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The purpose of this paper is to check the feasibility of using biomaterial such as of Phragmites-Australis (PA) in cement paste to achieve sustainable building materials.

In this study, cement pastes were prepared by adding locally produced PA fibers in four different volumes: 0%, 0.5%, 1% and 2% for a duration of 180 days. Bottles and prisms were subjected to chemical shrinkage (CS), drying shrinkage (DS), autogenous shrinkage (AS) and expansion tests. Besides, prism specimens were tested for flexural strength and compressive strength. Furthermore, a mathematical model was proposed to determine the variation length change as function of time.

The experimental findings showed that the mechanical properties of cement paste were significantly improved by the addition of 1% PA fiber compared to other PA mixes. The effect of increasing the % of PA fibers reduces the CS, AS, DS and expansion of cement paste. For example, the addition of 2% PA fibers reduces the CS, expansion, AS and DS at 180 days by 36%, 20%, 13% and 10%, respectively compared to the control mix. The proposed nonlinear model fit to the experimental data is appropriate with R2 values above 0.92. There seems to be a strong positive linear correlation between CS and AS/DS with R2 above 0.95. However, there exists a negative linear correlation between CS and expansion.

The PA used in this study was obtained from one specific location. This can exhibit a limitation as soil type may affect PA properties. Also, one method was used to treat the PA fibers.

The utilization of PA fibers in paste may well reduce the formation of cracks and limit its propagation, thus using a biomaterial such as PA in cementitious systems can be an environmentally friendly option as it will make good use of the waste generated and enhance local employment, thereby contributing toward sustainable development.

To the authors best knowledge, there is hardly any research on the effect of PA on the volume stability of cement paste. Therefore, the research outputs are considered to be original.

John Newton, Texaco Ltd, gives Chemrecon delegates an A‐to‐Z insight into lube‐oil blending, application and environmentally safe usage

The purpose of this paper is to provide a modeling perspective relevant to the use of cathodic prevention (CPre) for unconventional concrete in salt‐laden environment.

Based on the experimentally obtained concrete resistivity and chloride diffusion coefficient data, numerical studies with the Nernst‐Planck equations were conducted to investigate the influence of applied voltage (magnitude, direction, and interruption), surface chloride concentration, and concrete mix design on the effectiveness of cathodic prevention and the distribution of ionic species in protected concrete.

The modeling results revealed that the direction of applied electric voltage has significant effect on the distributions of electrical potential and hydroxyl ions in the reinforced concrete, confirming the benefits of cathodic prevention in significantly increasing hydroxyl concentration near rebar and in slowing down the ingress of chloride ingress into concrete. The performance of intermittent CPre was found to be constrained by the variations in concrete resistance from the anode to the cathode. The model was also useful in illustrating the temporal and spatial evolutions on rebar surface in terms of oxygen, hydroxyl and chloride concentrations and electrical potential of top rebar, as well as such evolutions in concrete domain in terms of concrete resistivity and current density for each mix design.

The results reported herein shed light on the fundamental processes defining the performance of CPre for new unconventional concrete in salt‐laden environment.

This study aims to present the results of an experimental evaluation of low (M30), mid (M40) and high (M50) grade self-compacting concrete (SCC) with three nominal maximum aggregate sizes (NMAS), namely, 20 mm, 16 mm and 12.5 mm, with Bailey gradation (BG) in comparison with Indian standard gradation (ISG).

This study was conducted in a laboratory by testing the characteristics of fresh and hardened properties of self-compacting concrete.

Rheological and mechanical properties of SCC were evaluated in detail and according to the results, a concrete sample containing lower NMAS with BG demonstrated improvement in modulus of elasticity and compressive strength, while improving the rheological properties as well. Meanwhile, SCC demonstrated poor performance in split tensile and flexural strengths with lower NMAS gradations and a direct correlation was evident as the increase in NMAS caused an increase in the strength and vice-versa.

Upon comparison of BG with ISG, it was revealed that BG mixes succeeded to demonstrate superior performance. From the material optimization, rheological and mechanical performance study, it is recommended that BG with NMAS 16 mm can be used for conventional SCC.

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.

The purpose of this paper was mainly to select one of the three types of coatings for protection of steel used as reinforcement in composite pipes (thin steel shell covered by cement-mortar) subjected to chloride exposure. To achieve this target, an attempt was made to develop a simple methodology for evaluating the performance of corrosion protection measures in terms of chloride threshold level (CTL) and corrosion initiation time (TI).

Bare, epoxy, red oxide and zinc primer-coated steel strips were embedded in cement mortar with sand/cement and water/cement ratios of 2 and 0.5 (by mass), respectively, to prepare the specimens which were exposed to chloride solution having a high concentration of 10 per cent NaCl. For determining the amounts of the water-soluble chloride diffused inside the specimens, powdered samples of mortar were collected from two different depths from the exposed surface of specimens on completion of each of the four different exposure times. The corrosion current densities were determined at two different stages. A step-by-step procedure for calculating CTL and TI using the measured chloride contents and corrosion current densities was established with the help of relevant information available in the literature.

Based on the comparison of the values of CTL and TI calculated for bare steel and steel with all three types of coatings, utilizing the experimental data and the proposed calculation procedure, the epoxy-coated steel was found to have the best performance.

This research has resulted into development of a simple methodology for evaluation of the performance of protective measures against corrosion of steel embedded in mortar or concrete exposed to chloride-bearing environment.

The purpose of this paper is to present the modeling of statistical variation of experimental data using the design of experiments method to optimize the formulation of a high performance concrete (HPC) using materials that are locally available in Algeria. For this, two mineral additions (natural pozzolana and limestone filler [LF]) were used. Both additions are added by substitution of cement up to 25%. To better appreciate the effect of replacing a part of cement by natural pozzolana and LF and to optimize their combined effect on the characteristics of HPC, an effective analytical method is therefore needed to reach the required objective.

The experimental part of the study consisted of substituting a portion of cement by various proportions of these additions to assess their effects on the physico-mechanical characteristics of HPC. A mixture design with three factors and five levels was carried out. The JMP7 software was used to provide mathematical models for the statistical variation of measured values and to perform a statistical analysis. These models made it possible to show the contribution of the three factors and their interactions in the variation of the response.

The mixture design approach made it possible to visualize the influence of LF and pozzolanic filler (PF) on the physico-mechanical characteristics of HPC, the developed models present good correlation coefficients (R² = 0.82) for all studied responses. The obtained results indicated that it is quite possible to substitute a part of cement with LF and PF in the formulation of a HPC. Thanks to the complementary effect between the two additions, the workability could be improved and the strengths drop could be avoided in the short, medium and long term. The optimization of mixture design factors based on the mathematical models was carried out to select the appropriate factors combinations; a good agreement between the experimental results and the predicted results was obtained.

The coefficient of PF in Cs₂₈ model is closer to that of LF than in Cs₇ model, thanks to the complementary effect between LF and PF at the age of 28 days. It was found that the optimal HPC14 concrete (10%LF–5%PF) provides the best compromise between the three responses. It is also worth noting that the use of these two local materials can reduce the manufacturing costs of HPC and reduce carbon dioxide emissions into the atmosphere. This can be an important economic and environmental alternative.

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.

This paper offers a brief review of the present use of scanning electron microscopy (SEM) in concrete studies, from the perspective of how research in materials science is translated into applications in construction engineering. It describes the scope of present use of the method, and attempts a prospective for the near future in areas where more work could make productive use of the technology. Selected case studies have also been discussed. The electron microscope has been used as a research tool in understanding the root cause of the differing performance of various types of concrete under various conditions, a development tool in making better concrete, and a diagnosis tool on problems like cracking of concrete. The paper also explains how sample preparation affects the type and quality of information which the SEM can produce.

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.