a continuous increase in strength was observed with an increase in curing period. Moreover, the increase in rapid chloride penetration and decrease in UPV over time period suggested that the concrete structure has improved in terms of compactness, thus giving rise to a less permeable concrete. The mechanical tests showed little discrepancies in the final results when compared to reference concrete. Therefore, it is opined that C&DWM can be used effectively in concrete.

This study explores the possible utilisation of C&DWM as a suitable surrogative materials in concrete in a practical perspective, where the slump parameter will be kept constant throughout the experimental process. Moreover, research on this method is very limited and is yet to be elaborated in-depth. This approach will encourage the use of C&DWM in the construction sector and in the same time minimise the disposal problems caused due to in landfills.

The quest to reduce the cost of concrete which is a major construction input has prompted investigations into assessing the suitability of alternative sources of conventional materials. This paper aims to report the compressive strength and workability of lateritic gravel used as all-in aggregate for concrete production.

Three prescribed mixes from all-in aggregate concrete were compared with concrete from lateritic gravel. The paper investigated the variation in strength of four different mixes – 100: 0, 90: 10, 80: 20 and 70: 30 – when portions of the lateritic gravel were replaced with pit sand, respectively, using varying water cement ratios to achieve optimal workability.

The density and compressive strength of each cube was measured on the 7th and 28th test dates. An increase in slump and compressive strength was observed in the lateritic concrete, as portions of the lateritic gravel were replaced with sand. However, the rate of increase in the compressive strength tended to decrease with increase in part replacement of lateritic gravel with sand indicating that there was a threshold of percentage of sand increase after which the compressive strengths are likely to decrease. This work never reached this threshold, but it is estimated to be about 40 per cent.

Investigations focused on lateritic gravel sampled from two sites to represent samples from both the forest and savannah belt.

Lateritic gravel can be used as all-in aggregate for non-structural concrete.

The compressive strengths achieved were better than those for the available normal all-in aggregate used.

This paper presents the effects of replacing fine aggregate (FA) with waste foundry sand (WFS) in natural aggregate and construction waste aggregate concrete specimens without and with superplasticizer (SP), silica fume (SF) and fiber (F) to solve the disposal problems of various wastes along with saving the environment. This study aims to investigate the effect of construction waste, WFS along with additives on the stress-strain behavior and development of compressive strength with age.

The various concrete specimen were prepared in mix proportion of 1: 2: 4 (cement (C): sand: coarse aggregate). The water-cement ratio of 0.5 (decreased by 10% for samples containing SP) to grading 1: 2: 4 under air-dry condition was adopted in the preparation of concrete specimens. The compressive strength of various concrete specimen were noticed for 3, 7 and 28 days by applying load through universal testing machine.

Upon adding construction and demolition waste aggregates, the compressive strength of concrete after 28 days was comparable to that of the control concrete specimen. An enhancement in the value of compressive strength is perceived when FA is replaced with WFS to the extent of 10%, 20% and 30%. If both construction and demolition waste aggregate and WFS replacing FA are used, the compressive strength increases. When FA is interchanged with WFS in natural aggregate or construction demolition waste aggregate concrete including usage of SF or F, the compressive strength improves significantly. Further, when construction and demolition waste aggregate and WFS replacing FA including SP are used, the compressive strength improves marginally compared to that of control specimen. The rate of strength development with age is observed to follow similar trend as in control concrete specimen. Therefore, construction and demolition waste and or WFS can be used effectively in concrete confirming an improvement in strength.

The utilization of these wastes in concrete will resolve the problem of their disposal and save the environment.

Recycling construction waste is a promising way towards sustainable development in construction. Recycled aggregate (RA) is obtained from demolished concrete structures, laboratory crushed concrete, concrete waste at a ready mix concrete plant and the concrete made from RA is known as RA concrete. The purpose of this study is to apply multiple linear regressions (MLRs) and artificial neural network (ANN) to predict the mechanical properties, such as compressive strength (CS), flexural strength (FS) and split tensile strength (STS) of concrete at the age of 28 days curing made completely from the recycled coarse aggregate (RCA).

MLR and ANN are used to develop a prediction model. The model was developed in the training phase by using data from a previously published research study and a developed model was further tested by obtaining data from laboratory experiments.

ANN shows more accuracy than MLR with an R²-value of more than 0.8 in the training phase and 0.9 in a testing phase. The high R²-value indicates strong relation between the actual and predicted values of mechanical properties of RCA concrete. These models will help construction professionals to save their time and cost in predicting the mechanical properties of RCA concrete at 28 days of curing.

ANN with rectified linear unit transfer function and backpropagation algorithm for training is used to develop a prediction model. The outcome of this study is the prediction model for CS, FS and STS of concrete at 28 days of curing.

Due to the high demand of concrete, significant volume of natural resources is required, including virgin aggregates. Many studies have shown that the production of concrete has one of the highest CO₂ levels. Although efforts are in place to recycle, enormous effects on landfill and the wider environment remain. Research has suggested the importance of reusing construction and demolition waste such as aggregate for use in recycled concrete. However, robust construction and demolition waste reduction strategies are required. There have been numerous researches on the use of recycled concrete and its management in the construction industry. This paper further consolidates this position.

This paper exhibits the barriers and benefits of using recycled aggregates for construction industry. This is achieved via reviewing the current construction and demolition waste reduction strategies used mainly in three countries: the UK, Australia and Japan. These countries were selected since they seemingly have similar construction industry and environment. Subsequently, evolving barriers and benefits of using recycled aggregates for construction industry are also reviewed and discussed. And to support such focus, robust construction and demolition waste reduction strategies will be advocated.

The findings are summarized as follows. The recycling construction and demolition waste could have a positive net benefit compared to the procurement and production of virgin aggregate materials with the same properties. This is not only financially beneficial but also environmentally viable, as fewer resources would be required to produce the same aggregate material. There are effective ways to achieve a high recycle rate target, as demonstrated by Japan. The implementation of a similar recycling process could be implemented globally to achieve a more effective recycle rate through the help of governments at all levels. By creating awareness about the financial and environmental benefits of using recycled aggregate products, large recycling companies can be also enticed to follow suit.

The findings from this paper can ultimately support the construction industry to further consolidate and advocate the use of recycled aggregates.

To achieve the research aim, this paper reviews some of the main sustainability factors of recycled aggregates (including coarse and fine aggregates) and provides comparison to virgin aggregates.

This paper aims to review the effect of using recycled aggregates (RA) on the properties of recycled aggregate concrete (RAC) following the steady rise in global demand for concrete and the large generation of construction and demolition waste.

This study reviewed relevant literature of research work carried out by previous researchers, leading to a deeper understanding of the properties of both RA and RAC. The properties of RA and RAC reported in the various studies were then compared to their corresponding natural aggregate (NA) and natural aggregate concrete, as well as the specifications provided in different codes of practice. In addition, the mix design methods appropriate to RAC and the cost implication of using RA were reviewed.

Findings show that the contribution of RA to strength appears inferior in comparison to NA. The shortcoming is attributed to the mortar attached to the RA, which raises its water absorption capacity and lowers its density relative to those of NA. However, it has been reported that the use of regulated quantity of RA, new mixing and proportioning methods, the addition of admixtures and strengthening materials such as steel fibres, can improve both mechanical and durability properties of RAC. Cost evaluation also showed that some savings can be realized by using RA instead of NA.

This research serves as a guide for future works and suggests that the use of RA as aggregate in new concrete is technically possible, depending on the mix design method adopted.

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.

Currently, many experimental studies on the properties and behavior of rubberized concrete are available in the literature. These findings have motivated scholars to propose models for estimating some properties of rubberized concrete using traditional and advanced techniques. However, with the advancement of computational techniques and new estimation models, selecting a model that best estimates concrete's property is becoming challenging.

In this study, over 1,000 different experimental findings were obtained from the literature and used to investigate the capabilities of ten different machine learning algorithms in modeling the hardened density, compressive, splitting tensile, and flexural strengths, static and dynamic moduli, and damping ratio of rubberized concrete through adopting three different prediction approaches with respect to the inputs of the model.

In general, the study's findings have shown that XGBoosting and FFBP models result in the best performances compared to other techniques.

Previous studies have focused on the compressive strength of rubberized concrete as the main parameter to be estimated and rarely went into other characteristics of the material. In this study, the capabilities of different machine learning algorithms in predicting the properties of rubberized concrete were investigated and compared. Additionally, most of the studies adopted the direct estimation approach in which the concrete constituent materials are used as inputs to the prediction model. In contrast, this study evaluates three different prediction approaches based on the input parameters used, referred to as direct, generalized, and nondestructive methods.

Utilization of sustainable materials is a global demand in the construction industry. Hence, this study aims to integrate waste management and artificial intelligence by developing an artificial neural network (ANN) model to predict the compressive strength of green concrete. The proposed model allows the use of recycled coarse aggregate (RCA), recycled fine aggregate (RFA) and fly ash (FA) as partial replacements of concrete constituents.

The model is constructed, trained and validated using python through a set of experimental data collected from the literature. The model’s architecture comprises an input layer containing seven neurons representing concrete constituents and two neurons as the output layer to represent the 7- and 28-days compressive strength. The model showed high performance through multiple metrics, including mean squared error (MSE) of 2.41 and 2.00 for training and testing data sets, respectively.

Results showed that cement replacement with 10% FA causes a slight reduction up to 9% in the compressive strength, especially at early ages. Moreover, a decrease of nearly 40% in the 28-days compressive strength was noticed when replacing fine aggregate with 25% RFA.

The research is limited to normal compressive strength of green concrete with a range of 25 to 40 MPa.

The developed model is designed in a flexible and user-friendly manner to be able to contribute to the sustainable development of the construction industry by saving time, effort and cost consumed in the experimental testing of materials.

Green concrete containing wastes can solve several environmental problems, such as waste disposal problems, depletion of natural resources and energy consumption.

This research proposes a machine learning prediction model using the Python programming language to estimate the compressive strength of a green concrete mix that includes construction and demolition waste and FA. The ANN model is used to create three guidance charts through a parametric study to obtain the compressive strength of green concrete using RCA, RFA and FA replacements.