Estimation Of Compressive Strength Research Articles

The Influence of the Rebound Hammer Test Location on the Estimation of Compressive Strength of a Historical Solid Clay Brick

This paper presents the study of a two-hundred-year-old, demolded solid clay brick using the rebound hammer test for the estimation of the compressive strength. During the test, the location and face type influence on the rebound values are monitored and recorded. In addition, the calculation of the average rebound value has been modified to encounter the influence of location and face types. Furthermore, the estimated compressive strength is compared with the normalized mean compressive strength to check the accuracy of the rebound hammer test if it is within the confidential limit of ±25%. The result shows that the location and surface types have influence on the rebound value, which in turn affected the compressive strength.

A Two-Step FE Model Updating Approach for System and Damage Identification of Prestressed Bridge Girders

This study presents a two-step FE model updating approach for health monitoring and damage identification of prestressed concrete girder bridges. To reduce the effects of modeling error in the model updating process, in the first step, modal-based model updating is used to estimate linear model parameters mainly related to the stiffness of boundary conditions and material properties. In the second step, a time-domain model updating is carried out using acceleration data to refine parameters accounting for the nonlinear response behavior of the bridge. In this step, boundary conditions are fixed at their final estimates using modal-based model updating. To prevent the convergence of updating algorithm to local solutions, the initial estimates for nonlinear material properties are selected based on the first-step model updating results. To validate the applicability of the two-step FE model updating approach, a series of forced-vibration experiments are designed and carried out on a pair of full-scale decommissioned and deteriorated prestressed bridge I-girders. In the first step, parameters related to boundary conditions, including stiffness of supports and coupling beams, as well as material properties, including initial stiffness of concrete material, are estimated. In the second step, concrete compressive strength and damping properties are updated. The final estimates of the concrete compressive strength are used to infer the extent of damage in the girders. The obtained results agree with the literature regarding the extent of reduction in concrete compressive strength in deteriorated concrete structures.

Compressive strength estimation of continuous carbon fibre/epoxy resin composite by micro-mechanical numerical modelling

Compressive strength estimation of continuous carbon fibre/epoxy resin composite by micro-mechanical numerical modelling

Evaluating uncertainty in empirically derived unconfined compressive strength (UCS) estimates and implications for drilling applications; a case study from the Cooper Basin

Accurate estimates of Unconfined Compressive Strength (UCS) are essential for a range of subsurface applications, including drilling wells for subsurface fluid extraction or injection. However, measuring UCS of subsurface rock samples through laboratory-based uniaxial and triaxial testing is time consuming, expensive, and potentially subject to individual sample variance. P-wave velocity logs are routinely obtained in petroleum basins and are commonly used to estimate UCS using empirically-derived correlations. In this study we analysed P-wave velocity data from 43 wells in the Cooper Basin, Australia and created mean ranges of expected UCS through the Tirrawarra, Toolachee, Patchawarra, Murteree Shale and Epsilon formations, using literature-derived P-wave velocity-UCS correlations, and also estimated UCS based on selected available laboratory-determined P-wave velocity measurements. These two suites of P-wave velocity-derived estimates of UCS are then compared to available results from laboratory uniaxial testing of core samples. Our analysis indicates that P-wave velocities, and subsequent derived estimates of UCS from P-wave wireline logs, are typically lower than those from laboratory-analysed samples. This may reflect a sampling bias towards the selection of strong rocks for laboratory UCS testing or lower estimates of UCS due to wellbore damage and pore space content (air or liquids) from P-wave log derived velocities. However, the laboratory-derived P-wave velocities generally agree with laboratory-derived UCS data from the Patchawarra, Murteree Shale and Epsilon formations when using published empirical velocity-strength correlations. A retrospective case study presents the impacts of different UCS estimates on mud weight required to produce observed borehole breakouts in the Epsilon Formation. Breakouts were observed 90 degrees to the orientation of SHmax (117° N) with a mean width of 51°, the breakouts were produced during drilling using a 9.7 ppg mud weight. The back-calculated estimate of UCS from the observed breakout widths at 9.7 ppg is 154 MPa. This sits between the UCS estimate of 171 MPa from uniaxial testing and 145 MPa from the mean of laboratory sample-derived P-wave velocities. Estimates of UCS from empirical correlations based solely on p-wave log-derived are far lower and vary between 87.3 and 114.4 MPa.

Decision tree models for the estimation of geo-polymer concrete compressive strength.

The green concretes industry benefits from utilizing gel to replace parts of the cement in concretes. However, measuring the compressive strength of geo-polymer concretes (CSGPoC) needs a significant amount of work and expenditure. Therefore, the best idea is predicting CSGPoC with a high level of accuracy. To do this, the base learner and super learner machine learning models were proposed in this study to anticipate CSGPoC. The decision tree (DT) is applied as base learner, and the random forest and extreme gradient boosting (XGBoost) techniques are used as super learner system. In this regard, a database was provided involving 259 CSGPoC data samples, of which four-fifths of is considered for the training model and one-fifth is selected for the testing models. The values of fly ash, ground-granulated blast-furnace slag (GGBS), Na2SiO3, NaOH, fine aggregate, gravel 4/10 mm, gravel 10/20 mm, water/solids ratio, and NaOH molarity were considered as input of the models to estimate CSGPoC. To evaluate the reliability and performance of the decision tree (DT), XGBoost, and random forest (RF) models, 12 performance evaluation metrics were determined. Based on the obtained results, the highest degree of accuracy is achieved by the XGBoost model with mean absolute error (MAE) of 2.073, mean absolute percentage error (MAPE) of 5.547, Nash-Sutcliffe (NS) of 0.981, correlation coefficient (R) of 0.991, R2 of 0.982, root mean square error (RMSE) of 2.458, Willmott's index (WI) of 0.795, weighted mean absolute percentage error (WMAPE) of 0.046, Bias of 2.073, square index (SI) of 0.054, p of 0.027, mean relative error (MRE) of -0.014, and a20 of 0.983 for the training model and MAE of 2.06, MAPE of 6.553, NS of 0.985, R of 0.993, R2 of 0.986, RMSE of 2.307, WI of 0.818, WMAPE of 0.05, Bias of 2.06, SI of 0.056, p of 0.028, MRE of -0.015, and a20 of 0.949 for the testing model. By importing the testing set into trained models, values of 0.8969, 0.9857, and 0.9424 for R2 were obtained for DT, XGBoost, and RF, respectively, which show the superiority of the XGBoost model in CSGPoC estimation. In conclusion, the XGBoost model is capable of more accurately predicting CSGPoC than DT and RF models.

Uncertainty of concrete compressive strength determination by a combination of Rebound Number, UPV and uniaxial compressive tests on cubical concrete samples

Rebound number (RN) and ultrasonic pulse velocity (UPV) determination provide a non-destructive indirect estimation of concrete compressive strength (CS). They have an extensive uncertainty, both due to limited reproducibility and trueness of these methods. Tests on a uniaxial compressive apparatus generate a destructive result, also with a relatively high uncertainty, which requires repeating this test on multiple samples to produce a mean result with an acceptable uncertainty. In this work, a combination of all three methods was performed on cubical concrete specimens. Three different mixes were designed, each corresponding to a different assigned compressive strength. Six specimens were produced from each mix design, and each specimen was cured for 28 days. During and until that time, each specimen was tested according to all three aforementioned methods. Results were compared in pairs and factors related to equipment tolerance and operator variations were assessed. Regression processes were applied to estimate optimized functions that predict compressive strength based on the non-destructive RN and UPV measurements. Additionally, a function was estimated as the mean of all three different test method results. Finally, a statistically significant multivariate function was established along with a corresponding uncertainty budget. The result is a determination of compressive strength for cubical concrete specimen with known, reduced uncertainty. These lower uncertainty limits enable achieving conformity assessment for concrete compressive strength with minimized decision risk. The calibrated multivariate regression function in combination with its resulting uncertainty intervals are expected to benefit constructors in their aim at interpreting their measurement data to comply with the EN 206 quality control procedure and accredited laboratories to verify the validity of their results by performing internal quality control.

Artificial Neural Network Levenberg–Marquardt Based Algorithm for Compressive Strength Estimation of Concrete Mixed with Magnetic Salty Water

This study develops an ANN-Levenberg-Marquardt hybrid model to estimate compressive strength of concrete mixed with magnetized salty water, demonstrating its accuracy and applicability for sustainable construction materials.

A Novel Multi-variable Model for the Estimation of Compressive Strength of Pervious Concrete

A Novel Multi-variable Model for the Estimation of Compressive Strength of Pervious Concrete

Generic compressive strength prediction model applicable to multiple lithologies based on a broad global database

In this study, we present a global database of ten parameters, which include measurements of rock index properties, strength stiffness and dynamic properties. Hoek–Brown constant mi, is included, and was estimated, using Hoek and Brown proposed guidelines for determining mi values for different rock types that can be used for preliminary design when triaxial tests are not available. This broad database is compiled from 96 studies and is labelled as “ROCK/10/4025”, to describe the type of geomaterial, the number of the parameters, and the number of the data samples included. It consists of 35.4 % igneous, 54.8 % sedimentary, and 9.2% metamorphic rocks. The purpose of this paper is to propose a generic soft computing model applicable to multiple lithologies, that can become more reliable and perhaps more suitable for a specific site study when used in order to densify often limited similar site-specific data. To this end four broad samples of data were selected, and served as training data sets for developed machine learning models, to develop a generic compression strength prediction model applicable to multiple lithologies. The suggested algorithms in this study are Back-Propagation Artificial Neural Networks, Artificial Neuro-Fuzzy Inference Systems, Support Vector Machines, Nearest Neighbour classifiers and Ensemble Bagged Trees. According to the findings of this study, Artificial Neuro-Fuzzy Inference Systems model performance was found to be marginally superior, while Back Propagation Artificial Neural Networks, Support Vector Machines and Ensemble Bagged Trees models were found to have good performance. Constant mi seems to be an important training parameter when training predictive models centred on data from multiple lithologies. As a result, we can suggest that these models are powerful tools that allow for a reliable estimation of compressive strength, based on the performance indicators. The performance was found to be 70%–82% when the problem of compressive strength prediction was approached as a classification problem (that is successful prediction of class from very weak to very strong), and 80%–96% when solved as a function approximation problem.

Compressive strength prediction of hollow concrete masonry blocks using artificial intelligence algorithms

In this study, artificial intelligence algorithms are proposed for estimating the compressive strength of hollow concrete block masonry prisms, including neural networks (ANN), combinatorial methods of group data handling (GMDH-Combi), and gene expression programming (GEP). To train and test the proposed models, 102 samples of hollow concrete prisms from previous research works were collected. The height-to-width ratio of hollow concrete prisms and the compressive strength of mortar and concrete blocks were considered as inputs. In order to evaluate the validity and predictability of the proposed models, they were compared with empirical models and models presented in codes and standards. Among the suggested and existing models, the ANN model with an R-value of 0.950 and MAPE error value of 6.921% had the best performance, which with a more complicated equation, can be used in the scientific aspect. In contrast, the other two proposed models (GMDH-Combi and GEP) with acceptable performance and accuracy levels and more simple closed-form equations can be utilized in practical aspects. Based on the parametric analysis, the proposed models were highly efficient and accurate. Moreover, the sensitivity analysis results showed that in all three proposed models of ANN, GMDH-Combi, and GEP, the compressive strength of concrete blocks was the most effective input parameter in the compressive strength estimation of hollow concrete prisms.

Compressive Strength Estimation of Rice Husk Ash-Blended Concrete Using Deep Neural Network Regression with an Asymmetric Loss Function

Compressive Strength Estimation of Rice Husk Ash-Blended Concrete Using Deep Neural Network Regression with an Asymmetric Loss Function

Development of the New Prediction Models for the Compressive Strength of Nanomodified Concrete Using Novel Machine Learning Techniques

Concrete is a heterogeneous material that is extensively used as a construction material. However, to improve the toughness and mechanical properties of concrete, various ingredients (fillers) have been added in the past. The addition of nanomaterials for the improvement of the aforementioned properties has attracted many researchers worldwide. The high surface area, high reactivity, and finer size of various nanomaterials have made them preferable for the enhancement of durability, as well as compressive and flexural strength. The aim of the current research is focused on the estimation of compressive strength for the concrete modified with various nanomaterials using two machine learning techniques, namely decision tree technique (DTT) and random forest technique (RFT), and comparison with existing models. The database is collected for different percentages of four major widely used nanomaterials in concrete, i.e., carbon nanotubes, nano silica, nano clay, and nano alumina. The other four input variables used for the calibration of the models are: cement content (CC); water–cement ratio (W/C); fine aggregate, i.e., sand (FA); and coarse aggregate (CA). Both DTT and RFT models were developed for 94 collected experimental datasets from the published literature. The predicted results are further validated through K-fold cross-validation using correlation coefficient (R2), mean absolute error (MAE), root mean square error (RMSE), relative root mean square error, relative square error (RRMSE), and performance index factor (PiF). The RFT model was found to have the lowermost MAE 3.253, RMSE 4.387, RRMSE 0.0803, and performance index factor (PiF) 0.0061. In comparison, predicted results overall revealed better performance and accuracy for the RFT-developed models than for DTT and gene expression programming (GEP) models, as illustrated by their high R2 value, equal to 0.96, while the R2 value for DTT and GEP was found 0.94 and 0.86, respectively.

A new design equation to estimate the axial compressive strength of circular concrete-filled steel tubular stub columns

This paper proposes a new design equation for circular concrete-filled steel tubular (CCFST) stub columns. A database consisting of 394 CCFST stub column tests are first compiled. Finite element (FE) analysis is then performed to generate 174 new results to cover the gaps in the database. Based on these, the correlations between the strength enhancement index and the tube diameter-to-thickness ratio, section slenderness, and relative strength factor are investigated. The experimental and FE ultimate loads are used to assess the accuracy of the existing codified strength predictions. Overall, it is found that the European Code (EC 4) provides more accurate strength predictions than the American (AISC 360-16) and Japanese (AIJ) codes, but is unconservative for high-strength CCFST short columns. A new unified design equation by incorporating the effect of ξ is then proposed. The strengths predicted by the proposed equation are comparably accurate as those predicted by EC 4. The proposed equation allows a fast and convenient estimation of the axial compressive strength of CCFST stub columns, with no need to calculate or classify the section slenderness of CCFST stub columns. Hence, it is recommended for practical strength estimation.

On the Variability of Conversion Models for Concrete Strength Assessment Based on Pulse Velocity Measurements

Abstract The existence of a wide reinforced concrete (r.c.) building stock, which is reaching the end of its service life, has focused the attention on its vulnerability assessment. The first step of this analysis is the estimation of the concrete compressive strength. In this field, many codes allow to supplement the classical destructive tests with nondestructive ones, because of their versality and of the possibility of reducing the time spent for tests. It is worth noting that the spread of these tests is strictly connected to the accuracy of the conversion models, which correlate the nondestructive measurements to the concrete strength. The present paper deals with this issue by examining the results of the experimental investigations, which made use of destructive and ultrasonic pulse velocity tests, on three r.c. buildings in Bari (Italy). The data are analyzed and rearranged to define several testing conditions, for each one of which a conversion model is calibrated. Moreover, the variability of the parameters of the identified models is discussed with respect to the number of data considered in the calibration process, to the chosen model, and to the accuracy of the assessed strength.

Sustainable use of waste eggshells in cementitious materials: An experimental and modeling-based study

This study evaluated the compressive strength (CS) of cement-based materials (CBMs) incorporating eggshell powder (ESP) as a partial substitute for cement and fine aggregate using both experimental and machine learning (ML)-based strategies. Initially, the CS of CBMs based on ESP was measured experimentally. ML techniques, including support vector machine and bagging regressor, were applied for CS estimation. Comparing the coefficient of determination (R2), statistical checks, k-fold evaluation, and measuring the difference between experimental and projected CS were used to evaluate the performance of ML models. According to the results of the experiment, the addition of ESP increased the CS of CBMs when used in lower replacement ratios. In addition, the support vector machine model had a reasonable degree of precision, while the bagging regressor model predicted the CS of ESP-based CBMs with a higher degree of precision. Incorporating waste eggshells into building materials will promote sustainable development by minimizing environmental problems connected with eggshell disposal, conserving natural resources, offering cost-effective materials, and decreasing CO2 emissions. In addition, the use of ML approaches will benefit the construction sector by facilitating efficient and economical ways of analyzing the properties of materials.

Evaluating the Strength and Impact of Raw Ingredients of Cement Mortar Incorporating Waste Glass Powder Using Machine Learning and SHapley Additive ExPlanations (SHAP) Methods

This research employed machine learning (ML) and SHapley Additive ExPlanations (SHAP) methods to assess the strength and impact of raw ingredients of cement mortar (CM) incorporated with waste glass powder (WGP). The data required for this study were generated using an experimental approach. Two ML methods were employed, i.e., gradient boosting and random forest, for compressive strength (CS) and flexural strength (FS) estimation. The performance of ML approaches was evaluated by comparing the coefficient of determination (R2), statistical checks, k-fold assessment, and analyzing the variation between experimental and estimated strength. The results of the ML-based modeling approaches revealed that the gradient boosting model had a good degree of precision, but the random forest model predicted the strength of the WGP-based CM with a greater degree of precision for CS and FS prediction. The SHAP analysis revealed that fine aggregate was a critical raw material, with a stronger negative link to the strength of the material, whereas WGP and cement had a greater positive effect on the strength of CM. Utilizing such approaches will benefit the building sector by supporting the progress of rapid and inexpensive approaches for identifying material attributes and the impact of raw ingredients.

Monitoring and estimate of concrete compressive strength in full curing period using smart piezoelectric modules

Concrete compressive strength is a most important material performance index and can reflect the quality of a concrete material. An innovative test method is proposed for monitoring the development of concrete compressive strength during the full curing period using smart piezoelectric modules (SPMs). The proposed method overcomes shortcomings of traditional methods and can be used for non-destructive, real-time, continuous, in situ monitoring of concrete strength during the whole curing period. A series of experiments demonstrates the validity and effectiveness of the method using SPMs. The energy values of monitoring signals from SPMs is applied as a characteristic identification parameter for concrete compressive strength. The signal waveform and frequency suitable for monitoring concrete compressive strength are determined, and a standard test procedure using SPMs to monitor concrete compressive strength is defined. Finally, equations for correlating the signal energy ratio and compressive strength ratio of different grades of concrete (i.e., C30, C40 and C50) based on the SPM monitoring data are established. The compressive strength of concrete for the given curing age can be effectively predicted by the proposed method, and the established correlation equations can be applied to assess the concrete condition and strength in curing.

CHARACTERISATION OF CONCRETE FROM THE RUPNIK MILITARY LINE

The Rupnik military line was established in about 1935 in the Kingdom of Yugoslavia as a defence system against the Kingdom of Italy. It consists of more than 4000 reinforced concrete military bunkers positioned on the eastern part of the Rapallo border, varying in size and purpose, and was a top-secret project at the time. Different non-destructive and non-invasive techniques were used to characterise the selected bunkers and carefully conceived concrete samples to gain an insight into the concrete technology used to build these historic military infrastructures. The results of the non-destructive techniques were further compared to those of destructive techniques for a given property. It was established that very different concrete compositions were used to build the bunkers, and an extensive dispersion of the properties was confirmed for each composition. The average compressive strength of the Schmidt hammer for a given position is an acceptable estimate of the actual compressive strength of concrete without destructive intervention into a bunker. It also enables an estimation of the secant modulus of elasticity using the ModelCode2010 approach. Cylinders drilled from a bunker provided additional information about the concrete petrography, its physical and mechanical properties and the durability of reinforced concrete.

Estimation of concrete compression using regression models

The objective of this study is to evaluate the effectiveness of different regression models in concrete compressive strength estimation. A concrete compressive strength dataset is employed for the estimation of the regressor models. Regression models such as linear regressor, ridge regressor, k-neighbors regressor, decision tree regressor, random forest regressor, gradient boosting regressor, AdaBoost regressor, and support vector regressor are used for developing the model that predicts the concrete strength. Cross-validation techniques and grid search are used to tune the parameters for better model performance. Python 3.8 programming language is used to conduct the experiment. The Performance evaluation result reveals that the gradient boosting regressor has better performance as compared to other models using root mean square error (RMSE).

Fly ash-based geopolymer concrete's compressive strength estimation by applying artificial intelligence methods

Geopolymer concrete is a kind of eco-friendly concrete and its important characteristic is that the cement is completely or partially removed from its mixture and other materials such as industrial waste have replaced it. Using industrial waste materials, in particular fly ash, as full or partial replacement of cement in this type of concrete can provide advantages like improving concrete's fresh and hardened traits, decreasing the amounts of released waste materials in the environment, reducing CO2 footprint and alleviate the pressure on natural resources. According to the acceptable performance of Artificial Intelligence (AI) methods in the various fields, as well as in the issues related to the properties of cementitious materials, in this research three developed AI methods including radial basis function neural network (RBFNN) aided by ant colony optimization algorithm (ACO), group method of data handling (GMDH), and artificial neural network (ANN) was proposed for estimating the compressive strength (that is a notable mechanical character of every type of concrete) of fly ash-based geopolymer concrete (FAGC). To achieve this goal, the information from 360 samples of FAGC was collected from the previous studies. Models' accuracy and predictive capacity have been appraised via Statistical formulas as well as comparing models' outputs with experimental test results. The assessment showed that the performance of all three models for forecasting this mechanical feature of FAGC is acceptable but the RBFNN has the best performance among these proposed models.