A comprehensive assessment of mechanical and environmental properties of green concretes produced using recycled concrete aggregates and supplementary cementitious material

Ulucan, Muhammed; Alyamac, Kursat Esat

doi:10.1007/s11356-023-29197-y

Cited by 12 publications

(1 citation statement)

References 79 publications

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“…Therefore, the prospect of using green concretes produced using supplementary cementitious material and recycled concrete aggregates in the engineering sector has been widely tested. Research studies such as Ulucan and Alyamac ( 2023 ) have evaluated not only its mechanical properties, but also environmental factors such as energy consumption, global warming potential, and waste generation, which have confirmed the high potential for use of RA in concrete manufacturing.…”

Section: Introductionmentioning

confidence: 90%

LCA applied to comparative environmental evaluation of aggregate production from recycled waste materials and virgin sources

Linares,

López-Uceda,

Piccinali

et al. 2024

Environ Sci Pollut Res

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Nowadays, all productive sectors, including the construction industry, are facing the challenge of reducing their environmental impact. To achieve this objective, numerous actions are being carried out to access greater levels of environmental and economic sustainability. Techniques as Life Cycle Assessment contribute to quantifying environmental impacts, promoting a circular economy in a sector that consumes a high volume of resources, materials, and energy while generating large amounts of gaseous, liquid, or solid emissions. The present study aims to deepen our understanding of aspects that demonstrate the benefits of using RA instead of natural aggregates. This study not only quantifies the environmental impact but also explores the effects of potential improvements in the productive system and their impact on reducing environmental harm. The Life Cycle Assessment methodology is applied to quantify and compare the environmental impacts generated in the production of a ton of mixed recycled aggregates (MRA) from construction and demolition wastes, based on the data provided by plant managers. This is compared to the environmental impacts generated in the production of one ton of natural aggregates extracted from a quarry. The results revealed that the production of mixed recycled aggregate is more environmentally beneficial, confirming a reduction of 70.66% in environmental impacts during the production of recycled aggregates, in comparison to the natural aggregates extraction. Furthermore, the economic analysis demonstrates the economic advantage since the cost of producing recycled aggregates is over 30% cheaper than natural aggregates, being more competitive even when the transportation distances from the plant to the work sites exceed those of natural aggregates. Graphical Abstract

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Section: Introductionmentioning

confidence: 90%

LCA applied to comparative environmental evaluation of aggregate production from recycled waste materials and virgin sources

Linares,

López-Uceda,

Piccinali

et al. 2024

Environ Sci Pollut Res

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Optimizing sustainable concrete compressive strength prediction: A new particle swarm optimization‐based metaheuristic approach to neural network modeling for circular economy and disaster resilience

Ulucan,

Gunduzalp,

Yildirim

et al. 2024

Structural Concrete

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This study aims to predict the final‐age compressive strengths of sustainable concrete series produced using different aggregate types with high accuracy using an artificial intelligence model and to reduce environmental degradation and conserve rapidly decreasing natural resources within the scope of sustainable development and circular economy goals. For this purpose, 45 different sustainable concrete series containing all‐natural, recycled, natural, and recycled concrete aggregates were produced and subjected to compressive strength tests at 7 and 28 days. In addition, this study also considers whether deep neural network models, which have gained popularity in recent years but have high time and resource costs, or shallow models are more suitable for determining the compressive strength values with high accuracy. For this purpose, a method is proposed that can automatically generate the optimal neural network model using a metaheuristic approach that eliminates the human factor. For this purpose, the proposed method was extensively compared with different classical machine learning algorithms. The proposed method predicted the 7 and 28‐day compressive strength with a coefficient of determination of 0.999 and presented better compressive strength predictions than all other algorithms. Considering the number of buildings to be constructed after earthquakes, the widespread use of concrete, and the importance of compressive strength, the proposed method will likely provide significant gains within sustainable development, circular economy, and disaster risk reduction.

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Valorization of recycled aggregates to eco-efficient lightweight self-compacting mortars: Studies on microstructure, mechanical, durability, environmental, and economic properties

Acikgenc Ulas,

Culcu,

Ulucan

2024

Construction and Building Materials

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A comprehensive assessment of mechanical and environmental properties of green concretes produced using recycled concrete aggregates and supplementary cementitious material

Cited by 12 publications

References 79 publications

LCA applied to comparative environmental evaluation of aggregate production from recycled waste materials and virgin sources

LCA applied to comparative environmental evaluation of aggregate production from recycled waste materials and virgin sources

Optimizing sustainable concrete compressive strength prediction: A new particle swarm optimization‐based metaheuristic approach to neural network modeling for circular economy and disaster resilience

Valorization of recycled aggregates to eco-efficient lightweight self-compacting mortars: Studies on microstructure, mechanical, durability, environmental, and economic properties

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