Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms

Song, Hongwei; Ahmad, Ayaz; Farooq, Furqan; Ostrowski, Krzysztof; Maślak, Mariusz; Czarnecki, Sławomir; Aslam, Fahid

doi:10.1016/j.conbuildmat.2021.125021

Cited by 181 publications

(44 citation statements)

References 57 publications

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“…Studies on the effect of axial load have shown that the ductility of an RC column decreases as the axial load ratio applied to it increases [ 15 , 16 ]. Similar results were found in experiments on concrete columns using low-quality concrete and high-strength materials [ 17 , 18 , 19 ]. A study conducted by Mo and Wang showed that the spacing of transverse reinforcement had a significant effect on the performance of the column [ 20 ].…”

Section: Introductionsupporting

confidence: 86%

Seismic Behavior of Existing Reinforced Concrete Columns with Non-Seismic Details under Low Axial Loads

Choi

Lee

2022

Materials

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Reinforced concrete (RC) columns of old existing buildings are vulnerable to earthquakes because the hoops comprising their transverse reinforcement are widely spaced and anchored using 90° hooks. This study extensively evaluated the seismic behavior of RC columns with such non-seismic details. Experiments were conducted by applying lateral cyclic loads to five full-scale column specimens with various transverse reinforcement details subjected to low axial loads. The experimental results demonstrated that the internal transverse crosstie had a significant confinement effect in the non-seismic detailed columns with 90° hoop anchor hooks. In addition, the lateral load–drift relationships, ductilities, and energy dissipation capabilities of the columns were not significantly affected by the hoop spacing or anchor hook angle when a low axial load was applied up to a drift ratio of 3.5% before failure. The evaluation model based on ASCE/SEI 41-17 was then shown to approximate the initial stiffness, maximum strength, and post-peak strength reduction behavior of the non-seismically reinforced column. This study was based on the experimental behavior of single column members, and it needs to be extended to research on frame structures in which columns are connected to beams and slabs.

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Section: Introductionsupporting

confidence: 86%

Seismic Behavior of Existing Reinforced Concrete Columns with Non-Seismic Details under Low Axial Loads

Choi

Lee

2022

Materials

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“…However, although this concept was tested for each presented design situation, this study does not cover all possible aspects of advanced materials and the respective whole-life assessment and environmental influences. In future investigations, this concept can be applied not only to a wider range of modern civil engineering problems, such as construction materials under various environmental impacts, high-and ultra-high-performance concrete and advanced concrete composites in terms of resilience, durability and sustainability, but also extreme events, such as seismic actions, as discussed in [57][58][59][60][61][62][63][64][65][66][67][68][69][70].…”

Section: Discussionmentioning

confidence: 99%

Quality Control Method for the Service Life and Reliability of Concrete Structures

et al. 2022

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In the past few years, there has been an increasing societal and industrial demand for the reliable assessment and design of structural systems with service-life criteria of at least several decades. The life cycle characterisation of engineering structures in terms of an anticipated service life remains a significant aspect of sustainability in the construction industry. This requires special attention to the definition of structural performance under various actions, and to the implemented engineering materials and methods as well as to the inverse identification and monitoring of structural conditions. Subsequently, the focus remains on the development of a holistic performance-based design approach for new and existing structures and infrastructures. This paper presents the fundamental reliability concepts of performance-based design, with a focus on lifetime assessment. Case studies from actual structural components’ design are used to verify the proposed methodology and indicate the significance of quality assurance in the lifetime assessment of engineering structures. We also confirmed that reliability and quality assurance criteria are strongly connected. Therefore, a methodology for quality-based service life assessment is presented and elaborated in the case studies.

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“…In recent years, machine learning (ML) algorithms have demonstrated significant potential for forecasting cementitious material properties [22][23][24][25][26][27][28]. Among the numerous machine learning methods, support vector regression (SVR) and artificial neural network (ANN) methods have been widely utilized to predict concrete parameters such as compressive strength (C-S) [29], split-tensile strength, elastic modulus, and so on [30][31][32]. ANN and SVR, however, are standalone models.…”

Section: Introductionmentioning

confidence: 99%

Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques

Ahmad

et al. 2021

Materials

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The current trend in modern research revolves around novel techniques that can predict the characteristics of materials without consuming time, effort, and experimental costs. The adaptation of machine learning techniques to compute the various properties of materials is gaining more attention. This study aims to use both standalone and ensemble machine learning techniques to forecast the 28-day compressive strength of high-performance concrete. One standalone technique (support vector regression (SVR)) and two ensemble techniques (AdaBoost and random forest) were applied for this purpose. To validate the performance of each technique, coefficient of determination (R2), statistical, and k-fold cross-validation checks were used. Additionally, the contribution of input parameters towards the prediction of results was determined by applying sensitivity analysis. It was proven that all the techniques employed showed improved performance in predicting the outcomes. The random forest model was the most accurate, with an R2 value of 0.93, compared to the support vector regression and AdaBoost models, with R2 values of 0.83 and 0.90, respectively. In addition, statistical and k-fold cross-validation checks validated the random forest model as the best performer based on lower error values. However, the prediction performance of the support vector regression and AdaBoost models was also within an acceptable range. This shows that novel machine learning techniques can be used to predict the mechanical properties of high-performance concrete.

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Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms

Cited by 181 publications

References 57 publications

Seismic Behavior of Existing Reinforced Concrete Columns with Non-Seismic Details under Low Axial Loads

Seismic Behavior of Existing Reinforced Concrete Columns with Non-Seismic Details under Low Axial Loads

Quality Control Method for the Service Life and Reliability of Concrete Structures

Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques

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