EN STANDARD - HIGH STABILITY COMPRESSION MACHINES. The compression testing machines tested for high stability meet the stringent requirements of the: EN 12390-4 | BS 1881:115 | DIN 51220 | UNI 6686, …
In addition to compressive strength, the stress-strain and peak strain relationships were evaluated for MCC samples. According to GB50010, the MCC was more ductile than natural aggregate concrete ...
The paper presents a study on the various investigation of machine learning (ML) algorithms to estimate the compressive strength (CS) of concrete containing fly ash (FA). The research also aims to ...
Albaijan et al. 20 employed Silica nano powder to increase the strength of concrete, considering eleven machine learning methods to estimate the uniaxial …
The purpose of a Concrete Compression Machine is straightforward; it uses compressive force to load different types of concrete specimens to ensure the material meets design and structural strength requirements. …
The 1030 datasets for 17 studies used in constructing the data-driven model are the long-term compressive strength of ternary-blend concrete experimental data retrieved from the University of California dataset repository, Irvine [78], [79].It is important to note that the compressive strength tests were conducted on cubic concrete …
Experimentally predicting the compressive strength (CS) of concrete (for a mix design) is a time-consuming and laborious process. The present study aims to propose surrogate models based on Support Vector Machine (SVM) and Gaussian Process Regression (GPR) machine learning techniques, which can predict the CS of concrete …
Considering the numerical compressive strength reported in Table 4, the 3- and 7-day strength values vary in the range of 4.76–16.90% and 6.47–13.26, respectively, while 28-day strength alters in the range of and 9.83–34.06%. On the other hand, for the concrete with fine recycled aggregate the reduction in compressive strength of 3-day ...
The paper is devoted to prediction of concrete compressive strength depending on its composition using machine learning methods. The use of machine learning improves the accuracy of the prediction of concrete strength and reduces the number of necessary experimental checks when selecting the composition.The …
Compressive strength is considered to be one of the most important mechanical properties of high-strength concrete (HSC). In this study, three machine learning models, ELM, PSO-ANN, and GS-SVR were employed to predict the compressive strength of HSC using 681 data records.
There is a complex high-dimensional nonlinear mapping relationship between the compressive strength of High-Performance Concrete (HPC) and its components, which has great influence on the accurate ...
Digital Compression Testing Machine with Single Gauge also AvailableThe Digital Compression Testing Machine has been designed to meet the need for a simple, economic and reliable means to test concrete for its compressive strength. The Digital Indicator incorporates a 4-Digits display calibrated in Kilo Newton (KN) and preset to …
Applying machine learning methods for predicting the mechanical characteristics of concrete, particularly its compressive strength, is a crucial element in shaping the future of civil engineering. The current study uses gradient boosting (GBM) and light gradient boosting (LGBM) supervised machine learning (ML) approaches in the …
The main objective of this research is to propose two ensemble machine learning models, namely random forest (RF) and adaptive gradient boosting (AGB), for predicting the compressive strength of ultra-high-performance concrete (UHPC). These models are developed based on a total of 810 experimental results of the compressive …
Accurate prediction of concrete compressive strength can minimize the need for extensive, time-consuming, and costly mixture optimization testing and analysis.
Recycled aggregates (RA) can provide a sustainable solution for replacing natural aggregates (NA) in the concrete mix. However, the stakeholders and inspection professionals lack confidence in predicting their compressive strength (CS) due to limited databases. Most of them solely focus on the concrete mix with natural aggregates only. …
The concrete compressive strength is a highly nonlinear function of age and ingredients. Dataset Characteristics. Multivariate. Subject Area. Physics and Chemistry. Associated Tasks. ... By using the UCI Machine Learning Repository, you acknowledge and accept the cookies and privacy practices used by the UCI Machine Learning Repository.
A comparative analysis for the prediction of compressive strength of concrete at the ages of 28, 56, and 91 days has been carried out using machine learning techniques via "R" software environment, and it is inferred that the NN model predicts with high accuracy for compressivestrength of concrete.
Compressive strength (C–S) is the most significant mechanical property for all types of concrete composites, including geopolymer concrete. Due to the importance of the C–S of concrete at 28 days in the design of structures, it is necessary to build a reliable model to predict the compressive strength of geopolymer concrete.
Concrete is the most extensively used construction material, and cement is its main component. Hybrid machine learning models attract researchers in building materials due to their high applications and prediction accuracy. Hybrid machine learning model interpretability is crucial to apply to the interest of field experts. Therefore, this …
Nonetheless, valuation-based data gathering of compressive strength is extremely appropriate for highly accurate compressive strength prediction. In contrast to soft computing and ML models, determining the compressive strength of HP-ACC using traditional techniques is a very time-consuming procedure rather than an expensive one. …
These machines can test the compressive strength of beams, blocks, cubes, cylinders and other various concrete structures. Choose from a variety of automatic controllers …
In simple terms, compressive strength is a measure of concrete's ability to withstand axial loading. It is specifically determined from standard 150 mm x 150 mm x …
Compressive or flexural strength of concrete is easily measured in a number of ways for different types of specimens. Gilson Compression Testing Machines are the stiffest in the industry and meet or exceed …
The use of fly ash (FA)-based geopolymer concrete as a low-carbon and eco-friendly substitute to Portland cement concrete has gained attention in recent years. However, accurately predicting its compressive strength remains a challenge due to the complex chemical and physical interactions involved in the geopolymerization process. …
Then the compressive strength of the concrete was obtained through a typical compressive test procedure of the cylinder specimens with height 150 mm. Obviously, there are 9 parameters in total in the experimental data sets. ... Machine learning in concrete strength simulations: multi-nation data analytics. Constr. Build. …
3D concrete (3DCP) is crucial in the construction because of the low labor cost, eco-friendly behavior; however, getting a proper mixture is always a challenge. This study focuses on predicting the compressive strength (CS) of fiber-reinforced concrete produced with 3DCP using eight machine learning (ML) algorithms to get …
One of the most commonly performed tests on concrete is the determination of the compressive strength of standard sized concrete cubes and/or cylinders, typically …
Here, we develop the Gaussian process regression model to shed light on the relationship among the contents of cement, blast furnace slag, fly ash, water, …
2. Research significance and objective. In this study, an attempt has been made to predict the compressive strength of concrete containing GGBFS using different ML-based approaches, namely Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-nearest Neighbors (KNN), and Artificial Neural Network (ANN).
Machine learning techniques can predict the compressive strength of cement-based materials with good accuracy and learning capacity. Traditional compressive strength prediction according to machine learning techniques such as the support vector machine (SVM), decision tree, and Gaussian regression are normally …
In order to develop the random forest model (RF) and multiple linear regression model (MLR), a total of 144 testing data of the confined compressive strength of concrete were collected from the literatures [5,6,7,8,9,10,11,12,13,14,15].Seven essential input parameters are required to estimate the confined compressive strength of the …
Forney compression machines provide uncompromising durability, performance and productivity for use in the lab and the field. Manual, Automatic or Connected Control. Backed by the best technical support …
The casting and testing specimens for determining the mechanical properties of concrete is a time-consuming activity. This study employed supervised machine learning techniques, bagging, AdaBoost, gene expression programming, and decision tree to estimate the compressive strength of concrete containing supplementary …
