Engineering Fracture Mechanics最新文献

{"title":"Prediction of mode I crack propagation in rock based on fracture parameters from the boundary effect Model: Numerical study and experimental Validation","authors":"Zhe Li , Zhi Suo , Yu Liang , Xizhou Ding , Zhitao Lin , Hongbo Liu , Yanjie Wang , Xinru Li , Xinxin Li , Xiaozhi Hu , Zhimin Wu","doi":"10.1016/j.engfracmech.2026.111983","DOIUrl":"10.1016/j.engfracmech.2026.111983","url":null,"abstract":"<div><div>Knowledge and accurate prediction of crack extension in rocks that incorporates accurate fracture criterion and fracture parameters are crucial for the stability of rock masses and for safe design of rock structures. While the stress intensity factor (SIF)-based crack propagation criteria have been employed to evaluate the fracture process of concrete, limited research is conducted on the applicability of SIF-based criteria in modelling the crack extension in rocks. In this paper, a numerical model was proposed to evaluate the mode-I crack propagation process in rocks with the consideration of the nil-SIF-based (<span><math><mrow><msubsup><mi>K</mi><mrow><mtext>IC</mtext></mrow><mi>P</mi></msubsup><mo>-</mo><msubsup><mi>K</mi><mrow><mtext>IC</mtext></mrow><mi>σ</mi></msubsup><mo>=</mo><mn>0</mn></mrow></math></span>) and initial fracture toughness (<span><math><mrow><msubsup><mi>K</mi><mrow><mtext>IC</mtext></mrow><mrow><mi>ini</mi></mrow></msubsup></mrow></math></span>)-based criteria (<span><math><mrow><msubsup><mi>K</mi><mrow><mtext>IC</mtext></mrow><mi>P</mi></msubsup><mo>-</mo><msubsup><mi>K</mi><mrow><mtext>IC</mtext></mrow><mi>σ</mi></msubsup><mo>=</mo><msubsup><mi>K</mi><mrow><mtext>IC</mtext></mrow><mrow><mi>ini</mi></mrow></msubsup></mrow></math></span>), where <span><math><mrow><msubsup><mi>K</mi><mrow><mtext>IC</mtext></mrow><mi>P</mi></msubsup></mrow></math></span> and <span><math><mrow><msubsup><mi>K</mi><mrow><mtext>IC</mtext></mrow><mi>σ</mi></msubsup></mrow></math></span> represent the SIF induced by external loads and cohesive forces, respectively. To facilitate these simulations, the fracture parameters were analytically calculated using the boundary effect model (BEM) and validated with those measured from experimental tests. Laboratory experiments were carried out on three-point bending (3-p-b) sandstone beam specimens with different initial crack length. After the validity of the proposed model is verified with both the self-conducted experimental results and the test data available in the literature, a comparative analysis was conducted to assess the effectiveness of the two SIF-based criteria by comparing with experimental database given in the literature. The results indicated that the predicted load-crack mouth opening displacement (<em>P</em>-CMOD) response, maximum load, critical CMOD (CMOD<sub>c</sub>) at the peak load using the <span><math><mrow><msubsup><mi>K</mi><mrow><mtext>IC</mtext></mrow><mrow><mi>ini</mi></mrow></msubsup></mrow></math></span>−based criteria showed better agreement with the experimental data, regardless of rock type, specimen configurations and dimensions, while the predictions derived from the nil-SIF-based criterion were underestimated. The crack resistance (<em>K<sub>R</sub></em>) values calculated using the nil-SIF-based criterion were observed to be lower than those determined via the <span><math><mrow><msubsup><mi>K</mi><mrow><mtext>IC</mtext></mrow><mrow><mi>ini</mi></mrow></ms","PeriodicalId":11576,"journal":{"name":"Engineering Fracture Mechanics","volume":"337 ","pages":"Article 111983"},"PeriodicalIF":5.3,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable machine learning-based framework for predicting surface defect-induced fatigue behavior in additively manufactured Ti6Al4V miniature components","authors":"Yixuan Hou ,&nbsp;Tony Wauters ,&nbsp;Jianguang Liu ,&nbsp;Qiang Liu ,&nbsp;Xiaoqin Zhou ,&nbsp;Reza Talemi","doi":"10.1016/j.engfracmech.2026.111959","DOIUrl":"10.1016/j.engfracmech.2026.111959","url":null,"abstract":"<div><div>The presence of intrinsic surface defects in additively manufactured materials significantly compromises structural integrity and introduces uncertainty in fatigue design. This study proposes an interpretable machine learning-based framework to predict the fatigue lifetime of additively manufactured Ti6Al4V miniature components using geometrical features of critical defects. Surface morphology datasets, synthesized via a generative deep learning approach, were employed to construct high-fidelity finite element models for analyzing defect-induced stress concentrations. A fatigue damage model, integrating Continuum Damage Mechanics with the Theory of Critical Distances, was developed to estimate fatigue lifetime from the computed stress fields. Results reveal that variations in critical notch geometry govern the scatter in fatigue lifetime, and the coalescence band between adjacent defect stress fields is reported. The generated defect–lifetime datasets were used to train and evaluate multiple machine learning models. Hyperparameters were optimized using the Tree-structured Parzen Estimator algorithm to enhance robustness and reduce overfitting. Furthermore, Shapley Additive Explanations provided physical interpretability by quantifying feature contributions. The findings demonstrate that critical defect geometry serves as a reliable predictor of fatigue lifetime, bridging numerical simulation, machine learning prediction, and physical understanding of fatigue behavior in AM Ti6Al4V components.</div></div>","PeriodicalId":11576,"journal":{"name":"Engineering Fracture Mechanics","volume":"337 ","pages":"Article 111959"},"PeriodicalIF":5.3,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acoustic emission cascade predicting progression to failure of rock and bone","authors":"Andrew P. Bunger ,&nbsp;Yunxing Lu ,&nbsp;Ayyaz Mustafa ,&nbsp;Michael M. McDowell","doi":"10.1016/j.engfracmech.2026.111991","DOIUrl":"10.1016/j.engfracmech.2026.111991","url":null,"abstract":"<div><div>Quasi brittle materials such as rock and bone are understood to fail via microcrack coalescence. The accompanying Acoustic Emission (AE) event rate is known to increase as failure progresses. Here we examine the progression of the AE event rate for both rock and bone under conditions where failure progresses under constant loading. The experiments for rock entail subjecting granite beams to a constant loading under three point bending and monitoring the time-dependent failure. For bone, human cadaver skulls are loaded under pinning loads similar to those used for immobilization of the head for neurosurgical procedures. AE rates are shown to be consistent with a rate dependent material failure law including a quasi-linear cascade of the inverse AE energy rate in the lead up to failure that is shown here to arise because of a coupling wherein a response rate is a power law of a driver and the driver is, in turn, a power law of the accumulated response. For both materials and experimental configurations this cascade provides warning of impending failure. For granite beams there is accurate prediction of failure time over the final 30% of the specimens’ lifetimes, which ranged from 35 s to two days. For skull fracture, the commencement of the failure cascade provides 30–70 s for warning of failure, which would be sufficient for basic remedial action to avoid patient injury in a surgical application.</div></div>","PeriodicalId":11576,"journal":{"name":"Engineering Fracture Mechanics","volume":"337 ","pages":"Article 111991"},"PeriodicalIF":5.3,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fatigue crack growth characteristics of single-sided surface cracks in GLARE: Effects of loading condition and laminate configuration","authors":"Yongkun Ma ,&nbsp;Yuan Zhao ,&nbsp;Xingyu Wei ,&nbsp;Mengjia Li ,&nbsp;Pengfei Yang ,&nbsp;Jipeng Zhang ,&nbsp;Jiazhen Zhang","doi":"10.1016/j.engfracmech.2026.111996","DOIUrl":"10.1016/j.engfracmech.2026.111996","url":null,"abstract":"<div><div>GLARE have garnered extensive application and research interest across engineering domains owing to their exceptional mechanical properties. However, in service, surface and single-sided surface cracks often initiate due to scratches from sharp objects, posing notable threats to structural integrity. Digital image correlation was employed to in-situ monitor and quantify crack length evolution and laminate strain fields during fatigue loading. Systematic investigations evaluated the effect of initial crack depth on single-sided surface cracks propagation, influences of structural configurations and loading conditions on fatigue growth and associated delamination, and propagation differences between single-sided surface and through cracks. Our findings reveal four key insights: initial crack depth dictates the onset of single-sided surface cracks propagation but not subsequent growth kinetics; their fatigue growth rate trends are fundamentally distinct from through cracks; single-sided surface cracks growth rates exhibit lower sensitivity to stress ratio variations; and increasing the laminate count yields less significant improvement in fatigue resistance for single-sided surface cracks. This work elucidates the unique fatigue propagation mechanisms of single-sided surface cracks in GLARE, filling a critical knowledge gap. The findings provide foundational data and mechanistic insights to advance accurate predictive models for structural integrity assessment of GLARE components, supporting their reliable engineering application.</div></div>","PeriodicalId":11576,"journal":{"name":"Engineering Fracture Mechanics","volume":"337 ","pages":"Article 111996"},"PeriodicalIF":5.3,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-informed neural networks for inverse length scale identification and forward phase-field fracture modeling","authors":"Mengchen Yu ,&nbsp;Xiangyun Long ,&nbsp;Chao Jiang ,&nbsp;Xiaogang Wang ,&nbsp;Kai Liu","doi":"10.1016/j.engfracmech.2026.111978","DOIUrl":"10.1016/j.engfracmech.2026.111978","url":null,"abstract":"<div><div>This article presents a unified physics-informed neural networks (PINNs) framework for phase-field fracture modeling, capable of simultaneously identifying the length scale parameter and predicting crack evolution. The method embeds local observational data into a variational formulation by minimizing the total potential energy functional, enabling both inverse parameter identification and forward prediction without pre-assigned parameters. A discrete loading strategy with weight inheritance is employed to ensure the irreversibility of crack evolution. Numerical studies on single-edge cracked plates under tension and shear, together with a 3D compact-tension experiment, demonstrate substantial error reductions and superior performance compared to conventional PINNs-based phase-field model.</div></div>","PeriodicalId":11576,"journal":{"name":"Engineering Fracture Mechanics","volume":"337 ","pages":"Article 111978"},"PeriodicalIF":5.3,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanism of roadway deformation failure and support optimization in alpine-gorge mines: Insights from an integrated study of numerical-physical-experimental","authors":"Longji Guo ,&nbsp;Manchao He ,&nbsp;Zhigang Tao","doi":"10.1016/j.engfracmech.2026.111994","DOIUrl":"10.1016/j.engfracmech.2026.111994","url":null,"abstract":"<div><div>The mining of phosphate rock, as a strategically scarce mineral resource, has gradually shifted from shallow to deep mining, with depths exceeding 1,000 m. The high ground stress at these depths prevents rebar bolts from withstanding large deformations and break failures. In this study, the anchor-bolt anchoring area was located in an area with a large roof separation, which resulted in a large roof subsidence. Focusing on the geological and mining conditions of the Shanshuya mine in Yichang, various analytical methods have been used to reveal the law of roadway deformation failure. FLAC was used to optimize the support parameters of high-prestressed large-deformation negative Poisson’s ratio (NPR) bolts, and a physical-model experiment was conducted to optimize the spacing between rooms and pillars under optimal support conditions. The damage state of a roadway supported by rebar and NPR bolts was analysed, and the effect of the proposed NPR excavation compensation method was verified. At the project site, a high-strength dynamic active support technology was combined with a new type of NPR anchor bolt as the core, the mining room-pillar spacing was optimized from 8 to 16 m, and the roof deformation was decreased from 500 to 120 mm, thus preventing the emergence of mine pressure at the source and increasing the phosphate recovery rate by 8.7%. Comprehensively analysing the mechanism of roadway deformation failure in phosphate mines and devising mining-optimization measures are critical for improving the efficiency of mineral-resource mining, ensuring safety, and promoting the sustainable development of the mining industry.</div></div>","PeriodicalId":11576,"journal":{"name":"Engineering Fracture Mechanics","volume":"337 ","pages":"Article 111994"},"PeriodicalIF":5.3,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An equivalent structural stress method for variable-amplitude fatigue assessment of welded joints with load sequence effects","authors":"Qi Dong ,&nbsp;Jiaqi Zhang ,&nbsp;Yuedong Wang ,&nbsp;Bo Liu ,&nbsp;Qiyu An ,&nbsp;Tao Guo ,&nbsp;Guangwei Zhao","doi":"10.1016/j.engfracmech.2026.111982","DOIUrl":"10.1016/j.engfracmech.2026.111982","url":null,"abstract":"<div><div>This study investigates the fatigue life prediction of welded structures under variable-amplitude loading (VAL) and extends an equivalent structural stress (ESS) method that accounts for both load sequence and mean stress effects. Based on fracture mechanics, the elastic–plastic behavior at the crack tip is considered, with the influence of load sequence incorporated into the ESS framework by means of a generalized Willenborg model. Welding-induced residual stresses (WRS) and plasticity-induced residual stresses (PRS) are incorporated in the development of the VAL-ESS method. Load sequence effects on fatigue damage are quantified through a stress amplitude ratio correction factor and fitted to experimental fatigue data. The VAL-ESS method achieves a high correlation with the master S-N curve under VAL. Further validation using experimental data across varying load conditions, geometries, and surface treatments demonstrates that the VAL-ESS method substantially improves prediction reliability. It achieves higher accuracy and reduced scatter compared with the nominal stress and effective notch stress methods. Finally, the coupled effects of load amplitude sequence and defect size on fatigue behavior are mechanistically interpreted, highlighting the effectiveness of the VAL-ESS approach in capturing the critical features of welded fatigue under VAL.</div></div>","PeriodicalId":11576,"journal":{"name":"Engineering Fracture Mechanics","volume":"337 ","pages":"Article 111982"},"PeriodicalIF":5.3,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent warning method for fatigue crack propagation stages using acoustic emission and machine learning","authors":"Xiao Wang ,&nbsp;Qingrui Yue ,&nbsp;Xiaogang Liu","doi":"10.1016/j.engfracmech.2026.112021","DOIUrl":"10.1016/j.engfracmech.2026.112021","url":null,"abstract":"<div><div>Acoustic emission identification of fatigue crack propagation stages in steel components is crucial for understanding fatigue damage evolution and critical warning. However, traditional statistical indicators rely heavily on empirical design and need help accurately identifying critical transition points under complex coupled factors. We propose a fatigue damage AE warning method based on improved Mel-frequency cepstral coefficients (MFCC) and Long Short-Term Memory (LSTM) networks. The study first employs the Sentry function to label AE signals during the fatigue fracture. Subsequently, it utilizes MFCC coefficients optimized with an improved frequency warping function and an LSTM model to establish pattern associations with critical AE signals. Real-time warning of damage stages is achieved through a sliding window approach. Validation experiments conducted on an AE monitoring dataset from cast steel specimens with fatigue cracks demonstrate that the cutoff frequency significantly influences the high-frequency representation of AE waveforms in MFCC coefficients. Compared to Bi-LSTM and GRU models, the LSTM model exhibits superior warning performance. The proposed improved MFCC-LSTM method accurately predicts critical zones of fatigue damage, providing reliable early warning technology for fatigue damage monitoring in steel structures.</div></div>","PeriodicalId":11576,"journal":{"name":"Engineering Fracture Mechanics","volume":"337 ","pages":"Article 112021"},"PeriodicalIF":5.3,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A micro specimen testing method to study the anisotropy of creep-fatigue interaction for SLM GH4169 alloy","authors":"Guowen Yao ,&nbsp;Hongmin Zhang ,&nbsp;Qiao Dai ,&nbsp;Tianyu Chen ,&nbsp;Rui Zhang ,&nbsp;Hao Liu ,&nbsp;Jian Peng","doi":"10.1016/j.engfracmech.2026.111972","DOIUrl":"10.1016/j.engfracmech.2026.111972","url":null,"abstract":"<div><div>Nickel-based alloys fabricated by selective laser melting (SLM) offer significant advantages without constrained by geometric shapes in high-temperature equipment applications, but the anisotropic mechanical properties restrict its safety application. In this paper, small punch creep test (SPCT), small punch fatigue test (SPFT) and small punch creep-fatigue test (SPCFT) are applied to study the anisotropy of creep-fatigue interaction for SLM GH4169 at 650℃ using two disc specimens (i.e., X and Z) with flat surfaces parallel and perpendicular to the build direction, respectively. The failure life of X-specimen exceeds that of Z-specimen at all loading conditions, indicating homogeneity in anisotropy. However, the significance of anisotropy is inhomogeneity, and it is substantial in SPCT, weak in SPFT, and moderate in SPCFT. Moreover, the creep-fatigue interaction is negative at the short hold time as 2 s due to the relieving of fatigue damage, but it is positive at the long hold time as 22 s, since the creep and fatigue damages coexist. And the Z-specimen exhibits stronger interaction than the X-specimen. At last, the anisotropic fracture mechanism diagrams of SPFT, SPCT and SPCFT are constructed. The surface crack propagation of X-specimen is parallel to the build direction, while that of Z-specimen shows a star shape along the radial direction. The crack propagation mode is primarily transgranular fracture for SPFT, and is intergranular fracture for SPCT, while is the coexistence of transgranular fatigue crack and intergranular creep void for SPCFT. This paper innovatively develops the SPCFT to study the anisotropy of creep-fatigue interaction for SLM GH4169 alloy, which provides a reference for the structural design and life prediction of SLM fabricated nickel-based alloys under high-temperature creep-fatigue conditions.</div></div>","PeriodicalId":11576,"journal":{"name":"Engineering Fracture Mechanics","volume":"337 ","pages":"Article 111972"},"PeriodicalIF":5.3,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new artificial neural network model for prediction of fatigue strength and yield strength of various steel grades","authors":"Dj. Ivković ,&nbsp;D. Arsić ,&nbsp;D. Adamović ,&nbsp;A. Sedmak ,&nbsp;M. Delić ,&nbsp;R. Vulović","doi":"10.1016/j.engfracmech.2026.111990","DOIUrl":"10.1016/j.engfracmech.2026.111990","url":null,"abstract":"<div><div>The prediction of mechanical properties of steels traditionally relies on experimental testing and empirical modeling, which are time-consuming and resource-intensive. Recent advances in artificial intelligence (AI) and machine learning (ML) enable efficient estimation of material properties directly from chemical composition data. In this study, two Artificial Neural Network (ANN) models were developed to predict key mechanical properties of steels—fatigue strength and yield strength—using chemical composition as the sole input. Each network employed a feed-forward backpropagation architecture with Bayesian regularization and was trained on a dataset comprising 200 steel grades, including structural, high-strength, and tool steels. The models were evaluated through regression analysis and further validated using experimental data for three representative steels: S355J2 + N, S690QL, and X37CrMoV5-1. The fatigue strength model achieved correlation coefficients of R = 0.895 (training), 0.833 (validation), and 0.893 (testing), with an average prediction error of approximately 9%. The yield strength model reached R = 0.835 (training), 0.829 (validation), and 0.864 (testing), with a mean absolute percentage error of 3.97%. The obtained results demonstrate that ANN-based models can reliably capture the relationship between chemical composition and mechanical properties of steels, supporting preliminary material screening and property estimation. The proposed approach provides a basis for the development of data-driven tools in digital materials engineering.</div></div>","PeriodicalId":11576,"journal":{"name":"Engineering Fracture Mechanics","volume":"337 ","pages":"Article 111990"},"PeriodicalIF":5.3,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}