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In-situ quality monitoring in LPBF via melt-pool radiation:Compressive sampling and deep feature extraction 认领 引用 被引量：1
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作者 Hanxiang Zhou Yongqiang Yang +5 位作者 Vyacheslav Trofimov Hui Li Zibin Liu Yunmian Xiao Tuixin Chen Changhui Song 《Additive Manufacturing Frontiers》 CAS CSCD 2026年第2期26-38,共13页
In-situ monitoring methods and deep learning models are increasingly being used for the quality assessment of parts fabricated using laser powder bed fusion to overcome the limitations of poor process repeatability.Ho... In-situ monitoring methods and deep learning models are increasingly being used for the quality assessment of parts fabricated using laser powder bed fusion to overcome the limitations of poor process repeatability.However,the massive data collection required for part-quality monitoring results in high transmission loads and storage costs.To address this problem,this study utilized the compressed sensing theory to acquire compressed photodiode signals.These signals were then used to train and test convolutional neural networks(CNN)to identify the lack-of-fusion,normal,and keyhole modes.At a compressive-sampling rate of 25%,the classification accuracy decreased from 93.1%(raw signals)to 79.3%.However,increasing the compression rate from 25%to 90%did not significantly decrease the classification accuracy.The linear mapping of the raw signal via a Gaussian measurement matrix causes coordinate information folding,thereby impairing the representation of latent features.Therefore,Gaussian process modeling was adopted for the features extracted using a pretrained CNN to mitigate the temporal information collapse and allow the compressed signals to achieve an accuracy comparable to that of the raw data.Furthermore,the sparsity and rank complexity of the melt-pool radiation signals were evaluated using sparse representation and principal component analysis. 展开更多
关键词 Laser powder bed fusion Compressive sampling In-situ quality monitoring Convolutional neural network Gaussian process
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Compressive strength of hydrothermally solidified waste clay by industry waste:Experimental tests and hybrid models 认领 引用
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作者 Gangqiang Kong Shuaijun Hu +3 位作者 Yinzhe Hong Yonghui Chen Geng Chen Long Chen 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第3期2408-2420,共13页
Replacing cement clinker with solid residue is a highly promising decarbonization strategy in soil improvement.Waste clay was improved by desulfurized gypsum and carbide slag using eco-friendly low-temperature hydroth... Replacing cement clinker with solid residue is a highly promising decarbonization strategy in soil improvement.Waste clay was improved by desulfurized gypsum and carbide slag using eco-friendly low-temperature hydrothermal solidification technology in this study.The compressive strength of the hydrothermally solidified waste clay was investigated,and four hybrid prediction models(PSO-RF,PSO-GPR,PSO-GBR,and PSO-AdaBoost)were developed to predict its compressive strength.The results showed that the strength of the hydrothermally solidified waste clay significantly increased but then decreased when the Ca/Si ratio exceeded 0.8–0.9.Water has a dual effect on the strength of hydrothermally solidified waste clay,with an optimum range of approximately 10%–20%.Among the strength prediction models developed for hydrothermally solidified waste clay,the optimization performance and prediction accuracy of PSO-GBR are superior,and the UCS prediction results of other studies with various precursor types have been verified.SHapley Additive exPlanation(SHAP)analysis of the PSO-GBR model revealed that the pressed dry density contributes most significantly to the compressive strength of hydrothermally solidified waste clay,followed by the autoclave temperature,water content,Ca/Si ratio,and autoclave temperature.The impact of each input feature of the prediction model is consistent with the experimental results and previous studies. 展开更多
关键词 Clays Soil improvement Laboratory tests Hydrothermal solidification Unconfined compressive strength Machine learning SHapley additive exPlanation(SHAP)
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Probability prediction of true-triaxial compressive strength of intact rocks based on the improved PSO-RVM model 认领 引用
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作者 Qi Zhang Maohui Wang +2 位作者 Ning Wang Yixin Shen Xuzhen He 《Deep Underground Science and Engineering》 EI CAS CSCD 2026年第2期396-409,共14页
In deep underground engineering design,the true-triaxial compressive strength of intact rocks is a critical evaluation index.Traditional methods for acquiring true-triaxial strength data are hampered by labor-intensiv... In deep underground engineering design,the true-triaxial compressive strength of intact rocks is a critical evaluation index.Traditional methods for acquiring true-triaxial strength data are hampered by labor-intensive manual operations.To mitigate the time-consuming nature of true-triaxial experiments,this study leverages the unique capabilities of the relevance vector machine(RVM)to develop machine learning prediction models.These models aim to streamline the process and enhance predictive accuracy,thereby offering a more efficient alternative to conventional experimental approaches.The proposed models establish a correlation between the major principal stress(σ1)and the material constants,alongside other Hoek–Brown(H–B)strength parameters.A comprehensive data set,encompassing 408 sets of true-triaxial experimental data from 12 different rock types,was collated from previous studies.This true-triaxial strength data set was systematically divided into three groups based on the intact rock material content(mi),facilitating subsequent validation efforts.To enhance prediction accuracy and generalization capability,particle swarm optimization(PSO)is employed to optimize the hybrid kernel function parameters of the RVM.This study introduces a dynamic inertia weight decreasing method,demonstrating superior prediction accuracy compared to conventional PSO improvement techniques.In comparison with five three-dimensional H–B type criteria and two other machine learning models,the improved PSO-RVM model demonstrated superior performance across three distinct mi groups.Additionally,the proposed model is capable of generating probabilistic predictions,thereby effectively capturing the inherent uncertainty associated with rock strength.The probability distribution of model prediction errors closely aligns with that indicated by the generalized Zhang–Zhu criterion,underscoring the improved PSO-RVM model's ability to capture the uncertainty in true-triaxial compressive strength.Furthermore,this study explores sample selection for combined tests integrating true-triaxial experiments and the proposed improved PSO-RVM model,providing a tentative optimal ratio for predicting the true-triaxial compressive strength of intact rocks. 展开更多
关键词 intact rock particle swarm optimization probabilistic prediction relevance vector machine true-triaxial compressive strength
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Predicting the Compressive Strength of Sustainable Concrete Containing Recycled Aluminum Beverage Cans Crumb Using Machine Learning Techniques 认领 引用
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作者 Manish Kewalramani Refka Ghodhbani +6 位作者 Arsalan Mahmoodzadeh Abdulaziz Alghamdi Faten Khalid Karim Abed Alanazi Abdullah Alqahtani Shtwai Alsubai Mounir Ltifi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期181-223,共43页
Concrete manufacturing consumes vast quantities of natural resources and contributes significantly to environmental degradation and carbon emissions.Therefore,integrating recycled waste substances into concrete has be... Concrete manufacturing consumes vast quantities of natural resources and contributes significantly to environmental degradation and carbon emissions.Therefore,integrating recycled waste substances into concrete has become a crucial approach to fostering eco-friendly building practices and supporting circular economy concepts.This study investigates the potential of incorporating recycled aluminum beverage can crumbs(RABCC)as a partial replacement for natural coarse aggregates(NCA)in concrete mixtures,focusing on its impact on compressive strength(CS)and the feasibility of its application in structural concrete.A comprehensive experimental program was conducted to assess the mechanical properties of concrete with varying levels of RABCC(up to 30%)and its interaction with other mix parameters,including water-to-cement ratio(w/c),superplasticizer content(SPC),silica fume content(SFC),and curing time(CT),fine aggregate content(FAC),coarse aggregate content(CAC),fly-ash content(FA-C).The experimental findings revealed that low levels of RABCC incorporation(≤5%)led to minimal reductions in CS,with strengths comparable to those of the reference mix.However,at higher replacement levels,significant reductions in CS were observed,with the CS decreasing by up to 29.8%at 20%RABCC.To predict CS across different RABCC contents,a series of machine learning models,including kernel-based methods,ensemble tree models,gradient boosting techniques,and neural networks,were developed and validated using both hold-out and 5-fold cross-validation.The Gaussian process regression(GPR)model demonstrated the best performance,achieving an R2of 0.88 and an root mean squared error(RMSE)of 3.33 MPa in hold-out testing,and an R2of 0.89-0.94 and an RMSE of 2.373.10 MPa in 5-fold cross-validation,confirming its robustness in predicting CS.Additionally,SHapley Additive exPlanations(SHAP)analysis identified the w/c ratio and RABCC content(RABCC-C)as the most influential factors on CS,with RABCC-C exhibiting a moderately negative correlation.This study demonstrated the potential of RABCC as an ecofriendly,sustainable alternative to conventional aggregates in concrete,offering a viable pathway to reduce aluminum waste without significantly compromising the material's mechanical performance.The results also underscored the importance of optimizing RABCC content to balance sustainability goals with structural performance. 展开更多
关键词 Recycled aluminum beverage cans crumb concrete compressive strength sustainability machine learning
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Assessment of Compressive Strength of Concrete with Glass Powder and Recycled Aggregates Using Machine Learning Approaches 认领 引用
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作者 Ehsan Momeni Mohammad Dehghannezhad +1 位作者 Fereydoon Omidinasab Danial Jahed Armaghani 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第3期559-592,共34页
In the last decade,the importance of sustainable construction and artificial intelligence(AI)in civil engineering has been underlined in many studies.Numerous studies highlighted the superiority of AI techniques over ... In the last decade,the importance of sustainable construction and artificial intelligence(AI)in civil engineering has been underlined in many studies.Numerous studies highlighted the superiority of AI techniques over simple and mathematical regression analyses,which suffer from relatively poor generalization and an inability to capture highly non-linear relationships among inputs and output(s)parameters.In this study,to evaluate the compressive strength of concrete with glass powder(GP)and recycled aggregates,600 concrete samples were tested in the laboratory,and their results were evaluated.For intelligent assessment of concrete compressive strength(CCS),the study utilized an improved artificial neural network(ANN)with particle swarm optimization(PSO)algorithm and imperialist competitive algorithm(ICA).For training the models,the experimentally obtained data were used.The concrete ingredients formed the inputs of the AI-based predictive models of CCS.The experimental findings reveal that the implementation of recycled coarse aggregates in concrete from a sustainable construction point of view is advantageous and can enhance the CCS by 11.43%.Apart from that,findings indicate that utilization of 10%GP can lead to a nearly 20%increase in CCS(from 44.6 to 54.1 MPa).Additionally,the experimental observations show almost 40%improvement of CCS when 5%micro silica was used in the concrete mixture.Based on the findings,the study suggests the utilization of waste glass powder to partially replace cement in concrete,which can reduce the amount of cement production.This reduction from economic,energy-saving,and environmental(reduction in greenhouse gas emissions)points of view is of interest.On the other hand,the AI results show that the PSO-based ANN model outperforms the ICA-based ANN for the utilized dataset.According to the findings,the PSO-based ANN predictive model(with a coefficient of determination value of 0.939 and root mean square value of 0.113 for testing data)is a capable tool in predicting the CCS.Hence,this study recommends the implementation of AI-based models in CCS assessment. 展开更多
关键词 Artificial intelligence ANN ICA PSO concrete glass powder recycled aggregate compressive strength
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Automated nondestructive evaluation of compressive strength of underground lining structure using hyperspectral imaging and deep neural networks 认领 引用
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作者 Changsong Wang Mingliang Zhou +1 位作者 Le Zhang Hongwei Huang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第5期3623-3639,共17页
Evaluation of compressive strength in underground lining structures is critical for ensuring structural integrity and safety.Traditional assessment methods are often destructive,time-consuming,and impractical in confi... Evaluation of compressive strength in underground lining structures is critical for ensuring structural integrity and safety.Traditional assessment methods are often destructive,time-consuming,and impractical in confined environments such as tunnels and utility corridors.This study introduces an automated,nondestructive approach to visualize and estimate the compressive strength of underground concrete lining using hyperspectral imaging(HSI)combined with deep neural network(DNN)models.High-dimensional spectral data of concrete lining are assembled and trained to develop two DNN-based regression models,namely the Mono-Spectrum Deep Neural Regressor(MS-DNR)and the Segmented-Spectrum Deep Neural Regressor(SegS_DNR).Utilizing the SegS_DNR model,two-dimensional(2D)compressive strength distribution heatmaps were generated for visualization and assessment of strength variations.The SegS_DNR model demonstrated excellent predictive performance,achieving a coefficient of determination(Rp²)of 0.925 and a Residual Prediction Deviation(RPD)of 5.28 on the testing set for compressive strength estimation.The idea is further validated in site by investigating the capability of identifying the defect regions of the tunnel concrete lining,namely the cracked,spalling,and leaking areas,and demonstrated promising performance in comparison with experienced inspectors on site.This approach offers a contact-free technique for automated structural health monitoring,contributing to safer and more sustainable underground maintenance practices. 展开更多
关键词 Hyperspectral imaging Underground concrete structure Compressive strength Nondestructive detection Deep neural networks(DNN)
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Unconfined compressive strength and small-strain shear modulus of soft marine clay improved by ordinary Portland cement and polyester fibers with modeling by Michaelis-Menten kinetics 认领 引用
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作者 Keeratikan Piriyakul Aruz Petcherdchoo +1 位作者 Koonnamas Punthutaecha Tanakorn Phoo-ngernkham 《Journal of Road Engineering》 EI CAS 2026年第1期134-147,共14页
This study proposes to use the unconfined compressive strength(UCS)and the bender element(BE)tests for determining the strength and the initial small-strain shear modulus of Bangkok soft marine clay improved by cement... This study proposes to use the unconfined compressive strength(UCS)and the bender element(BE)tests for determining the strength and the initial small-strain shear modulus of Bangkok soft marine clay improved by cement and polyester fibers.This study varies the content of admixed cement(1%–20%)and polyester fibers(0–20%),including the curing time(3–28 d)for preparing 360 samples.Moreover,this study uses the Michaelis-Menten kinetics concept to model cement hydration saturation.From the study,it is concluded as follows.The modelled results reveals that at least 10%cement and 1%polyester fiber are recommended to attain the 28-d UCS standards(294 kPa)for highway subgrade materials in Thailand.This also fulfils sustainable construction due to reducing normal-use cement from 20%to 10%.Unfortunately,the addition of polyester fibers into the Bangkok clay with at least 5%cement reduces shear modulus by 1.12–1.32 times.The Abram's relationship between shear modulus and the mixing-water-to-cement ratio is found time-dependent.From the composite theory,the BE detects the polyester fiber zone as a defect in the Bangkok clay(matrix)with 5%–20%cement.So,the 28-d shear modulus in the polyester fiber zone is negative(up to0.034 MPa for 20%fiber),similar to softening phenomenon in concrete cracking(negative stiffness).For the 28-d shear modulus of fiber zone,the optimum cement content is around 2%for the positive influences of polyester fibers.Experimentally,the timedependent normalized UCS for 10%and 20%cement is compatible with other studies,and its development rate increases with the cement content as 0.3017,0.3172 and 0.3204 for 5%,10%and 20%cement,respectively.The 28-d relationship between shear modulus and UCS shows that low-cement soft clay requires high polyester fiber content(5%–20%)to activate UCS improvement.However,the soft clay with enough cement(20%)causes the uniformly distributed UCS improvement. 展开更多
关键词 Bangkok soft marine clay Unconfined compressive strength Small-strain shear modulus Bender elements Polyester fibers Michaelis-Menten kinetics
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Accurate Compressive Strength Prediction of Fly Ash Geopolymers Using Advanced Ensemble Models and Morris Analysis 认领 引用
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作者 Arslan Qayyum Khan Muhammad Dawood Rasheed +1 位作者 Muhammad Huzaifa Naveed Amorn Pimanmas 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期666-693,共28页
The construction industry's substantial carbon footprint,primarily attributed to the production of Ordinary Portland Cement,necessitates a transition toward more sustainable alternatives.Geopolymer concrete(GPC),a... The construction industry's substantial carbon footprint,primarily attributed to the production of Ordinary Portland Cement,necessitates a transition toward more sustainable alternatives.Geopolymer concrete(GPC),an innovative binder synthesized from industrial by-products like fly ash(FA),offers a promising low-carbon solution but is hindered by performance variability and a lack of standardized design protocols.This research addresses this critical barrier by developing robust predictive models for the compressive strength of FA-based GPC.Six machine learning algorithms,including Bagging,Categorical Boosting(CatBoost),K-Nearest Neighbors(KNN),LightGBM,Random Forest Regressor(RFR),and eXtreme Gradient Boosting(XGBoost),were developed and evaluated.The results demonstrate that the XGBoost model achieved superior predictive accuracy,with the lowest average errors(Mean squared error of 4.66,Mean absolute error of 1.42)and the highest average coefficient of determination(0.962).To enhance model interpretability,a Morris sensitivity analysis was conducted.The analysis quantitatively identified the coarse aggregate as the most influential parameter governing compressive strength,followed by key chemical precursors such as silicon dioxide(SiO2)and aluminum oxide(Al2O3).These findings not only align with established material science principles but also validate the physical realism of the machine learning model.This study provides a reliable computational framework for predicting the performance of FA-based GPC,facilitating mix design optimization and accelerating the adoption of this sustainable material in modern construction. 展开更多
关键词 Geopolymer concrete compressive strength ensemble machine learning XGBoost Morris analysis
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A pseudo-elastic damage-based model for compressive stress in expanded polystyrene foams 认领 引用
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作者 Alejandro E.Rodriguez-Sanchez 《Theoretical & Applied Mechanics Letters》 EI CAS CSCD 2026年第2期175-184,共10页
This work presents the application of a pseudo-elastic model to address the uniaxial compressive stress-strain behavior of expanded polystyrene foams.The model combines an Ogden-based hyperfoam formulation for the loa... This work presents the application of a pseudo-elastic model to address the uniaxial compressive stress-strain behavior of expanded polystyrene foams.The model combines an Ogden-based hyperfoam formulation for the loading path of experimental testing with a damage-dependent approach for the unloading.The loading during compression is described using a simplified hyperfoam strain-energy function that effectively captures the nonlinear response of compressible polymer foams.For the unloading path,the model incorporates a scalar damage parameter controlled by three key variables:maximum damage,a damage evolution rate,and a transition parameter.Experimental validation confirms the model's accuracy in predicting the mechanical response of polystyrene foams,including inelastic phenomena.This precision is supported by coefficient of determination R2 values close to unity when comparing the model's predictions with experimental data.Thus,the proposed model provides a practical tool for analyzing the compressive stress response in polystyrene foams. 展开更多
关键词 Expanded polystyrene Compressive stress Pseudo-elastic model Stress-strain curves
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Predicting the compressive strength of clay solid masonry using optimized machine learning algorithms and partial dependence plot analysis 认领 引用
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作者 Amar MESSAS Karim BENYAHI +2 位作者 Arezki ADJRAD Youcef BOUAFIA Malika BELHOCINE 《ENGINEERING Structure and Civil Engineering》 SCIE EI CAS CSCD 2026年第2期297-320,共24页
Due to its low cost and its thermal and acoustic insulation properties,clay masonry is widely used in construction.Its compressive strength is the main mechanical property and is critical for structural design.This st... Due to its low cost and its thermal and acoustic insulation properties,clay masonry is widely used in construction.Its compressive strength is the main mechanical property and is critical for structural design.This study aims to predict it using machine learning(ML)techniques.An experimental database was compiled from uniaxial compression tests on solid clay masonry specimens.First,the performance of 18 empirical models from the literature was evaluated.Then,several ML algorithms were developed,including least absolute shrinkage and selection operator regression,decision tree regression,support vector regression,bagging tree,gradient boosting,random forest regression,artificial neural networks,and Gaussian process regression(GPR).The models were trained on 80%of the data and tested on the remaining 20%,with hyperparameter optimisation and 10-fold cross-validation.The findings highlight the lower performance of traditional empirical models compared to ML methods.They also show the superior predictive ability of GPR over other ML algorithms for estimating the compressive strength of clay solid masonry.Sensitivity analysis confirms that masonry unit strength is the most influential factor,with notable nonlinear interactions involving mortar strength and geometric ratios,while joint thickness primarily acts as a regime modulator. 展开更多
关键词 ML clay solid masonry compressive strength partial dependence plot analysis bayesian optimisation
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Explainable MRF-BBAPM with self-learning for predicting compressive strength of oxidized pellet 认领 引用
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作者 Zezheng Li Jue Tang +1 位作者 Mansheng Chu Quan Shi 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2026年第6期1912-1924,共13页
The compressive strength of oxidized pellets is a key indicator for evaluating pellet quality and stability.Accurate prediction of its variation trend is essential for improving production efficiency and optimizing pr... The compressive strength of oxidized pellets is a key indicator for evaluating pellet quality and stability.Accurate prediction of its variation trend is essential for improving production efficiency and optimizing process parameters.However,due to the high dimensionality and strong nonlinearity of compressive strength prediction,existing models still face limitations in terms of reliability,applicability,and generalization.This study proposes the metallurgical-random forest-based Bayesian optimized bidirectional gated recurrent unit(BiGRU)attention prediction model(MRF-BBAPM)model,which employs feature selection guided by metallurgical mechanisms and random forest to enhance model efficiency and relevance.The BiGRU network parameters are optimized using Bayesian optimization,and an attention mechanism is incorporated to focus on critical features,further improving model performance.The SHapley Additive ex-Planations(SHAP)method is introduced to quantify the contribution of each feature to the prediction results,revealing the model’s decision-making process and enhancing its interpretability and reliability.The model also incorporates a self-learning mechanism that automatically updates and optimizes itself based on weekly prediction errors.Experimental results show that the proposed model achieves a mean absolute error of 80.58 N(2.77%of the mean)and a root mean square error of 95.75 N(3.29%of the mean)in predicting pellet compressive strength,demonstrating strong stability and reliability in real-world applications.This method provides effective data support for accurate prediction of pellet compressive strength and informed decision-making in production. 展开更多
关键词 compressive strength of oxidized pellet metallurgical mechanisms random forests Bayesian optimization bidirectional gated recurrent unit SHapley Additive exPlanations
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Active learning for global optimization of highly non-stationary geotechnical engineering systems using Bayesian compressive sensing 认领 引用
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作者 Peiping Li Yin-Fu Jin +1 位作者 Lei Wang Xiangsheng Chen 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第7期5324-5341,共18页
Global optimization constitutes a crucial challenge in design optimization in geotechnical engineering,which aims to maximize the performance objective function of a geotechnical engineering system,thereby achieving t... Global optimization constitutes a crucial challenge in design optimization in geotechnical engineering,which aims to maximize the performance objective function of a geotechnical engineering system,thereby achieving the optimal output.For complex geotechnical engineering systems with computationally time-consuming models and highly non-stationary responses,the direct application of stochastic optimization algorithms usually requires numerous evaluations of the original model,resulting in significantcomputational expense.To tackle this challenge,this study develops an innovative and efficientglobal optimization(EGO)method using Bayesian compressive sensing(BCS)and active learning for highly non-stationary geotechnical engineering problems,referred to as BCS-based EGO.In BCS-based EGO,BCS is utilized to train a response surface from a training sample set,enabling the efficientexecution of the stochastic optimization algorithm and providing response predictions along with the associated uncertainty at each search point.The response surface results are combined with an active learning sampling criterion to adaptively identify additional optimal sampling points,updating the response surface and training sample set to enhance the accuracy of response prediction and global optimization,until the stopping criterion of active learning is satisfied.The proposed method is capable of handling highly non-stationary data because BCS is data-driven and non-parametric.Moreover,it efficientlyaddresses the challenge of underestimating the factor of safety and failure probability in limit equilibrium method-based slope stability and reliability analysis using the potential slip surface method.Investigations utilizing three highly non-stationary benchmark examples and two highly nonstationary engineering examples indicate that BCS-based EGO performs well with sparse sampling points. 展开更多
关键词 Efficientglobal optimization(EGO) Highly non-stationary data Bayesian compressive sensing(BCS) Active learning Slope reliability analysis
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Constitutive damage model for polypropylene fiber-reinforced recycled fine aggregate concrete under compressive loading 认领 引用
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作者 LUO Bin CAO Guang +2 位作者 SONG Yechao ZHAO Fengnian TANG Zhonghua 《Journal of Southeast University(English Edition)》 EI CAS 2026年第1期92-99,共8页
To promote the application of green recycled construction materials in civil engineering,this study presents a statistical damage constitutive model for polypropylene fiber recycled fine aggregate concrete(PRFAC),base... To promote the application of green recycled construction materials in civil engineering,this study presents a statistical damage constitutive model for polypropylene fiber recycled fine aggregate concrete(PRFAC),based on the strain equivalence principle and the assumption that microelement strength follows a Weibull statistical distribution.The proposed model incorporates the Drucker-Prager failure criterion.By examining the influence of Weibull distribution parameters m and S0on the stress-strain response,empirical relationships were established between the fine aggregate replacement ratio and the distribution parameters.This enabled the derivation of a theoretical stress-strain curve accounting for variable recycled fine aggragate(RFA)replacement ratios.The experimental results show that the proposed model exhibits high agreement with measured data and effectively captures the increased brittleness of PRFAC with higher RFA replacement ratios.Moreover,increasing the replacement rate accelerates internal crack propagation,reduces deformability and toughness,and significantly hastens the accumulation of internal damage in PRFAC. 展开更多
关键词 green concrete polypropylene fibers recycled fine aggregate damage constitutive model compressive behavior aggregate replacement ratio
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Reverse size effect on unconfined compressive strength of red-bed rocks using micro-CT technique 认领 引用 被引量：1
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作者 Qinyuan Liang Hengxing Lan +3 位作者 Yu Zhou Bo Li Shijie Liu Han Bao 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第5期3425-3446,共22页
This study integrates unconfined compression tests with high-resolution computed tomography(CT)to analyze the pore heterogeneity,crack propagation,and failure modes of red sandstone specimens with diameters ranging fr... This study integrates unconfined compression tests with high-resolution computed tomography(CT)to analyze the pore heterogeneity,crack propagation,and failure modes of red sandstone specimens with diameters ranging from 10 mm to 100 mm.Key findings include:(1)With increasing specimen size,crack initiation stress(CI),damage stress(CD),and unconfined compressive strength(UCS)initially increase and then decrease;(2)In smaller specimens,stress concentration due to pore heterogeneity leads to splitting failure and lower strength;(3)In medium-sized specimens,friction dominates crack propagation,causing shear failure,while increased fragment rotation enhances energy dissipation,yielding highest strength;(4)In larger specimens,cracks tend to propagate along bedding planes,reducing energy dissipation and then weakening strength.These results provide insights into the reverse size effect on sandstone strength and have implications for engineering applications. 展开更多
关键词 Red sandstone Reverse size effect Unconfined compression test Pore heterogeneity Crack propagation Computed tomography(CT)
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Epitaxial compressive strain effect in La3Ni2O7-δthin films 认领 引用
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作者 Yu Chen Wenbo Yang +5 位作者 Jie Gong Ke Zhang Huaqian Leng Nian Li Haiyan Xiao Liang Qiao 《Superconductivity》 EI CSCD 2026年第2期6-13,共8页
Epitaxial strain provides an effective route to tune the structural and electronic properties of correlated oxide thin films.Here,to investigate the influence of in-plane compressive strain effect on La3Ni2O_(7-... Epitaxial strain provides an effective route to tune the structural and electronic properties of correlated oxide thin films.Here,to investigate the influence of in-plane compressive strain effect on La3Ni2O7-δthin films,we synthesize a series of samples on three compressively strained substrates.We observe that the resistance exhibits a metal-to-insulator transition behavior as the strain level increases.This is attributed to the aggravated oxygen deficiency in large compressive strain cases.Our theoretical calculations confirm that the formation energy of oxygen vacancies gradually decreases with the increase of in-plane compressive strain strengths,suggesting more serious oxygen content deviation.Furthermore,we also reveal that theγband,dominated by Ni 3dz2orbitals,exhibits a dependent relationship with the compressive strain,which is gradually moving away from the Fermi energy as the strain increases.These results establish a correlation between epitaxial strain,oxygen vacancy formation,and electronic transport in La3Ni2O7-δthin films.Our work provides important insights into the compressive strain,oxygen defect,and electronic structure interplay in bilayer nickelates,which is essential for understanding and tuning their emergent physical properties. 展开更多
关键词 La3Ni2O7-δthin films Compressive strain effect Theoretical calculations
Dynamic compressive mechanical properties and fracture mechanism of conglomerate 认领 引用
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作者 WANG Mi CHEN Xiao-bin +2 位作者 TANG Lu-bo WANG Ye-shun LIANG Wei-qun 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第1期299-316,共18页
Conglomerate rock's complex and heterogeneous microstructure significantly affects its mechanical properties,especially under dynamic loading.However,research on their dynamic behavior and fracture mechanisms is l... Conglomerate rock's complex and heterogeneous microstructure significantly affects its mechanical properties,especially under dynamic loading.However,research on their dynamic behavior and fracture mechanisms is limited.Through uniaxial compression tests and split Hopkinson pressure bar(SHPB)impact tests,the dynamic compressive mechanical properties and fracture mechanisms of conglomerate rock were studied.Nanoindentation and high-resolution X-ray computed tomography were employed to analyze the micro-mechanical behavior and internal structure of the conglomerate rock.Results indicate significant differences in mechanical properties between different gravel particles and cementing materials,with initial fractures primarily distributed at the gravel-cement interfaces.The dynamic mechanical properties of conglomerate rocks exhibit a clear strain rate dependency.Based on the stress−strain curves and failure characteristics,the dynamic compressive mechanical behavior can be categorized into two types using a critical strain rate.The dynamic compressive strength,peak strain,and toughness of conglomerate rock increased with the strain rate,with the strength at 54 s−1 being 2.6 times that at 6 s−1.The dynamic compressive fracture mechanism of conglomerate rock is related to the strain rate and microstructure;at low strain rates,gravel distribution is the key factor,whereas at high strain rates,gravel content becomes critical. 展开更多
关键词 conglomerate compressive response realistic micro-heterogeneity fracture mechanism strain rate dependency
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Sparsity-precise iterative hard thresholding for medical image compressive sensing 认领 引用
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作者 Yongxian Zheng Hao Liu 《Optoelectronics Letters》 EI 2026年第1期46-52,共7页
This paper firstly analyzes the characteristics of medical images,and then proposes a specialized compressive sensing algorithm called sparsity-precise iterative hard thresholding(SIHT),which is specifically designed ... This paper firstly analyzes the characteristics of medical images,and then proposes a specialized compressive sensing algorithm called sparsity-precise iterative hard thresholding(SIHT),which is specifically designed to address their specific features such as low sparsity and low frequency.SIHT adaptively measures sparsity and step length which becomes more precise during the iteration process to achieve a certain quality improvement in medical image reconstruction.Experimental results demonstrate that as compared to other image compressive sensing(ICS)reconstruction algorithms across three different types of medical image datasets,SIHT can achieve the best subjective recovery quality particularly in terms of mitigating blocky artifacts and noise,where a notable improvement is obtained in terms of peak signal-to-noise ratio(PSNR)and structural similarity index measurement(SSIM)of medical ICS reconstruction. 展开更多
关键词 blocky artifacts noise medical imagesand medical image measures sparsity step length peak signal noise ratio medical image compressive sensing sparsity precise iterative hard thresholding
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Study on Machine Learning-based Prediction of Compressive Strength of Concrete with Different Waste Glass Powder Contents 认领 引用 被引量：1
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作者 YU Daidong MA Yuwei +3 位作者 LI Gang WANG Aiqin HUANG Wei WANG Jingchao 《材料导报》 EI CAS CSCD 北大核心 2026年第6期111-125,共15页
The application and promotion of waste glass powder concrete(WGPC)cansignificantly alleviate the pressure of concrete material scarcity and environmental pollution.Compressive strength(CS)is a critical parameter for e... The application and promotion of waste glass powder concrete(WGPC)cansignificantly alleviate the pressure of concrete material scarcity and environmental pollution.Compressive strength(CS)is a critical parameter for evaluating the efficacy of WGPC.Unlike conventional testing methods,machine learning techniques offer precise and reliable predictions of concrete’s compressive strength,especially in its long-term mechanical properties.In this work,four models,namely Multiple Linear Regression(MLR),Back Propagation Neural Network(BPNN),Support Vector Regression(SVR),and Random Forest Regression(RFR)were employed.Furthermore,particle swarm optimization(PSO)algorithm and cross-validation techniques were applied to fine-tune the model parameters,striving for peak prediction performance.The results indicated that optimized models generally exhibit enhanced predictive accuracy compared to their basic counterparts.Notably,the PSO-RFR model excels among all evaluated models,showcasing superior performance on the testing dataset.It achieves a coefficient of determination(R2)of 0.9231,a mean absolute error(MAE)of 2.1073,and a root mean square error(RMSE)of 3.6903.When compared to experimental results,the PSO-RFR and PSO-BPNN models demonstrate exceptional predictive accuracy.Notably,the PSO-BPNN model exhibits the closest R2values between its training and test sets.This close alignment of R2values between the training and testing sets reflects the PSO-BPNN model’s superior generalization ability for unseen data.The findings present an efficient method for predicting concrete’s compressive strength,contributing to the sustainable development of concrete materials,and providing theoretical support for their research and application. 展开更多
关键词 waste glass powder concrete compressive strength machine learning particle swarm optimization algorithm visualization
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Compressive strength of biopolymer-stabilized residual granitic soil using polybutylene succinate and xanthan gum:A mechanical-microstructural study 认领 引用
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作者 Fazal E.Jalal Mudassir Iqbal +2 位作者 Xiaohua Bao Syed Taseer Abbas Jaffar Xiangsheng Chen 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第1期673-701,共29页
The biodegradable polybutylene succinate(PBS)material offers a sustainable solution for a circular economy to address the global issue of marine plastic waste.Its cross-linkage with non-biodegradable xanthan gum(XG)bi... The biodegradable polybutylene succinate(PBS)material offers a sustainable solution for a circular economy to address the global issue of marine plastic waste.Its cross-linkage with non-biodegradable xanthan gum(XG)biopolymer to ameliorate residual granitic soil(RGS)in arid and semiarid regions can significantly mitigate soil erosion.This study investigates the enhancement of RGS by cross-linking the PBS and XG biopolymers.Employing a multitude of geotechnical tests(liquid limit,linear shrinkage,specific gravity,compaction,and UCS tests)at 3 d,28 d,and 90 d of steam-curing at a controlled temperature of 16℃,the outcomes were validated through scanning electron microscopy(SEM),thermogravimetric analysis(TGA),Fourier transform infrared spectroscopy(FTIR),and Brunauer-Emmett-Teller(BET)analyses.In addition,a comprehensive experimental database of 150 tests and nine parameters from the current study was utilized to model the UCS90-d(i.e.unconfined compressive strength after 90 d of curing)of the PBS-XG-treated RGS mixtures by deploying the random forest(RF)and eXtreme Gradient Boost(XGBoost)methods.The results found that the two biopolymers significantly improve the mechanical properties of RGS,with optimal UCS achieved at specific dosages(0.4PBS,1.5XG,and 0.2PBS+1.5XG dosage levels)and curing times.The UCS of PBS-XG-treated RGS showed up to a 57%increase after 90 d of curing.Furthermore,SEM and FTIR analyses revealed the formation of stronger microstructures and chemical bonds,respectively,whereas BET analysis indicated that pore volume and diameter are critical in affecting UCS.The proposed RF model outperformed XGBoost in predictive accuracy and generalization,demonstrating robustness and versatility.Moreover,SHAP values highlighted the significant impact of input parameters on UCS90-d,with curing time and specific material properties being key determinants.The study concludes with the proposal of a novel PyCharm intuitive graphical user interface as a"UCS Prediction App"for engineers and practitioners to forecast the UCS90-d of granitic residual soil. 展开更多
关键词 Residual granitic soil Polybutylene succinate Xanthan gum(XG)Unconfined compression strength(UCS) Random forest(RF) eXtreme gradient boost(XGBoost)method
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Compressive Sensing Aided Deep Learning Based Congested Link Detection in Network Tomography 认领 引用
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作者 Wu Xiaoge 《China Communications》 SCIE EI CSCD 2026年第5期256-270,共15页
Congested link detection(CLD)has attracted more and more attention due to the rapid development of data traffic and services.In this work,we proposed a novel compressive sensing(CS)aided deep learning CLD scheme.First... Congested link detection(CLD)has attracted more and more attention due to the rapid development of data traffic and services.In this work,we proposed a novel compressive sensing(CS)aided deep learning CLD scheme.Firstly,considering the network tomography structure,we proposed a CS-based preliminary CLD process to estimate the congestion probabilities for each link by utilizing the sparsity of congestions,it enables a decrease in the number of monitors needed,which in turn,enhances the flexibility and practicality of our scheme in various real-world scenarios.Then,based on the CS aided preliminary estimation,a long short-term memory network(LSTMN)is exploited to extract the time relationship of the congested states for each link,which can improve the accuracy of CLD.Moreover,since LSTMN utilizes the CS-aided preliminary estimation results to extract time relationships,our proposed scheme can reduce the monitoring cost and improve CLD accuracy.Ultimately,the simulation results substantiate the efficacy of our proposed scheme. 展开更多
关键词 compressive sensing(CS) congested link detection(CLD) deep learning long short-term memory network(LSTMN)
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