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1. Characteristics and microstructures of the GFRP waste powder/GGBS-based geopolymer paste and concrete

   https://doi.org/10.1515/rams-2022-0005  

   Zheng, Chuji ; Wang, Jun ; Liu, Hengjuan ; GangaRao, Hota ; Liang, Ruifeng ( January 2022 , REVIEWS ON ADVANCED MATERIALS SCIENCE)

   Abstract A novel method is developed for reusing the waste glass fiber-reinforced polymer (GFRP) powder as a precursor in geopolymer production. Several activation parameters that affect the workability and strength gain of GFRP powder-based geopolymers are investigated. The results of an experimental study reveal that the early strength of GFRP powder-based geopolymer pastes develops slowly at ambient temperature. The highest compressive strength of GFRP powder-based geopolymer pastes is 7.13 MPa at an age of 28 days. The ratio of compressive strength to flexural strength of GFRP powder-based-geopolymers is lower than that of fly ash and ground granulated blast furnace slag (GGBS)-based geopolymers, indicating that the incorporation of GFRP powder can improve the geopolymer brittleness. GGBS is incorporated into geopolymer blends to accelerate the early activity of GFRP powder. The binary geopolymer pastes exhibit shorter setting times and higher mechanical strength values than those of single GFRP powder geopolymer pastes. The GGBS geopolymer concrete mixture with 30 wt% GFRP powder displayed the highest compressive strength and flexural strength values and was less brittle. The developed binary GFRP powder/GGBS-based geopolymers reduce the disadvantages of single GFRP powder or GGBS geopolymers, and thus, offer high potential as a building construction material.
2. Influence of fly ash type on mechanical properties and self-healing behavior of Engineered Cementitious Composite (ECC)

   https://doi.org/10.21012/FC9.209  

   Ma, Hui ; Qian, Shunzhi ; Li, Victor C ( January 2016 , FramCos-9)

   This paper aims to clarify the influence of different types of fly ash on the mechanical properties and self-healing behavior of Engineered Cementitious Composite (ECC). Five types of fly ash with different chemical and physical properties were used in ECC mixtures. The fly ash to cement ratio was fixed at 3.0. The compressive and uniaxial tensile tests were conducted to evaluate the influence of fly ash type on mechanical properties. The permeability test was used to assess self-healing behavior of ECCs with different types of fly ash. The microtopography and chemical characteristics of the self-healing products in the crack were observed and examined by scanning electron microscope (SEM) and energy dispersive X-ray spectroscopy (EDS). The fly ash with relatively higher calcium content and smaller particle size was found conducive to a higher compressive strength. The lower combined Al2O3 and CaO content of this fly ash, however, was found to enhance the tensile strain capacity. Furthermore, high calcium fly ash accelerates the self-healing process of ECC for the same pre-damaged level. The self-healing product was a mixed CaCO3/C-S-H system with the CaCO3 as the main ingredient.
3. Nutrient Removal Rates of Permeable Reactive Concrete

   https://doi.org/https://doi.org/10.1061/JSWBAY.0000850  

   Ramsey, Andrew J. ; Hart, Megan L. ; Kevern, John T. ( February 2018 , Journal of sustainable water in the built environment)

   Nitrogen and phosphorus contained in stormwater runoff contaminate both surface and groundwaters, causing problems for natural aquatic systems and human health. Pervious concrete specifically designed for pollutant removal, otherwise known as permeable reactive concrete (PRC), may be used as a novel component of existing infrastructure to remove nutrients from runoff. This research compares the removal and retention of dissolved, inorganic nitrate-nitrogen (NO3-N) and orthophosphate-phosphorus (PO4-P) for three PRC mixtures. The control PRC was ordinary portland cement (OPC) and was compared against other mixtures containing 25% replacement with Class C fly ash or with drinking water treatment residual waste (DWTR). Concrete specimens were jar-tested for 72 h in three different concentrations of nitrate or phosphate. The control mixture removed 60% of NO3-N and more than 80% PO4-P, and the fly ash mixture removed up to 39% of NO3-N and more than 91% PO4-P. The DWTR mixture leached NO3-N while removing more than 80% PO4-P. Linear isotherms were determined for the range of nutrient concentrations tested. Column leach tests were conducted on specimens after initial jar testing and used as an indication of removal permanence. Inorganic removal mechanisms were investigated, including crystallographic substitution, adsorption, and physical solute filtering in cement pore space.more »Results indicate PRC can be one of the leading methods to remove nitrate from surface waters and is as efficient as other methods for orthophosphate removal.« less
4. Feasibility and Compatibility of a Biomass Capsule System in Self-Healing Concrete

   https://doi.org/10.3390/ma14040958  

   Sinha, Arkabrata ; Wang, Qi ; Wei, Jianqiang ( February 2021 , Materials)

   Cracking can facilitate deteriorations of concrete structures via various mechanisms by providing ingress pathways for moisture and aggressive chemicals. In contrast to conventional maintenance methods, self-healing is a promising strategy for achieving automatic crack repair without human intervention. However, in capsule-based self-healing concrete, the dilemma between capsules’ survivability and crack healing efficiency is still an unfathomed challenge. In this study, the feasibility of a novel property-switchable capsule system based on a sustainable biomass component, polylactic acid, is investigated. Capsules with different geometries and dimensions were studied focusing on the compatibility with concrete, including survivability during concrete mixing, influence on mortar and concrete properties, and property evolution of the capsules. The results indicate that the developed elliptical capsules can survive regular concrete mixing with a survival ratio of 95%. In concrete containing 5 vol.% of gravel-level capsules, the compressive strength was decreased by 13.5% after 90 days, while the tensile strength was increased by 4.8%. The incorporation of 2 vol.% of sand-level capsules did not impact the mortar strength. Degradation and switchable properties triggered by the alkaline matrix of cement were observed, revealing the potential of this novel biomass capsule system in achieving both high survivability and self-healing efficiency in concrete.
5. Predicting Concrete’s Strength by Machine Learning: Balance between Accuracy and Complexity of Algorithms

   https://doi.org/10.14359/51728128  

   Ouyang, N ; Song, Y ; Li, Y ; Wu, F ; Yu, H ; Wang, Y ; Sant, G ; Bauchy, M ( November 2020 , ACI Materials Journal)

   The properties of concretes are controlled by the rate of reaction of their precursors, the chemical composition of the binding phase(s), and their structure at different scales. However, the complex and multiscale structure of the cementitious hydrates and the dissimilar rates of numerous chemical reactions make it challenging to eluci- date such linkages. In particular, reliable predictions of strength development in concretes remain unavailable. As an alternative route to physics- or chemistry-based models, machine learning (ML) offers a means to develop powerful predictive models for materials using existing data. Here, it is shown that ML models can be used to accurately predict concrete’s compressive strength at 28 days. This approach relies on the analysis of a large data set (>10,000 observations) of measured compressive strengths for industrially produced concretes, based on knowledge of their mixture proportions. It is demonstrated that these models can readily predict the 28-day compressive strength of any concrete based merely on the knowledge of the mixture proportions with an accuracy of approximately ±4.4 MPa (as captured by the root- mean-square error). By comparing the performance of select ML algorithms, the balance between accuracy, simplicity, and inter- pretability in ML approaches is discussed.