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The utilization of industrial and agricultural by-products for the production of alkali activated concrete (AAC) has the potential to yield significant benefits towards sustainability goals. To be a viable material, the construction industry requires a construction material that achieves the requisite strength and the other property requirements as specified in codes and standards while demonstrating improved sustainability criteria. Fly ash and Rice Husk Ash (RHA) are abundantly available waste products, principally located in Asian countries. Currently, a significant proportion of these materials are disposed of in landfills, lagoons and rivers but offer potential to utilize in AAC. Hence, the identification of variables associated with fly ash and fly ah-RHA blended AAC by utilizing fly ash and RHA is vital. This study quantifies the environmental and economic factors by assessing the Greenhouse gas (GHG) emission, environmental impacts and benefits, and cost analysis of utilizing fly ash and RHA in AAC compared to Portland Cement (PC) concrete. Alkaline activator is a key component responsible for the highest GHG emission, cost and environmental impact amounts obtained for fly ash geopolymer and blended alkali-activated concrete compared with PC concrete. Alkali activators contribute to 74% of the total GHG emission, while heat curing contributed only 9% to the total GHG emission. The addition of 10% RHA to alkali-activated concrete showed a slight benefit for the analysis. Utilization of waste fly ash and RHA is responsible for providing significant benefits in terms of fresh and marine water ecotoxicity by avoiding waste disposal at the dumpsites, rivers, and storage lagoons., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Despite extensive in-depth research into high calcium fly ash geopolymer concretes and a number of proposed methods to calculate the mix proportions, no universally applicable method to determine the mix proportions has been developed. This paper uses an artificial neural network (ANN) machine learning toolbox in a MATLAB programming environment together with a Bayesian regularization algorithm, the Levenberg-Marquardt algorithm and a scaled conjugate gradient algorithm to attain a specified target compressive strength at 28 days. The relationship between the four key parameters, namely water/solid ratio, alkaline activator/binder ratio, Na 2 SiO 3 /NaOH ratio and NaOH molarity, and the compressive strength of geopolymer concrete is determined. The geopolymer concrete mix proportions based on the ANN algorithm model and contour plots developed were experimentally validated. Thus, the proposed method can be used to determine mix designs for high calcium fly ash geopolymer concrete in the range 25-45 MPa at 28 days. In addition, the design equations developed using the statistical regression model provide an insight to predict tensile strength and elastic modulus for a given compressive strength.

Buried steel infrastructure can be a source of iron ions for bacterial species, leading to microbiologically influenced corrosion (MIC). Localized corrosion of pipelines due to MIC is one of the key failure mechanisms of buried steel pipelines. In order to better understand the mechanisms of localized corrosion in soil, semisolid agar has been developed as an analogue for soil. Here, Pseudomonas fluorescens has been introduced to the system to understand how bacteria interact with steel. Through electrochemical testing including open circuit potentials, potentiodynamic scans, anodic potential holds, and electrochemical impedance spectroscopy it has been shown that P. fluorescens increases the rate of corrosion. Time for oxide and biofilms to develop was shown to not impact on the rate of corrosion but did alter the consistency of biofilm present and the viability of P. fluorescens following electrochemical testing. The proposed mechanism for increased corrosion rates of carbon steel involves the interactions of pyoverdine with the steel, preventing the formation of a cohesive passive layer, after initial cell attachment, followed by the formation of a metal concentration gradient on the steel surface.