Prediction Model for Compressive Strength of Porous Concrete with Low-Grade Recycled Aggregate

Liu, Junshi; Ren, Fangfang; Quan, Hongzhu

doi:10.3390/ma14143871

Cited by 7 publications

(2 citation statements)

References 45 publications

(40 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Porositas beton memiliki dampak signifikan terhadap kuat tekan beton. Semakin tinggi tingkat porositasnya, semakin rendah kuat tekan betonnya (Kartini & Sumaidi, 2021;Liu et al, 2021). Air mengisi ruang dan saat mengering akan membentuk rongga udara.…”

Section: Pendahuluanunclassified

Penggunaan Bahan Aditif Master Glenium Ace 8595 Untuk Meminimalisir Porositas Pada Beton

Fitriani,

Mahardana,

Pasya

et al. 2024

jurissipil

View full text Add to dashboard Cite

<p><em>Concrete has now become the most essential material in modern construction. The strength and durability of concrete are vital factors that influence the strength and stability of buildings and infrastructure. Porosity refers to the degree to which concrete has voids or voids within its structure. Concrete porosity has a significant impact on concrete compressive strength. The higher the level of porosity, the lower the compressive strength of the concrete. In this research, we conducted a concrete formulation experiment by adding a type F additive to MasterGlenium Ace 8595. This research investigates the effect of adding the additive Master Glenium Ace 8595 on changes in concrete porosity and increasing concrete compressive strength. The results of the research show that from the aggregate suitability testing, it was stated that all the aggregates tested were suitable for use as a building material for concrete, workability testing obtained results with dilute criteria, porosity testing obtained an average porosity of 8.05%, and compressive strength testing obtained an average compressive strength value. -an average of 23.56 MPa indicates an increase of 8.69% of the planned concrete quality. This research concludes that concrete porosity is one of the factors that must be considered to increase the compressive strength of concrete because it significantly influences its density and compressive strength. Keeping the porosity at a low level and the compressive strength of the concrete at a high level can ensure that the concrete structure has good resistance to loads and the environment and reduces the risk of damage and maintenance.</em></p>

show abstract

Section: Pendahuluanunclassified

Penggunaan Bahan Aditif Master Glenium Ace 8595 Untuk Meminimalisir Porositas Pada Beton

Fitriani,

Mahardana,

Pasya

et al. 2024

jurissipil

View full text Add to dashboard Cite

show abstract

“…At present, there are substantial experimental, numerical and theoretical studies on the strength of RAC. Researchers proposed methods based on aggregate skeleton theory, gray correlation analysis, finite element analysis and so on to predict the compressive strength of RAC and analyze its influencing factors [ 4 , 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Machine-Learning-Based Prediction Models for the Compressive Strength of Recycled Powder Mortar

et al. 2023

View full text Add to dashboard Cite

Recycled powder (RP) serves as a potential and prospective substitute for cementitious materials in concrete. The compressive strength of RP mortar is a pivotal factor affecting the mechanical properties of RP concrete. The application of machine learning (ML) approaches in the engineering problems, particularly for predicting the mechanical properties of construction materials, leads to high prediction accuracy and low experimental costs. In this study, 204 groups of RP mortar compression experimental data are collected from the literature to establish a dataset for ML, including 163 groups in the training set and 41 groups in the test set. Four ensemble ML models, namely eXtreme Gradient-Boosting (XGBoost), Random Forest (RF), Light Gradient-Boosting Machine (LightGBM) and Adaptive Boosting (AdaBoost), were selected to predict the compressive strength of RP mortar. The comparative results demonstrate that XGBoost has the highest prediction accuracy when the a10-index, MAE, RMSE and R2 of the training set are 0.926, 1.596, 2.155 and 0.950 and the a10-index, MAE, RMSE and R2 of the test set are 0.659, 3.182, 4.285 and 0.842, respectively. SHapley Additive exPlanation (SHAP) is adopted to interpret the prediction process of XGBoost and explain the influence of influencing factors on the compressive strength of RP mortar. According to the importance of influencing factors, the order is the mass replacement rate of RP, the size of RP, the kind of RP and the water binder ratio of RP. The compressive strength of RP mortar decreases with the increase in the RP mass replacement rate. The compressive strength of RBP mortar is slightly higher than that of RCP mortar. Machine learning technologies will benefit the construction industry by facilitating the rapid and cost-effective evaluation of RP material properties.

show abstract

Review on zero waste strategy for urban construction and demolition waste: Full component resource utilization approach for sustainable and low-carbon

Gao

Jiang

et al. 2023

Construction and Building Materials

View full text Add to dashboard Cite

Prediction Model for Compressive Strength of Porous Concrete with Low-Grade Recycled Aggregate

Cited by 7 publications

References 45 publications

Penggunaan Bahan Aditif Master Glenium Ace 8595 Untuk Meminimalisir Porositas Pada Beton

Penggunaan Bahan Aditif Master Glenium Ace 8595 Untuk Meminimalisir Porositas Pada Beton

Ensemble Machine-Learning-Based Prediction Models for the Compressive Strength of Recycled Powder Mortar

Review on zero waste strategy for urban construction and demolition waste: Full component resource utilization approach for sustainable and low-carbon

Contact Info

Product

Resources

About