State-of-the art on preparation, performance, and ecological applications of planting concrete

Liu, Yanming; Wang, Bin; Qian, Zhuohao; Yu, Jie; Shi, Tao; Fan, Yujian; Zhou, Yang; Ning, Yingjie; Zhou, Xiangming

doi:10.1016/j.cscm.2024.e03131

Cited by 12 publications

(2 citation statements)

References 69 publications

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“…As nanotechnology (NT) advances, several attempts have been made to incorporate nanomaterials into concrete to enhance its strength and functional properties. Various nanomaterials, such as graphene nanoplatelets (GrN), nano-titanium dioxide, carbon-nanofiber (CNF), nano-silica, nano-clay, and carbon-nanotube (CNT), have been incorporated into cement composites to enhance durability, strength, and functionality 3 – 5 . Nanomaterials (NMs) are commonly employed to improve concrete performance by harnessing physical processes such as nucleation and filling 6 – 8 .…”

Section: Introductionmentioning

confidence: 99%

Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches

Fawad,

Alabduljabbar,

Farooq

et al. 2024

Sci Rep

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Graphene nanoplatelets (GrNs) emerge as promising conductive fillers to significantly enhance the electrical conductivity and strength of cementitious composites, contributing to the development of highly efficient composites and the advancement of non-destructive structural health monitoring techniques. However, the complexities involved in these nanoscale cementitious composites are markedly intricate. Conventional regression models encounter limitations in fully understanding these intricate compositions. Thus, the current study employed four machine learning (ML) methods such as decision tree (DT), categorical boosting machine (CatBoost), adaptive neuro-fuzzy inference system (ANFIS), and light gradient boosting machine (LightGBM) to establish strong prediction models for compressive strength (CS) of graphene nanoplatelets-based materials. An extensive dataset containing 172 data points was gathered from published literature for model development. The majority portion (70%) of the database was utilized for training the model while 30% was used for validating the model efficacy on unseen data. Different metrics were employed to assess the performance of the established ML models. In addition, SHapley Additve explanation (SHAP) for model interpretability. The DT, CatBoost, LightGBM, and ANFIS models exhibited excellent prediction efficacy with R-values of 0.8708, 0.9999, 0.9043, and 0.8662, respectively. While all the suggested models demonstrated acceptable accuracy in predicting compressive strength, the CatBoost model exhibited exceptional prediction efficiency. Furthermore, the SHAP analysis provided that the thickness of GrN plays a pivotal role in GrNCC, significantly influencing CS and consequently exhibiting the highest SHAP value of + 9.39. The diameter of GrN, curing age, and w/c ratio are also prominent features in estimating the strength of graphene nanoplatelets-based cementitious materials. This research underscores the efficacy of ML methods in accurately forecasting the characteristics of concrete reinforced with graphene nanoplatelets, providing a swift and economical substitute for laborious experimental procedures. It is suggested that to improve the generalization of the study, more inputs with increased datasets should be considered in future studies.

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Section: Introductionmentioning

confidence: 99%

Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches

Fawad,

Alabduljabbar,

Farooq

et al. 2024

Sci Rep

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“…These composites, formed through the chemical reaction of alkali metal and silicate particles, offer a three-dimensional amorphous alumino-silicate matrix. The characteristics of AABs are influenced by several factors, including mixture rheology, desired compressive and flexural strength, modulus of elasticity, flexural toughness, ductility index, and shrinkage during drying [ 6 , 7 ]. They possess durability, acid resistance, fire resistance, and good thermal insulation properties [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Development of innovative alkali activated paste reinforced with polyethylene fibers for concrete crack repair

Iqbal,

Ashraf,

Nazar

et al. 2024

PLoS ONE

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Concrete structures are susceptible to cracking, which can compromise their integrity and durability. Repairing them with ordinary Portland cement (OPC) paste causes shrinkage cracks to appear in the repaired surface. Alkali-activated binders offer a promising solution for repairing such cracks. This study aims to develop an alkali-activated paste (AAP) and investigate its effectiveness in repairing concrete cracks. AAPs, featuring varying percentages (0.5%, 0.75%, 1%, 1.25%, 1.5%, and 1.75%) of polyethylene (PE) fibers, are found to exhibit characteristics such as strain hardening, multiple plane cracking in tension and flexure tests, and stress-strain softening in compression tests. AAP without PE fibers experienced catastrophic failure in tension and flexure, preventing the determination of its stress-strain relationship. Notably, AAPs with 1.25% PE fibers demonstrated the highest tensile and flexural strength, exceeding that of 0.5% PE fiber reinforced AAP by 100% in tension and 70% in flexure. While 1% PE fibers resulted in the highest compressive strength, surpassing AAP without fibers by 17%. To evaluate the repair performance of AAP, OPC cubes were cast with pre-formed cracks. These cracks were induced by placing steel plates during casting and were designed to be full and half-length with widths of 1.5 mm and 3 mm. AAP both with and without PE fibers led to a substantial improvement in compressive strength, reducing the initial strength loss of 30%-50% before repair to a diminished range of 2%-20% post-repair. The impact of PE fiber content on the compressive strength of repaired OPC cube is marginal, providing more flexibility in using AAP with any fiber percentage while still achieving effective concrete crack repair. Considering economic and environmental factors, along with observed mechanical enhancements, AAPs show promising potential for widespread use in concrete repair and related applications, contributing valuable insights to the field of sustainable construction materials.

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Experimental Investigation and Machine Learning Prediction of Mechanical Properties of Rubberized Concrete for Sustainable Construction

Vadivel,

Suseelan,

Karthick

et al. 2024

Sci Rep

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Concrete is widely used in civil engineering applications and the natural aggregates which used in concrete are scarce, but its demand is increasing. The disposal of rubber tyres poses a significant environmental challenge, as their decomposition releases harmful chemicals into the soil and water bodies over many years. Decomposition of tyres should be done in a smart way and hence came the emergence of mixing recycled rubber crumbs into concrete as Rubberised Concrete (RC). This paper provides an in-depth analysis of the mechanical properties of concrete such as Compressive Strength (f ck ), Tensile Strength (f t ), Flexural Strength (f cr ) of 7, 14, 28 days in replacement of fine aggregate with fine rubber (FR), and Coarse Aggregate with Coarse Rubber (CR). The results indicate that RC is more suitable for structural applications, including Reinforced Concrete columns, beams, slabs, than conventional concrete. The primary objective of the article is to explore the potential use of recycled rubber crumbs in concrete, referred to as Rubberised Concrete (RC), and to analyze its mechanical properties such as compressive strength, tensile strength, and flexural strength over different curing periods. Additionally, machine learning (ML) based prediction model has been developed for various strength characteristics of concrete mixtures at 28 days. The hyperparameter optimization using Grid Search CV with fivefold cross-validation have been performed to obtain the best hyperparameters. The model’s performance is evaluated using metrics like MAE, MSE, RMSE, and R-squared values. Results reveal varying performances among different ML algorithms for predicting flexural, tensile, and compressive strengths.

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State-of-the art on preparation, performance, and ecological applications of planting concrete

Cited by 12 publications

References 69 publications

Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches

Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches

Development of innovative alkali activated paste reinforced with polyethylene fibers for concrete crack repair

Experimental Investigation and Machine Learning Prediction of Mechanical Properties of Rubberized Concrete for Sustainable Construction

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