Estimation of concrete compression using regression models

Assegie, Tsehay Admassu; Salau, Ayodeji Olalekan; Badrudeen, Tayo Uthman

doi:10.11591/eei.v11i5.4210

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…(3) LR is a type of compressed estimation. By adding L1 norm penalty as a penalty function to the loss function, a more refined model is obtained that can compress some coefficients to zero [100]. The loss function formula is arg min…”

Section: Regression Modelmentioning

confidence: 99%

Recent Development in Intelligent Compaction for Asphalt Pavement Construction: Leveraging Smart Sensors and Machine Learning

Wang,

Li,

Zhang

et al. 2024

Sensors

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Intelligent compaction (IC) has emerged as a breakthrough technology that utilizes advanced sensing, data transmission, and control systems to optimize asphalt pavement compaction quality and efficiency. However, accurate assessment of compaction status remains challenging under real construction conditions. This paper reviewed recent progress and applications of smart sensors and machine learning (ML) to address existing limitations in IC. The principles and components of various advanced sensors deployed in IC systems were introduced, including SmartRock, fiber Bragg grating, and integrated circuit piezoelectric acceleration sensors. Case studies on utilizing these sensors for particle behavior monitoring, strain measurement, and impact data collection were reviewed. Meanwhile, common ML algorithms including regression, classification, clustering, and artificial neural networks were discussed. Practical examples of applying ML to estimate mechanical properties, evaluate overall compaction quality, and predict soil firmness through supervised and unsupervised models were examined. Results indicated smart sensors have enhanced compaction monitoring capabilities but require robustness improvements. ML provides a data-driven approach to complement traditional empirical methods but necessitates extensive field validation. Potential integration with digital construction technologies such as building information modeling and augmented reality was also explored. In conclusion, leveraging emerging sensing and artificial intelligence presents opportunities to optimize the IC process and address key challenges. However, cooperation across disciplines will be vital to test and refine technologies under real-world conditions. This study serves to advance understanding and highlight priority areas for future research toward the realization of IC’s full potential.

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Section: Regression Modelmentioning

confidence: 99%

Recent Development in Intelligent Compaction for Asphalt Pavement Construction: Leveraging Smart Sensors and Machine Learning

Wang,

Li,

Zhang

et al. 2024

Sensors

View full text Add to dashboard Cite

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A soft-computing-based modeling approach for predicting acid resistance of waste-derived cementitious composites

Cao,

Yuan,

Nasir Amin

et al. 2023

Construction and Building Materials

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A Machine Learning Approach to Prediction of the Compressive Strength of Segregated Lightweight Aggregate Concretes Using Ultrasonic Pulse Velocity

Migallón

Penadés

et al. 2023

Applied Sciences

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Lightweight aggregate concrete (LWAC) is an increasingly important material for modern construction. However, although it has several advantages compared with conventional concrete, it is susceptible to segregation due to the low density of the incorporated aggregate. The phenomenon of segregation can adversely affect the mechanical properties of LWAC, reducing its compressive strength and its durability. In this work, several machine learning techniques are used to study the influence of the segregation of LWAC on its compressive strength, including the K-nearest neighbours (KNN) algorithm, regression tree-based algorithms such as random forest (RF) and gradient boosting regressors (GBRs), artificial neural networks (ANNs) and support vector regression (SVR). In addition, a weighted average ensemble (WAE) method is proposed that combines RF, SVR and extreme GBR (or XGBoost). A dataset that was recently used for predicting the compressive strength of LWAC is employed in this experimental study. Two different types of lightweight aggregate (LWA), including expanded clay as a coarse aggregate and natural fine limestone aggregate, were mixed to produce LWAC. To quantify the segregation in LWAC, the ultrasonic pulse velocity method was adopted. Numerical experiments were carried out to analyse the behaviour of the obtained models, and a performance improvement was shown compared with the machine learning models reported in previous works. The best performance was obtained with GBR, XGBoost and the proposed weighted ensemble method. In addition, a good choice of weights in the WAE method allowed our approach to outperform all of the other models.

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Estimation of concrete compression using regression models

Cited by 3 publications

References 15 publications

Recent Development in Intelligent Compaction for Asphalt Pavement Construction: Leveraging Smart Sensors and Machine Learning

Recent Development in Intelligent Compaction for Asphalt Pavement Construction: Leveraging Smart Sensors and Machine Learning

A soft-computing-based modeling approach for predicting acid resistance of waste-derived cementitious composites

A Machine Learning Approach to Prediction of the Compressive Strength of Segregated Lightweight Aggregate Concretes Using Ultrasonic Pulse Velocity

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