CO2 emission assessment of lightweight aggregate concrete using artificial lightweight and bottom ash particles

Jung, Yeon‐Back; Yang, Keun–Hyeok

doi:10.1007/s10163-022-01469-8

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…The latter are usually hardened by sintering at temperatures of 900-1200 °C [28,40]. The production process of the sintered LWA causes emissions of about 0.05-0.06 kg CO 2 /kg [41,42]. Hence, replacement of the C-LWA with sintered LWA would actually increase the emissions of concrete by about 10-12 kg CO 2 /m 3 , i.e.…”

Section: Negative Emissions Of C-lwamentioning

confidence: 99%

Cold-bonded biochar-rich lightweight aggregates for net-zero concrete

Wyrzykowski

Toropovs

Winnefeld

et al. 2023

Preprint

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An emerging strategy to remove CO2 from the atmosphere and compensate for the greenhouse-gas emissions of cement and concrete is based on incorporating biochar into concrete. With this approach, concrete can be turned into a functional carbon sink (C-sink). Until now, biochar has been used without modification to replace part of the cement or of the aggregates in concrete. However, this technology comes with a number of practical problems, which include the high water absorption of the biochar (due to its high specific surface) and hazards (dust, risk of combustion). In this paper we present an alternative approach in which biochar is first processed into lightweight aggregates in a cold-bonding process. To this end, biochar is pelletized together with a small amount of hydraulic binder and with water and forms round pellets that further harden with hydration time. In this way, carbon-rich lightweight aggregates (C-LWA) are obtained that are easier to handle than the pure biochar. The C-LWA pellets have similar porosity and strength as conventional LWA and can be used for similar applications. Yet, the CO2 emissions from sintering traditional LWA are avoided and the C-LWA are instead an effective C-sink. We demonstrate that it is possible to incorporate in the pellets and eventually in the concrete a sufficient amount of carbon to compensate for the original emissions of concrete. The net-zero emissions concrete obtained with this approach possesses mechanical performance sufficient for typical structural applications in buildings.

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Section: Negative Emissions Of C-lwamentioning

confidence: 99%

Cold-bonded biochar-rich lightweight aggregates for net-zero concrete

Wyrzykowski

Toropovs

Winnefeld

et al. 2023

Preprint

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“…The lower dead loads eventually lower the structural members' design actions that help reduce the cross-section and reinforcement requirements [11]. Due to the lower self-weight, the cross-section of the structural members also decreases, leading to a lower use of the cement, which is a primary contributor of greenhouse gases as well as aggregate [12]. Furthermore, waste of the different sectors is utilized as lightweight aggregate such as crumb rubber, electric arch furnace slag, fly ash, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, waste of the different sectors is utilized as lightweight aggregate such as crumb rubber, electric arch furnace slag, fly ash, etc. [12][13][14][15]. Artificial lightweight aggregate is also manufactured using local clays and fly ash, which is replacing the natural aggregate in order to reduce the usage of natural reserves [12,16,17].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Predictive Modeling of Sustainable Lightweight Aggregate Concrete

et al. 2022

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Nowadays, lightweight aggregate concrete is becoming more popular due to its versatile properties. It mainly helps to reduce the dead loads of the structure, which ultimately reduces design load requirements. The main challenge associated with lightweight aggregate concrete is finding an optimized mix per requirements. However, the conventional material design of this composite is quite costly, time-consuming, and iterative. This research proposes a simplified methodology for the mix designing of structural and non-structural lightweight aggregate concrete by incorporating machine learning. For this purpose, five distinct machine learning algorithms, support vector machine (SVM), artificial neural network (ANN), decision tree (DT), Gaussian process of regression (GPR), and extreme gradient boosting tree (XGBoost) algorithms, were investigated. For the training, testing, and validation process, a total of 420 data points were collected from 43 published journal articles. The performance of models was evaluated based on statistical performance indicators. Overall, 11 input parameters, including ingredients of the concrete mix and aggregate properties were entertained; the only output parameter was the compressive strength of lightweight concrete. The results revealed that the GPR model outperformed the remaining four machine learning models by attaining an R2 value of 0.99, RMSE of 1.34, MSE of 1.79, and MAE of 0.69. In a nutshell, these simplified modern techniques can be employed to make the design of lightweight aggregate concrete easy without extensive experimentation.

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