Prediction Of Elastic Modulus Research Articles

Comparison of geomechanical upscaling methods for prediction of elastic modulus of heterogeneous media

Geomechanical properties of rocks are essential for understanding their elastic behavior. These parameters find their applications in various fields such as petroleum engineering, and geological storage sites, of nuclear waste and carbon dioxide and other geotechnical operations. There are several methods to obtain these parameters, including experimental methods, mathematical upscaling, and numerical simulation while experimental approaches are more trusted. The aim of this study is to estimate Young's modulus of a shale sample from several major upscaling mathematical methods and compare the results to polyaxial compressive strength test and nanoindentation measurements on the same piece of sample. To achieve this, five major theoretical upscaling models, including DEM, MT, SCA, KTF, and DM, have been utilized to calculate the Young's modulus based on constituent components of the sample that was obtained from routine XRD and geochemical analysis. The calculations for each of the five methods were performed in two scenarios: without incorporating the organic matter in the model and with it, in a range of porosity values while input parameters for each constituent components were found from the literature. Young's modulus with organic materials following the upscaling schemes was in the range of 24.0661 [GPa] to 27.0001 [GPa]. Likewise, excluding the organic substances Young’s modulus was calculated 25.1784 [GPa] and 29.1394 [GPa], as the smallest and largest values, respectively. These values were compared to the Young's modulus obtained from the nanoindentation (34.15 GPa) and polyaxial test (24.36 GPa). The Young’s modulus decreases of approximately 7% with considering organic matter around 2.45% and decrease of less than 5% with variation of 1% porosity. Based on the results, it was concluded that upscaling methods overall provide results in an acceptable range while they are more cost-effective and time-efficient when compared to expensive and time-consuming experimental approaches and can replace them. Furthermore, the use of upscaling methods reduces the need for destructive testing and also eliminates the requirement for expensive equipment with limited access to proper size of the samples and required preparations that should be followed. However, the lithology of the sample and presence of certain type of pore spaces and fractures should not be taken lightly in this process.

Elastic Modulus Prediction from Indentation Using Machine Learning: Considering Tip Geometric Imperfection

Elastic Modulus Prediction from Indentation Using Machine Learning: Considering Tip Geometric Imperfection

Explainable Prediction of Compressive Strength and Elastic Modulus for Concrete Containing Waste Foundry Sand Using Bayesian-Optimized XGBoost with 10-Fold Cross-Validation

Explainable Prediction of Compressive Strength and Elastic Modulus for Concrete Containing Waste Foundry Sand Using Bayesian-Optimized XGBoost with 10-Fold Cross-Validation

Machine learning assisted prediction and analysis of in-plane elastic modulus of hybrid hierarchical square honeycombs

In this study, experimental, finite element (FE) simulation, machine learning (ML), and theoretical techniques are employed to investigate the in-plane elastic modulus (EHHSH) of hybrid hierarchical square honeycombs (HHSHs). First, HHSHs with different configurations were fabricated using a 3D printer, and in-plane quasi-static compression tests were conducted on them. Then, 234 FE models are simulated to determine the EHHSH of HHSHs with various configurations, and the results are used to train 11 ML models. Comparative analysis demonstrates that the Extreme Gradient Boosting (XGBoost) model has the best predictive capability. Moreover, a modified theory for EHHSH is established based on the XGBoost model and existing theory, and its exceptional predictive capability is verified by comparing with experimental, FE, and existing theoretical results. Finally, the upper and lower bounds of EHHSH are determined by the modified theory, and the Shapley Additive Explanation (SHAP) method is used to identify the importance of different geometric parameters on tailoring EHHSH. The combination of theoretical and ML techniques provides a promising approach for developing a robust prediction model of material properties.

Evaluating generalized feature importance via performance assessment of machine learning models for predicting elastic properties of materials

Identifying key descriptors and understanding important features across different classes of materials are crucial for machine learning (ML) tools to both predict material properties and reveal the physics underlying any process of interest. Traditionally, the predictive modeling of elastic properties of materials is limited to only a few classes of materials and a small set of ML tools despite the broad applications of these materials. Users now have a broad choice of ML models ranging from simple regression models to graph neural networks (GNN) for predicting structure–property relationships. While in recent years, GNNs have outshined traditional ML models in terms of predictability, their intensive data requirement and lack of interpretability may limit practical applicability. Here, we develop a domain-segmented feature space using a diverse set of material attributes and perform a predictive analysis using state-of-the-art ML tools using elastic modulus prediction as a representative example. By deducing a model-independent overall ranking based on feature importance learned by each model, the knowledge is then transferred to GNNs. Our findings indicate a threshold limit on the predictability of traditional ML models, but our approach of transferring task-specific feature importance knowledge to the GNNs can enhance their performance by reducing their data requirement while retaining considerable accuracy.

Inline Determination of the Gel Elastic Modulus During Milk Coagulation Using a Multifiber Optical Probe

AbstractFrom a state-of-the-art point of view, it is currently possible to optically monitor the enzymatic coagulation of milk for real time estimation of the elastic modulus to cut the gel at optimum gel firmness. However, European cheese industry produces a wide variety of cheeses, many of them artisanal, and has a very fragmented productive structure with many small-, medium-sized companies. Therefore, if the technology is to be successfully uptake, it must be not only accurate but very low-cost. The objective of this work was to evaluate a low-cost commercial multifiber probe, for inline optical determination of curd firmness during cheese making. Preliminary tests were carried out to select the most appropriate fiber core size and wavelength and after that coagulation trials were performed following a fully randomized factorial design with two factors, i.e., concentration of protein (3.2, 3.6 and 4.0%) and added calcium (150, 200 and 250 mg L−1), with three replicates. The observed linear increase of the least square means of the initial voltage with the protein content (V0 = 0.15[P, %] + 0.88; R2 = 0.999), will be likely synergistic with the elastic modulus prediction if the model needs to be corrected for protein. Finally, the multifiber probe allowed predicting curd firmness using the proposed model with SEP values < 7 Pa. The present work has proven that a low-cost multifiber probe is suitable for accurate, real-time prediction of curd firmness during cheese manufacture.

Static elastic modulus prediction at early ages using a modified resonant test for concrete cylinders using mems microphones

Concrete Young’s modulus is seldom verified on-site due to the time-consuming and costly nature of current testing standards. This article proposes a new, quick and affordable testing methodology to estimate the ultimate static elastic modulus of normal strength concrete cylinders on-site at early ages using a smartphone. Impact-resonance tests were performed on several concrete cylinders from different mixtures at different ages, using an accelerometer and a smartphone microphone to measure the cylinders' flexural and longitudinal resonant frequencies. Measurements from both sensors were compared to show the validity of estimating the dynamic elastic modulus using acoustic signals from the cylinders recorded with a smartphone's built-in microphone. The cylinders' static and dynamic elastic moduli were determined, and the change in elastic modulus over time as the cylinders aged was also examined. This study shows the advantages of microphones over accelerometers, being less susceptible to measurement errors and proposes a filter algorithm to extract the resonant frequencies from environmental noise. A forecasting methodology to estimate static elastic modulus based on early dynamic impact-resonant tests can help engineers make early decisions on their supplied concrete on-site.

Machine Learning and Data Fusion Approach for Elastic Rock Properties Estimation and Fracturability Evaluation

Accurate rock elastic property determination is vital for effective hydraulic fracturing, particularly Young's modulus due to its link to rock brittleness. This study integrates interdisciplinary data for better predictions of elastic modulus, combining data mining, experiments, and calibrated synthetics. We used the microstructural insights extracted from rock images for geomechanical facies analysis. Additionally, the petrophysical data and well logs were correlated with shear wave velocity (Vs) and Young's modulus. We developed a machine-learning workflow to predict Young's modulus and assess rock fracturability, considering mineral composition, geomechanics, and microstructure. Our findings indicate that artificial neural networks effectively predict Young's modulus, while K-Means clustering and hierarchical support vector machines excel in identifying rock and geomechanical facies. Utilizing Microscale thin section analysis in conjunction with fracture modeling enhances our understanding of fracture geometries and facilitates fracturability assessment. Notably, fracturability is controlled by specific geomechanical facies during initiation and propagation and influenced by continuity of geomechanical facies in small depth intervals. In conclusion, this study demonstrates data mining and machine learning potential for predicting rock properties and assessing fracturability, aiding hydraulic fracturing design optimization through diverse data and advanced methods.

Prediction Models of Mechanical Properties of Jute/PLA Composite Based on X-ray Computed Tomography.

The tensile strength and modulus of elasticity of a jute/polylactic acid (PLA) composite were found to vary nonlinearly with the loading angle of the specimen through the tensile test. The variation in these properties was related to the fiber orientation distribution (FOD) and fiber length distribution (FLD). In order to study the effects of the FOD and FLD of short fibers on the mechanical properties and to better predict the mechanical properties of short-fiber composites, the true distribution of short fibers in the composite was accurately obtained using X-ray computed tomography (XCT), in which about 70% of the jute fibers were less than 300 μm in length and the fibers were mainly distributed along the direction of mold flow. The probability density functions of the FOD and FLD were obtained by further analyzing the XCT data. Strength and elastic modulus prediction models applicable to short-fiber-reinforced polymer (SFRP) composites were created by modifying the laminate theory and the rule of mixtures using the probability density functions of the FOD and FLD. The experimental measurements were in good agreement with the model predictions.

Mechanical properties of corrugated structure with negative Poisson’s ratio and its application on energy absorbing box based on parametric modeling method

A novel corrugated structure is proposed owing to its "compression-contraction" negative Poisson’s ratio characteristics. Firstly, theoretical predictions of Poisson's ratio, elastic modulus and platform stress for corrugated structure are derived. Then, finite element models of corrugated structure are established by utilizing parametric modeling approach based on secondary development of Abaqus. Moreover, quasi-static compression test of corrugated structure is executed to verify the accuracy of theoretical predictions and parametric models. Subsequently, the influences of geometric parameters and various base material combinations on mechanical properties of corrugated structure are investigated, and the optimal base material combination Al-ABS is obtained. Afterwards, impact performances of Al-ABS corrugated structure under various impact velocities and impact angles are studied, and deformation modes, stress-strain curves and specific energy absorptionSEAare acquired. In addition, the accuracy and reliability of the FEM of energy absorbing box embedded with corrugated structure is verified through energy curves and quasi-static compression test on 3D printed energy absorbing box. Finally, multi-objective optimization design on energy absorbing box embedded with Al-ABS corrugated structure is performed with design variables of θ,r1,r2,d and optimization objectives of minimum massMs, minimum peak crushing forcePCF and maximum energy absorption EA by employing NSGA III algorithm. Compared with un-optimized corrugated structure energy absorbing box, the MsandPCFof post-optimized box decrease by 1.31%, 13.98%, accordingly, and the EA and SEA of post-optimized box increase by 10.86% and 12.33%, separately.

Optimization of RF to alloy elastic modulus prediction based on cuckoo algorithm

In recent years, there has been an explosive rise in the combination of density-functional theory (DFT) computation and machine learning in materials science research. In this paper, a cuckoo algorithm-optimized random forest (CS-RF) based alloy elastic modulus prediction model is proposed using density-functional theory. 320 alloy samples are used as test materials to calculate elastic modulus using the density-functional theory method, and the calculated raw data are normalized to establish a cuckoo algorithm-optimized random-forest model to predict alloy elastic modulus. The experiments show that for predicting the elastic modulus of alloys, CS-RF also has obvious advantages in computational efficiency with improved prediction accuracy. In conclusion, CS-RF is an advanced machine learning (ML) method for predicting the elastic modulus of alloys, which can provide a scientific reference for metal elastic modulus prediction.

A Novel Method of Self-Healing Concrete to Improve Durability and Extend the Service Life of Civil Infrastructure

Concrete civil infrastructure often suffers severe damage to its internal structure due to insufficient durability in high and cold complex environments, affecting the service life of the infrastructure. Therefore, a novel method based on nanofillers for self-healing concrete is proposed to optimize the durability mix proportion of high-performance concrete in complex high and cold environments, which improves the strength recovery rate of concrete. Moreover, a concrete durability prediction model based on particle swarm optimization-least squares support vector machine (PSO-LSSVM) and improved NSGA-II (nondominated sorting genetic algorithm II) algorithm was proposed to quickly and accurately determine the optimization scheme of self-healing concrete mix proportion. First, the model employs PSO-LSSVM to achieve highly accurate predictions of relative dynamic elastic modulus and chloride ion permeability coefficient, which are key indicators of the concrete durability. Subsequently, the predicted regression functions for concrete durability are utilized as fitness functions, and the improved NSGA-II algorithm is employed to obtain the optimal mix ratio for durable concrete. Finally, the Pareto frontier solution set is processed using the ideal point method selection approach to determine the optimal concrete mix ratio scheme. To assess the self-healing ability of the proposed concrete, a concrete durability test study is conducted. Experimental research shows that the durability of the proposed self-healing concrete has been significantly improved. The optimized PSO-LSSVM model demonstrates excellent generalization capability. The coefficient of determination between the predicted and actual values in the test dataset is 0.9357, and the root-mean-square error is 0.10267. Building upon this, the enhanced concrete durability prediction model based on the NSGA-II algorithm proves to be highly effective in predicting the optimal concrete mix proportion scheme. The predicted values of chloride ion permeability coefficient and relative dynamic elastic modulus of the proposed model differ from the actual experimental values by 1.29% and 0.59%, significantly better than the other prediction models.

Prediction of Compressive Strength and Elastic Modulus for Recycled Aggregate Concrete Based on AutoGluon

While the civil construction industry brings great convenience to life, the large amount of waste concrete also poses a significant problem of construction waste disposal. As one of the effective ways to utilize waste concrete, recycled aggregate concrete (RAC) can improve the environment while reducing the consumption of construction materials. This study aims to use AutoGluon (AG), an automated machine learning platform, to predict both the compressive strength and elastic modulus of RAC. Then the performance of AG is compared with traditional empirical formulas and multiple linear regression models. The determination coefficient (R2) is chosen as one of the evaluation standards for predicting values. The results demonstrate that the WeightedEnsemble model of AG performed best in predicting both the compressive strength and elastic modulus, which provides a new method for the rapid and accurate prediction of the properties of RAC in engineering construction.

Prediction of tensile elastic modulus of SiC/SiC mini-composites with the artificial neural network

Prediction of tensile elastic modulus of SiC/SiC mini-composites with the artificial neural network

Three-dimensional reconstruction and computational analysis of a structural battery composite electrolyte

Structural batteries are multifunctional composite materials that can carry mechanical load and store electrical energy. Their multifunctionality requires an ionically conductive and stiff electrolyte matrix material. For this purpose, a bi-continuous polymer electrolyte is used where a porous solid phase holds the structural integrity of the system, and a liquid phase, which occupies the pores, conducts lithium ions. To assess the porous structure, three-dimensional topology information is needed. Here we study the three-dimensional structure of the porous battery electrolyte material using combined focused ion beam and scanning electron microscopy and transfer into finite element models. Numerical analyses provide predictions of elastic modulus and ionic conductivity of the bi-continuous electrolyte material. Characterization of the three-dimensional structure also provides information on the diameter and volume distributions of the polymer and pores, as well as geodesic tortuosity.

Numerical Modeling of the Dynamic Elastic Modulus of Concrete.

This article introduces simulations of theoretical material with controlled properties for the evaluation of the effect of key parameters, as volumetric fractions, elastic properties of each phase and transition zone on the effective dynamic elastic modulus. The accuracy level of classical homogenization models was checked regarding the prediction of dynamic elastic modulus. Numerical simulations were performed with the finite element method for evaluations of the natural frequencies and their correlation with Ed through frequency equations. An acoustic test validated the numerical results and obtained the elastic modulus of concretes and mortars at 0.3, 0.5 and 0.7 water-cement ratios. Hirsch calibrated according to the numerical simulation (x = 0.27) exhibited a realistic behavior for concretes of w/c = 0.3 and 0.5, with a 5% error. However, when the water-to-cement ratio (w/c) was set to 0.7, Young's modulus displayed a resemblance to the Reuss model, akin to the simulated theoretical triphasic materials, considering matrix, coarse aggregate and a transition zone. Hashin-Shtrikman bounds is not perfectly applied to theoretical biphasic materials under dynamic situations.

Study on Dynamic Modulus and Damping Characteristics of Modified Expanded Polystyrene Lightweight Soil under Cyclic Load.

In recent years, expanded polystyrene (EPS) lightweight soil has been widely used as subgrade in soft soil areas because of its light weight and environmental protection. This study aimed to investigate the dynamic characteristics of sodium silicate modified lime and fly ash treated EPS lightweight soil (SLS) under cyclic loading. The effects of EPS particles on the dynamic elastic modulus (Ed) and damping ratio (λ) of SLS were determined through dynamic triaxial tests at various confining pressures (σ3), amplitudes, and cycle times. Mathematical models of the Ed of the SLS, cycle times, and σ3 were established. The results revealed that the EPS particle content played a decisive role in the Ed and λ of the SLS. The Ed of the SLS decreased with an increase in the EPS particle content (EC). The Ed decreased by 60% in the 1-1.5% range of the EC. The existing forms of lime fly ash soil and EPS particles in the SLS changed from parallel to series. With an increase in σ3 and amplitude, the Ed of the SLS gradually decreased, the λ generally decreased, and the λ variation range was within 0.5%. With an increase in the number of cycles, the Ed of the SLS decreased. The Ed value and the number of cycles satisfied the power function relationship. Additionally, it can be found from the test results that 0.5% to 1% was the best EPS content for SLS in this work. In addition, the dynamic elastic modulus prediction model established in this study can better describe the varying trend of the dynamic elastic modulus of SLS under different σ3 values and load cycles, thereby providing a theoretical reference for the application of SLS in practical road engineering.

Mechanical properties of 3D printed concrete with coarse aggregates and polypropylene fiber in the air and underwater environment

In this study, three-dimensional concrete printing (3DCP) was performed for concrete with coarse aggregates in air and underwater environments for a comparative evaluation of the mechanical properties of the obtained samples. The base concrete mix was selected based on normal weight concrete (NWC) with ordinary Portland cement (OPC) as a binder, and the mechanical properties of the printed concrete applied using 3DCP and mold cast concrete were compared for analysis. For printability, fiber-reinforced concrete (FRC) with polypropylene fiber was additionally considered as a variable, and the fabrication method (printing and mold casting) and conditions (air and underwater environments) were varied. For the specimens fabricated using different materials, fabricating methods, and fabricating environments, the apparent density, compressive strength, splitting tensile strength, and stress–strain curves were comparatively analyzed. Considering the static elastic modulus calculated from the stress–strain curve, the correlation between the values obtained using design codes and those obtained via elastic modulus prediction models presented in previous studies was comparatively analyzed. When the concrete fabricated using the mold casting method was compared with the printed specimen, the density of the mold cast concrete was higher by 34 kg/m3 compared to that of the printed specimen, and the specimen fabricated in the air showed a density that was 45 kg/m3 greater compared to that of the specimen fabricated underwater. The compressive strength (28 days) was found to be approximately 8% higher in NWC compared to that in FRC, and the compressive strength of the underwater specimen was about 72% of that of the air specimen.

Multi-domain equivalent method for prediction of elastic modulus of complex fractured rock mass

Multi-domain equivalent method for prediction of elastic modulus of complex fractured rock mass

Interphase characterization of glass/epoxy composite using peridynamic method and micro tensile test

ABSTRACT In this paper, a novel procedure is developed to characterize the interphase region using the nonlocal peridynamic method and micro tensile tests of glass/epoxy specimens with a single fiber of glass. For this purpose, micro tensile tests are performed by an accurate tensile test device and high-quality camera imaging for data sampling. The obtained displacements are analysed by the image processing method and used as target values in the peridynamic analyses. Multiscale mass points from macro to micro scale are generated to model the interphase region in single fiber specimens using the state-based peridynamic method. With the coupling of peridynamic and multivariate optimization codes, the program is automatically executed by the prediction of thickness and elastic modulus of interphase to achieve the desired displacements similar to micro tensile test results. In this innovative method, unlike nanoindentation tests, where the results are limited to a specific section or point, the interphase characterization gives an overall elastic modulus value along the examined length of the sample. The obtained results from the presented procedure show that the interphase elastic modulus is between 20.8 and 28.86 GPa and the interphase thickness is between 1.149 and 1.986 microns. The obtained results are in the range of nanoindentation tests results presented in the literature, and the difference could be due to the manufacturing conditions, epoxy properties, and quality of the fiber silane coatings.