Prediction Of Strength Research Articles

Prediction of the Unconfined Compressive Strength of a One-Part Geopolymer-Stabilized Soil Using Deep Learning Methods with Combined Real and Synthetic Data

Prediction of the Unconfined Compressive Strength of a One-Part Geopolymer-Stabilized Soil Using Deep Learning Methods with Combined Real and Synthetic Data

Local buckling behaviour of web perforated cold-formed steel lipped channel columns

To connect beams and bracings with storage rack uprights, closely spaced perforations are provided along the web, flanges, and rear flanges of uprights. These perforations can significantly lower the ultimate capacity of such compression members with the possible influence of its natural buckling modes. This capacity reduction can depend on various parameters such as (a) geometrical shape, proportioning of cross-section, and stiffeners; (b) perforation shape, size, spacing, and location; (b) slenderness of member, cross-section, and elements of cross-section; and (d) material properties. A thorough understanding of the influence of the above-mentioned factors is necessary for the accurate strength prediction of perforated Cold-formed steel (CFS) compression members. Even though the current design standards are updated for the accurate strength prediction of unperforated CFS compression members, they do not collectively account for the influence of all the aforementioned factors on the load-carrying capacity of the perforated CFS members, particularly for the local buckling capacity. Though the Direct Strength Method (DSM) of design is the most accepted method for accurate strength prediction of CFS members even for complex cross-sectional shapes, recent research on the strength evaluation of perforated CFS members using DSM has emphasized the need for refinement in DSM. The Modified Direct Strength Method (MDSM), which accounts for the simultaneous buckling of flanges and web, includes the cross-section aspect ratio and cross-section slenderness to predict more accurately the local buckling design strength. However, it was developed only for unperforated specimens. Hence, a systematic experimental and numerical investigation was done to understand the influence of the perforation in the local buckling behavior of the lipped channel section. In total, 14 specimens, including 2 unperforated and 12 web perforated CFS lipped channel stub columns were physically tested with fixed support conditions. The Finite Element Analysis using ABAQUS software was used to conduct an extensive parametric numerical study. The results were used to compare the strength curves of DSM and MDSM and the modification in the design curves has been proposed by considering the erosion in strength due to the presence of perforation.

Investigating the change mechanism and quantitative analysis of minced pork gel quality with different starches using Raman spectroscopy

Minced pork gel often faces challenges during preparation, such as sticking and splitting, which can result in a loose structure, significantly compromises its quality. This study aimed to improve minced pork gel quality by incorporating different starches: mung bean starch (MBS), potato starch (POS), pea starch (PES), and corn starch (CS), focusing on enhancing water-holding capacity (WHC) and gel strength. Raman spectroscopy was employed to investigate the mechanisms behind gel quality changes and to develop quantitative predictions of WHC and gel strength in response to starch addition. The results showed that 15% starch addition was optimal, with POS performing best. Notably, starch addition promoted α-helix and random coil transformation into β-sheet and β-turn in protein structure. Additionally, the starch-minced pork gel system exhibited enhanced cross-linking and density through hydrophobic interactions, contributing to improved WHC and gel strength. Finally, to quantitatively assess the gel quality change under different starches, support vector machine (SVM), uninformative variable elimination-SVM (UVE-SVM), genetic algorithm-SVM (GA-SVM), and adaptive reweighted sampling-SVM (CARS-SVM) modeling were built. Among these, UVE-SVM model achieved the optimal performance for both WHC (Rp2 = 0.8553, RPD = 2.1567) and gel strength (Rp2 = 0.8508, RPD = 2.1981). Therefore, this study demonstrates that the addition of starch, particularly POS, can enhance minced pork gel quality by improving both WHC and gel strength. Moreover, it establishes Raman spectroscopy coupled with UVE-SVM as a reliable method for non-destructive, and rapid detection of these quality parameters in minced pork gel under different starch conditions.

Data-driven prediction of drilling strength ahead of the bit

This paper compares the performance of two data-driven methods, Signal-Matching Predictor (SMP) and Long Short-Term Memory (LSTM), for predicting drilling strength (Es) ahead of the bit based on drilling data from nearby offset wells. The comparison is based on the accuracy, applicability, complexity, and computational cost of the methods with the objective of suggesting the most appropriate tool for look-ahead drilling strength prediction. The methods were tested using data from offshore wells in Newfoundland. The SMP used a fixed-size sliding-window of real-time Es data from the target well to find a match in the offset well within similar geological formations and chose the scaled value from the offset well as the prediction. In the second approach, twelve LSTM models were trained using the drilling data of twelve offset wells, and the drilling data of the thirteenth well was used for blind testing. Results showed that the SMP achieved a coefficient of determination (R2) of 0.92, 0.92, and 0.79 for predicting 1.5, 3, and 5 feet ahead of the bit, respectively, while the LSTM reached an R2 of 0.95, 0.92, and 0.80 for the respective prediction intervals. The R2 of the LSTM models was further increased to 0.96, 0.94, and 0.83 after retraining it with weighted samples in formation transition zones. Also, a post-processing technique was proposed that further enhanced the R2 of the LSTM-based approach to 0.98, 0.97, and 0.93, respectively. The strength of the LSTM-based approach was to use measurable drilling parameters as the only inputs and not the Es itself. According to the results, the LSTM-based method can be reliably used to predict the Es ahead of the bit allowing drillers to identify upcoming drilling dysfunctions.

Random measurement and prediction errors limit the practical relevance of two velocity sensors to estimate the 1RM back squat.

While maximum strength diagnostics are applied in several sports and rehabilitative settings, dynamic strength capacity has been determined via the one-repetition maximum (1RM) testing for decades. Because the literature concerned several limitations, such as injury risk and limited practical applicability in large populations (e.g., athletic training groups), the strength prediction via the velocity profile has received increasing attention recently. Referring to relative reliability coefficients and inappropriate interpretation of agreement statistics, several previous recommendations neglected systematic and random measurement bias. This article explored the random measurement error arising from repeated testing (repeatability) and the agreement between two common sensors (vMaxPro and TENDO) within one repetition, using minimal velocity thresholds as well as the velocity = 0m/s method. Furthermore, agreement analyses were applied to the estimated and measured 1RM in 25 young elite male soccer athletes. The results reported repeatability values with an intraclass correlation coefficient (ICC) = 0.66-0.80, which was accompanied by mean absolute (percentage) errors (MAE and MAPE) of up to 0.04-0.22m/s and ≤7.5%. Agreement between the two sensors within one repetition showed a systematic lower velocity for the vMaxPro device than the Tendo, with ICCs ranging from 0.28 to 0.88, which were accompanied by an MAE/MAPE of ≤0.13m/s (11%). Almost all estimations systematically over/ underestimated the measured 1RM, with a random scattering between 4.12% and 71.6%, depending on the velocity threshold used. In agreement with most actual reviews, the presented results call for caution when using velocity profiles to estimate strength. Further approaches must be explored to minimize especially the random scattering.

Prediction and optimization framework of shear strength of reinforced concrete flanged shear wall based on machine learning and non-dominated sorting genetic algorithm-II

Reinforced concrete (RC) flanged shear wall has good lateral strength and stiffness, which has been widely used in building structures. Due to the coupling effect of many factors such as wall section shape, shear span ratio, so the shear performance evaluation of flanged wall is still very limited. This paper proposed a prediction framework for the shear capacity of RC flanged shear walls. A database containing 14 input variables, 1 output variable and 153 samples was constructed to evaluate the prediction accuracy of 11 existing design methods. The Pearson coefficient was used to preliminarily analyze the correlation between variables. The grid search was used to optimize the hyperparameters of 4 machine learning models, and six statistical indicators ( R2, R, RMSE, SD, MAE, and MAPE) were used to comprehensively compare the prediction results of the ML models to determine the best model. On this basis, SHapley Additive exPlanations (SHAP) was used to enhance the interpretability of the prediction models, and the mechanism of the input variables on the shear capacity was quantified. A graphical user interface (GUI) was proposed to guide the engineering design. A multi-objective model (MOO) was established to analyze the trade-off between shear performance and cost, thereby determining the best optimal scheme. The results show that the prediction accuracy of the ML models is better than the existing design methods. The XGB model has the best prediction performance, with R2, R, RMSE are 0.99, 0.99, 118.96, respectively. The SHAP method can effectively enhance the interpretability of the ML models, and tw, lw and f ′c are the key parameters affecting the shear capacity of the flanged shear wall.

Axial compressive performance of spirally reinforced high-strength CFST column

The concrete-filled steel tubular (CFST) member using high-strength materials could have less ductility than its counterpart using normal-strength materials. In this study, spiral reinforcement is used to enhance the performance of CFST columns using high-strength materials. Experiments were conducted for spirally reinforced high-strength CFST stub columns. The highest yield strengths of spiral reinforcement and steel tube were 1597.6 MPa and 771.7 MPa, respectively, and the highest compressive strength of concrete was 103.1 MPa. Results showed that the presence of high-strength spiral reinforcement effectively improved both the strength and ductility of composite columns, while the fracture of spiral reinforcement was observed in tests. A numerical study revealed that spiral reinforcement provided effective confinement to the high-strength core concrete. The steel tube and spiral reinforcement reached respective yield strength corresponding to peak load. The spirally reinforced CFST column exhibited higher resistance than the equivalent normal CFST column using a thick tube. A design-oriented strength prediction method was proposed and was validated against the test data. The average ratio of formula-predicted to experimental resistances was 0.934, with a coefficient of variation being 0.079.

Deep learning neural networks for monitoring early-age concrete strength through a surface-bonded PZT sensor configuration

Monitoring the immediate evolution of concrete’s strength is essential to ensure structural integrity and construction efficiency, requiring Forecasting to avoid unforeseen and severe failures during construction. The present investigation introduces an Equivalent structural parametric (ESP) study using a surface-bonded piezo sensor and electromechanical impedance (EMI) methodology to monitor and forecast the strength of concrete by implementing machine learning and deep learning methods. The concrete hydration processes are simulated using COMSOL™ 5.5. The concrete cube’s hydration is represented by altering Young’s modulus and damping ratio to show the rate of curing. Concrete strength development is examined in terms of conductance resonant frequency (CRF) and Conductance resonant peak (CRP). Continuous conductance signature monitoring and data analysis show that CRF and CRP increase with compressive strength. The system’s mechanical impedance is measured, and EMI signatures vs. frequency plots within a specific frequency range are compared to healthy impedance graphs. A mass-spring-damper system with identical properties is identified, and relevant structural parameters are computed. Modern ML algorithms include linear regression (LR), interaction LR, fine, medium, and coarse Gaussian SVM, etc. reliably predict strength with an error rate of less than 2%. Convolutional neural networks (CNN) have advanced image-based recognition, but their usage in EMI-based structural strength assessment is still being studied. A unique strategy using 2D CNN, 2D CNN– Long short-term memory (LSTM), and 2D CNN Bidirectional-LSTM to forecast concrete structure compressive strength shows the potential of deep learning. The proposed 2D CNN-Bi-LSTM model excels in compressive strength prediction, obtaining an R2 value of 0.99 and stabilizing loss error over a few epochs.

Performance of self-compacting concrete with treated rice husk ash at different curing temperatures

Self-compacting concrete (SCC) is currently gaining traction as a replacement to conventional vibrated concrete. Its distinct microstructure leads to varied mechanical behaviour under different curing temperatures. In the past various supplementary cementitious materials (SCMs) were used in SCC to investigate their respective effects on the performance. However, there has been no systematic studies conducted to determine the effect of different curing temperature on the sensitivity reaction of rice husk ash (RHA) in SCC. This research focuses on high-strength SCC with SCMs such as RHA, silica fume (SF), fly ash (FA), and ground granulated blast-furnace slag (GGBS), exploring their applicability for concrete structures under varying curing temperatures. Heat of hydration, compressive strength and open porosity of SCC specimens were assessed at various temperature. Results indicate high curing temperatures expedite the hydration and pozzolanic reaction, refining the microstructure and increasing the early-age concrete strength, but compromising the long-term performance, potentially mitigated by the use of SCMs. Conversely, lower curing temperature, impedes hydration leading to gradual strength gain, particularly with SCMs, yet yielding significant strength increases at later concrete age. SCMs presence and curing temperature significantly influence maturity function-based strength predictions, impacting strength trends in the samples studied.

Response Surface Design Models to Predict the Strength of Iron Tailings Stabilized with an Alkali-Activated Cement

Tailing storage facilities are very complex structures whose failure generally leads to catastrophic consequences in terms of casualties, serious environmental impacts on local biodiversity, and disruptions in the mineral supply. For this reason, structures at risk must be reinforced or decommissioned. One possible option is its reinforcement with compacted filtered tailings stabilized with binders. Alkali-activated binders provide a more sustainable solution than ordinary Portland cement but require an optimization of the tailing–binder mixture, which, in some cases, can lead to a substantial experimental effort. Statistical models have been used to reduce the number of those experiments, but a rational design methodology is still lacking. This methodology to define the right mixture for a required strength should consider both the mixture components and in situ conditions. In this paper, response surface methods were used to plan and interpret unconfined compression strength test results on an iron tailing stabilized with alkali-activated binders. It was concluded that the fly ash content was the most important parameter, followed by the liquid content and sodium hydroxide concentration. From the obtained results, several statistical models were defined and compared according to the definition of a strength prediction model based on a mixture index parameter. It was interesting to observe that models with the porosity cement index still provide reasonable adjustment even when different tailings’ water contents are considered.

Electrical resistance based residual strength prediction method for SiC/SiC mini-composites after stress-free oxidation

A stress-free oxidation model considering the microstructural characteristics of SiC/SiC mini-composites was proposed based on the electrical resistance change before and after oxidation. The relationship between the electrical resistance and the thickness of the internal fiber oxide layer was established. The stress-free oxidation tests were carried out at different temperatures for different oxidation times. The results of the oxide layer thickness calculated by the model agree well with those of the SEM observation. Based on the microstructure of tensile fracture after oxidation, the residual strength model of SiC/SiC mini-composites was proposed. The residual strength of the specimens after stress-free oxidation was calculated based on the oxide layer thickness obtained from the model. The error between the calculated and experimental results is within 14.48 %.

A Prediction Model for the Unconfined Compressive Strength of Pervious Concrete Based on Mix Design and Compaction Energy Variables Using the Response Surface Methodology

Pervious concrete is desirable for water drainage in building systems, but achieving both high strength and good permeability can be challenging. Also, the importance of compaction energy is significant in determining the efficiency of pervious concrete. However, research on the development of unconfined compressive strength (UCS) prediction models for pervious concrete materials that incorporate compaction energy parameters remains unexplored. Therefore, this study aimed to balance strength and permeability while optimizing the compaction energy required for concrete production. A Central Composite Design (CCD) was used to design experiments within the response surface methodology (RSM) and evaluate the UCS, the porosity and permeability of pervious concrete specimens produced with varying cement content (280.00–340.00 kg/m3), the water-to-cement ratio (0.27–0.33), the aggregate-to-cement ratio (4:1–4.5:1), and compaction energy (represented by VeBe compaction time, 13–82 s). A regression model with goodness of fit (R2adjusted > 0.87) was calibrated to estimate the UCS of pervious concrete as a function of mix design parameters and VeBe compaction time (Tvc). This model can potentially guide field practices by recommending compaction strategies and mix designs for pervious concrete, achieving a desirable balance between mechanical strength and hydraulic permeability for building construction applications.

Machine learning assisted design of new ductile high-entropy alloys: Application to Al-Cr-Nb-Ti-V-Zr system

The search for new high-entropy alloys (HEAs) with desired properties is an urgent problem that is hardly solvable experimentally due to the extremely large number of possible alloy compositions. Thus, methods for theoretical prediction of HEA's properties play a key role. Currently, effective predictive models are based on machine learning methods and modern data analysis algorithms. Here we address developing data-driven machine learning models (DDML) to predict the ductility of HEAs. We have built several DDMLs and found that the best approach is based on the Support Vector Classifier, which significantly outperforms phenomenological models (balanced accuracy of 0.784 and F-score of 0.824). By combining this model with a previously developed yield strength prediction model, we have predicted and fabricated novel HEAs of the Al-Cr-Nb-Ti-V-Zr system with good mechanical properties. An obtained Al1Cr9Nb35Ti5V40Zr10 alloy demonstrates a combination of high strength at room and elevated temperature, combined with good ductility at room temperature.

Explainable Ensemble Learning and Multilayer Perceptron Modeling for Compressive Strength Prediction of Ultra-High-Performance Concrete.

The performance of ultra-high-performance concrete (UHPC) allows for the design and creation of thinner elements with superior overall durability. The compressive strength of UHPC is a value that can be reached after a certain period of time through a series of tests and cures. However, this value can be estimated by machine-learning methods. In this study, multilayer perceptron (MLP) and Stacking Regressor, an ensemble machine-learning models, is used to predict the compressive strength of high-performance concrete. Then, the ML model's performance is explained with a feature importance analysis and Shapley additive explanations (SHAPs), and the developed models are interpreted. The effect of using different random splits for the training and test sets has been investigated. It was observed that the stacking regressor, which combined the outputs of Extreme Gradient Boosting (XGBoost), Category Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), and Extra Trees regressors using random forest as the final estimator, performed significantly better than the MLP regressor. It was shown that the compressive strength was predicted by the stacking regressor with an average R2 score of 0.971 on the test set. On the other hand, the average R2 score of the MLP model was 0.909. The results of the SHAP analysis showed that the age of concrete and the amounts of silica fume, fiber, superplasticizer, cement, aggregate, and water have the greatest impact on the model predictions.

Informatics-enhanced prediction of failure strength in skeletal muscle tissue

Skeletal muscles are susceptible to injury during daily activities and competitive sports. Reliable prediction of tissue failure strength is a major challenge due to the large uncertainty in the mechanical behavior of skeletal muscle tissue. The present study reveals a strong correlation between histological characteristics and skeletal muscle failure strength by means of mechanical and histological experiments. We propose a data-driven hybrid modeling approach that enables an effective integration of data science and informatics tools to capture tissue failure strength. Uncertainty in the tissue failure strength is propagated into the posterior information of reduced model parameters via the Bayesian inference framework and parameter space compression. A histological enhancement to the softening hyperelasticity model is made by linking a quantified tissue-scale histological characteristic and stiff model parameters using artificial neural networks. The model is applied to skeletal muscle tissue from different species and sites to assess its predictive capabilities for physiological differences. The results show that the approach can achieve reliable predictions of skeletal muscle tissue failure strength. The proposed approach can be extended to different scales to enrich the understanding of structure–property linkages for biomaterials.

Modelling of Bond Formation during Overprinting of PEEK Laminates.

The rapid technological progress of large-scale CNC (computer numerical control) systems for Screw Extrusion Additive Manufacturing (SEAM) has made the overprinting of composite laminates a much-discussed topic. It offers the potential to efficiently produce functionalised high-performance structures. However, bonding the 3D-printed structure to the laminate has proven to be a critical point. In particular, the bonding mechanisms must be precisely understood and controlled to ensure in situ bonding. This work investigates the applicability of healing models from 3D printing to the overprinting of thermoplastic laminates using semi-crystalline, high-performance material like PEEK (polyether ether ketone). For this purpose, a simulation methodology for predicting the bonding behaviour is developed and tested using experimental data from a previous study. The simulation consists of a transient heat analysis and a diffusion healing model. Using this model, a qualitative prediction of the bond strength could be made by considering the influence of wetting. It was shown that the thermal history of the interface and, in particular, the tolerance of the deposition of the first layer are decisive for in situ bonding. The results show basic requirements for future process and component developments and should further advance the maturation of overprinting.

Optimization of Flexural Strength of Sawdust Ash Blended Geopolymer Concrete

release of greenhouse gases linked to the manufacture of cement. Geopolymer as a binder for making concrete consists of two (2) main components; (1) the alkaline liquid consisting of sodium or potassium silicate and sodium or potassium hydroxide and (2) source material of geological origin or by-products rich in silica and alumina. The combination proportions utilised in this investigation were formulated utilising Scheffe's (5,2) basic lattice mix design approach with the intent to create the trial mix and the control mix. A total of thirty (30) geopolymers concrete sample mixes were made in the laboratory, with fifteen samples for trial mixes and fifteen mixtures for control mixes. These mixtures were used to appraise the performance of the sawdust ash geopolymer concrete in term of its flexural strength property. The study used sawdust ash as the source material and investigation revealed that subjecting sawdust ash to pyrolysis without oxygen has a notable impact on the pozzolanic characteristics of the constituent. Consequently, this also affects the flexural qualities of the concrete. Furthermore, it has been shown that softwood sawdust exhibits superior pozzolanic properties when compared to hardwood sawdust. The study revealed that the optimum flexural strength of sawdust ash blended geopolymer concrete is 3.3002 MPa and the corresponding mix deign obtained. Computer programs were created using Matlab and used for the optimization and prediction of the flexural strength of sawdust ash based geopolymer concrete.

Regression prediction model for shear strength of cold joint in concrete

The shear resistance of cold joint in concrete is influenced by various design parameters. Traditional mechanical models typically consider only a limited number of parameters to predict the shear strength of cold joints. This research aims to explore the complex nonlinear relationship between design parameters and cold joint shear strength using statistical method and machine learning technology. The goal is to develop a more accurate, reliable, and engineering applicable regression model for predicting shear strength. A dataset of 546 Z-shaped shear specimens characterizing cold joint in concrete was constructed, involving a total of 16 variables that may affect shear performance. Correlation analysis and recursive elimination were adopted to eliminate correlated and insignificant variables based on their importance. Multiple linear regression (MLR), random forest regression (RFR), and support vector machine regression (SVR) prediction models for cold joint shear strength were established based on rigorously screened variables and comprehensively evaluated using multiple methods. It was found that the most significant factors influencing the shear strength of cold joints are concrete strength, interface shear key, product of interface reinforcement strength and its reinforcement ratio, normal stress, fiber length of new concrete, casting method of the new concrete, and product of the fiber length and its tensile strength of old concrete. The MLR, SVR, and RFR models all exhibited superior performance relative to traditional mechanics-based models with regard to shear strength prediction of cold joints. The RFR model is recommended for predicting the shear strength of cold joints due to its superior evaluation indexes in comparison to the MLR and SVR models, and variable sensitivity analysis shows that it does not yield common-sense errors.

Field strength prediction of 220 kV cable oil terminal defects based on multivariate nonlinear regression model

During the installation of a cable oil terminal, it is easy to leave scratches on the main insulation owing to uneven forces when removing the semi-conductive layer. Scratch defects cause field intensity distortion, which leads to partial discharge and insulation failure. This study attempts to establish a simulation model of a 220 kV cable terminal to determine the effect of the length, depth, and position of the scratch on the maximum field strength at the defect. The simulation and experiment demonstrate that the maximum field strength at the defect increases with greater length and decreases as the depth increases. Therefore, a prediction method for the terminal defect field strength based on a multivariate nonlinear regression model was proposed in this study. When the defect is located at 20 mm from the root of the stress cone, the maximum field strength is 14.5 MV/m when the length and depth are 2 mm and 1 mm, respectively. The maximum field strength at the defect was predicted based on the length, depth, and position of the scratch defect to evaluate the severity of the defect.

Predicting shear strength of fiber-reinforced concrete beams reinforced with longitudinal FRP bars with machine learning techniques

ABSTRACT This study investigates the shear strength prediction of Fibre-Reinforced Concrete (FRC) beams reinforced with Fibre-Reinforced Polymer (FRP) bars and without stirrups through the application of Machine Learning (ML) techniques. The utilisation of FRP bars, particularly Glass Fibre-Reinforced Polymer (GFRP) and Basalt Fibre-Reinforced Polymer (BFRP) bars, has emerged as a promising alternative to traditional steel reinforcement due to their superior mechanical properties and corrosion resistance. Moreover, the incorporation of macro- and micro-discrete fibres into concrete compositions enhances both shear and flexural behaviour while reducing crack propagation induced by tensile stresses. The primary objective of this research is to develop accurate predictive models for the shear capacity of FRP-reinforced concrete beams, considering various influential parameters. Six different ML techniques, namely Multiple Linear Regression (MLR), Gaussian Process Regression (GPR), k-Nearest Neighbors (KNN), Support Vector Machines (SVMs), Artificial Neural Networks (ANNs) and Random Forest Regression (RFR), are employed to analyse the complex interactions between input variables, such as fibre type, bar diameter, concrete compressive strength, beam dimensions and the resulting shear strength. By leveraging these computational approaches, we aim to overcome the limitations of conventional analytical methods and provide robust predictions for structural design and optimisation.