Compressive strength and elastic modulus of RBAC: An analysis of existing data and an artificial intelligence based prediction

Li, Lin; Xu, Jinjun; Yuan, Jialiang; Yong, Yu

doi:10.1016/j.cscm.2023.e02184

Cited by 14 publications

(1 citation statement)

References 82 publications

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“…Elastic modulus (E) is one of the key parameters characterizing the properties of rock, and the accurate estimation of the E of rock is essential for the safe construction of geotechnical engineering [1][2][3][4][5]. The E of rock can be determined by situ and laboratory tests, and the test process should comply with a series of operating specifications.…”

Section: Introductionmentioning

confidence: 99%

Developing Hybrid DMO-XGBoost and DMO-RF Models for Estimating the Elastic Modulus of Rock

Lin,

Liu,

Zhao

et al. 2023

Mathematics

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Accurate estimation of the elastic modulus (E) of rock is critical for the design of geotechnical projects such as mining, slopes, and tunnels. However, the determination of rock mechanical parameters usually involves high budget and time requirements. To address this problem, numerous researchers have developed machine learning models to estimate the E of rock. In this study, two novel hybrid ensemble learning models were developed to estimate the E of rock by optimizing the extreme gradient boosting (XGBoost) and random forest (RF) algorithms through the dwarf mongoose optimization (DMO) approach. Firstly, 90 rock samples with porosity, dry density, P-wave velocity, slake durability, and water absorption as input indicators were collected. Subsequently, the hyperparameters of XGBoost and RF were tuned by DMO. Based on the optimal hyperparameters configuration, two novel hybrid ensemble learning models were constructed using the training set (80% of the data). Finally, the performance of the developed models was evaluated by the coefficient of determination (R2 score), root mean squared error (RMSE), mean absolute error (MAE), and variance accounted for (VAF) on the test set (20% of the data). The results show that the DMO-RF model achieved the best comprehensive performance with an R2 score of 0.967, RMSE of 0.541, MAE of 0.447, and VAF of 0.969 on the test set. The dry density and slake durability were more influential indicators than others. Moreover, the convergence curves suggested that the DMO-RF model can reduce the generalization error and avoid overfitting. The developed models can be regarded as viable and useful tools in estimating the E of rock.

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Section: Introductionmentioning

confidence: 99%

Developing Hybrid DMO-XGBoost and DMO-RF Models for Estimating the Elastic Modulus of Rock

Lin,

Liu,

Zhao

et al. 2023

Mathematics

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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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Comparative strength estimation model of recycled aggregate concrete modified with GGBS, Metakaolin, and fly ash

Zhang,

Tian,

Deng

2024

Multiscale and Multidiscip. Model. Exp. and Des.

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Compressive strength and elastic modulus of RBAC: An analysis of existing data and an artificial intelligence based prediction

Cited by 14 publications

References 82 publications

Developing Hybrid DMO-XGBoost and DMO-RF Models for Estimating the Elastic Modulus of Rock

Developing Hybrid DMO-XGBoost and DMO-RF Models for Estimating the Elastic Modulus of Rock

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

Comparative strength estimation model of recycled aggregate concrete modified with GGBS, Metakaolin, and fly ash

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