Advanced Engineering Informatics最新文献

{"title":"Multi-agent reinforcement learning method for joint optimization of block assignment and yard crane redeployment at river-sea intermodal container terminal","authors":"Huakun Liu ,&nbsp;Wenyuan Wang ,&nbsp;Yun Peng ,&nbsp;Shuzheng Yang ,&nbsp;Hongbin Tian ,&nbsp;Qiang Qi","doi":"10.1016/j.aei.2025.104271","DOIUrl":"10.1016/j.aei.2025.104271","url":null,"abstract":"<div><div>The continuous growth of global maritime trade and dynamic operational characteristics of river-sea intermodal container terminals create an urgent need to enhance transshipment efficiency. Block assignment and yard crane (YC) redeployment (BA-YCR) are two tightly coupled scheduling processes that significantly impact overall yard operational efficiency. In response to minute-level workload fluctuations, effectively optimizing the BA-YCR problem in real time remains challenging. To this end, this study proposes a multi-agent reinforcement learning (MARL)-based approach for real-time optimization of the BA-YCR problem. The BA-YCR is formulated as a Markov Decision Process model, with the objective of minimizing YC redeployments, operational delays, and transport time. A hybrid reward mechanism is designed to balance exploration and coordination between agents. A two-stage multi-agent decision framework is developed, in which the coupling scheduling policies are trained using the Proximal Policy Optimization algorithm. Numerical experiments demonstrate that, the proposed MARL-based approach consistently outperforms benchmark methods. The well-trained scheduling policies achieve improvements of 0.2% to 81.3% in solution quality, while maintaining a computation time of less than 5 seconds, even in large-scale scenarios. Furthermore, sensitivity analyses based on a real-world container terminal further validate the practical applicability and generalization of the proposed approach. The results not only support terminal operators in developing reliable real-time BA-YCR strategies, but also offer practical insights for real-time scheduling optimization using MARL-based method in broader engineering applications.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104271"},"PeriodicalIF":9.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 novel discriminative joint adversarial network for quantitatively detecting wheel polygonization of heavy-haul locomotives across variable running conditions","authors":"Maoyong Dong,&nbsp;Shiqian Chen,&nbsp;Hongbing Wang,&nbsp;Wanming Zhai","doi":"10.1016/j.aei.2026.104377","DOIUrl":"10.1016/j.aei.2026.104377","url":null,"abstract":"<div><div>Timely quantitative detection of wheel polygonal wear is of great significance for railway maintenance and improving the train running quality. However, existing deep learning-based detection methods struggle with speed variation-induced feature distribution shifts, exhibiting weak transferability and failing to achieve quantitative diagnosis of wheel defects. To address these issues, a novel discriminative joint adversarial network (NDJAN) for polygonal fault detection under varying running speeds is proposed in this paper. A multi-branch parallel ResNet is first developed to extract sensitive features from raw signals using shortcut connections, which can preserve critical wear amplitude-related information and alleviate gradient vanishing problems. Then, a two-level discriminative feature fusion (TLDFF) scheme is designed with a hybrid attention mechanism and lightweight depthwise separable convolutions. The former is employed to amplify discriminative features, while the latter achieves intelligent fusion of multi-branch features through learnable weighting coefficients, ensuring optimal integration of complementary information from different branches. Finally, an implicit-explicit joint distribution alignment (IEJDA) strategy is presented to address fundamental transfer distribution discrepancies under variable operating conditions. This module accomplishes global distribution matching and fine-grained adaptation of decision boundaries by acting on the feature layer and regression decision layer, respectively. Both dynamics simulations and field tests are carried out to demonstrate that the proposed NDJAN approach can effectively and accurately detect the polygonal wear amplitudes.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104377"},"PeriodicalIF":9.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 improved penalty kriging method for mixed qualitative and quantitative factors","authors":"Dahao Chen ,&nbsp;Zhijun Cheng","doi":"10.1016/j.aei.2026.104407","DOIUrl":"10.1016/j.aei.2026.104407","url":null,"abstract":"<div><div>In the testing problem of weapon systems, the construction of weapon system surrogate models presents the following three challenges: limited samples, mixed qualitative and quantitative factors, and high-dimensional analysis. These factors collectively hinder the accuracy and feasibility of modelling and the solution. To address these issues, this paper proposes an improved penalty Kriging method that combines qualitative and quantitative factors. The penalty terms for regression coefficients and related parameters are introduced into the trend and correlation functions, respectively, to simultaneously screen the variables of qualitative and quantitative factors. To solve the maximum likelihood estimation problem with a penalty function, the regularisation parameters of the two penalty functions are determined using a grid search with cross-validation. Subsequently, the parameters of the Kriging model are determined via a nested optimisation algorithm. The proposed model is validated using nine numerical functions and a case of missile system hit-rate modelling, and its performance is compared with existing methods. The results indicate that the relative root mean square error (RRMSE) of the missile hit probability surrogate model, constructed using the proposed penalty blind likelihood Kriging method with qualitative and quantitative mixed factors (QQPBLK) method, is 0.2587. This represents an approximately 30.88% improvement in RRMSE compared to the existing model, while reducing the computational time by approximately 70.01%. Moreover, under constrained computational resources, the proposed method achieves more accurate prediction outcomes than alternative approaches such as the unrestricted covariance, multiplicative covariance, and latent variable Gaussian process methods.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104407"},"PeriodicalIF":9.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"geof3D: SPARQL geometric functions for co-designing buildings","authors":"Diellza Elshani ,&nbsp;Daniel Hernandez ,&nbsp;Ali Nakhaee ,&nbsp;Anthony A. Arrascue ,&nbsp;Steffen Staab ,&nbsp;Thomas Wortmann","doi":"10.1016/j.aei.2025.104261","DOIUrl":"10.1016/j.aei.2025.104261","url":null,"abstract":"<div><div>Semantic Web technologies are increasingly used in the architecture, engineering, and construction (AEC) industry, yet the Resource Description Framework (RDF) and its query language, SPARQL, still lack native support for 3D geometry. Existing approaches either reduce geometry to 2D, rely on external spatial databases, or require processing workflows outside the semantic layer. This paper introduces geof3D, an extension to SPARQL that enables 3D geometric computation directly inside RDF triple stores. The framework is grounded in a formal function space derived from Architectural Geometry and provides typed operators for measurement, spatial predicates, constructive solid modeling, and affine transformations. These functions are implemented as SPARQL built-ins in RDF4J, supported by an execution backend that uses Java-based processing together with SFCGAL, a robust computational geometry engine accessed through the Java Native Interface (JNI). The system supports operations including geometric validation, Boolean solids, 3D spatial queries, and shape transformations without leaving the RDF environment. We evaluate geof3D using real building models from the Large-Scale Construction Robotics Laboratory and show that the framework supports spatial alignment, clash detection, and algorithmic modeling entirely through RDF-native queries. The evaluation examines both expressiveness and implementation performance, combining in-browser benchmarking with direct JNI measurements and comparative testing against a PostGIS configuration to assess performance, scalability, and geometric fidelity. All code, queries, datasets, and benchmarks are openly released. This work shows that SPARQL can serve not only as a semantic query language but also as a computational interface for 3D co-design, enabling integrated, interoperable, and geometry-aware workflows for building information management.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104261"},"PeriodicalIF":9.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AECBench: A hierarchical benchmark for knowledge evaluation of large language models in the AEC field","authors":"Chen Liang ,&nbsp;Zhaoqi Huang ,&nbsp;Haofen Wang ,&nbsp;Fu Chai ,&nbsp;Chunying Yu ,&nbsp;Huanhuan Wei ,&nbsp;Zhengjie Liu ,&nbsp;Yanpeng Li ,&nbsp;Hongjun Wang ,&nbsp;Ruifeng Luo ,&nbsp;Xianzhong Zhao","doi":"10.1016/j.aei.2026.104314","DOIUrl":"10.1016/j.aei.2026.104314","url":null,"abstract":"<div><div>Large language models (LLMs), as a novel information technology, are seeing increasing adoption in the Architecture, Engineering, and Construction (AEC) field. They have shown their potential to streamline processes throughout the building lifecycle. However, the robustness and reliability of LLMs in such a specialized and safety-critical domain remain to be evaluated. To address this challenge, this paper establishes AECBench, a comprehensive benchmark designed to quantify the strengths and limitations of current LLMs in the AEC domain. The benchmark features a five-level, cognition-oriented evaluation framework (i.e., Knowledge Memorization, Knowledge Understanding, Knowledge Reasoning, Knowledge Calculation, and Knowledge Application). Based on the framework, 23 representative evaluation tasks were defined. These tasks were derived from authentic AEC practice, with scope ranging from codes retrieval to specialized documents generation. Subsequently, a 4800-question dataset encompassing diverse formats, including open-ended questions, was crafted primarily by engineers and validated through a two-round expert review. Furthermore, an “LLM-as-a-Judge” approach was introduced to provide a scalable and consistent methodology for evaluating complex, long-form responses leveraging expert-derived rubrics. Through the evaluation of nine LLMs, a clear performance decline across five cognitive levels was revealed. Despite demonstrating proficiency in foundational tasks at the Knowledge Memorization and Understanding levels, the models showed significant performance deficits, particularly in interpreting knowledge from tables in building codes, executing complex reasoning and calculation, and generating domain-specific documents. Consequently, this study lays the groundwork for future research and development aimed at the robust and reliable integration of LLMs into safety-critical engineering practices.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104314"},"PeriodicalIF":9.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating segmentation and vision-language model for automated and interpretable building damage assessment from satellite imagery","authors":"Yong Wang ,&nbsp;Jiawei Cui ,&nbsp;Changhai Zhai ,&nbsp;Xigui Tao ,&nbsp;Yuhao Li","doi":"10.1016/j.aei.2026.104320","DOIUrl":"10.1016/j.aei.2026.104320","url":null,"abstract":"<div><div>The rapid assessment of constructed facilities after extreme events is a knowledge-intensive task critical for effective emergency management. However, methodologies for automated, object-level damage assessment at scale remain underdeveloped, often lacking fine-grained interpretability or scalability. This paper introduces a framework that integrates instance segmentation with temporal Vision Language Model (VLM), which is empowered with visual damage reasoning capabilities through fine-tuning on domain-specific knowledge, for the automated and interpretable assessment of structural assets from satellite imagery. Our three-stage approach synergizes: high-precision segmentation via a modified Segment Anything Model (SAM); spatiotemporal data pairing to isolate asset-specific changes; and BDAChat, the first temporal VLM fine-tuned for object-level damage assessment. Unlike traditional black-box models, BDAChat provides both high-accuracy damage classification and causal interpretations, serving as an intelligent damage inference system. The framework’s effectiveness and scalability are validated through the Lahaina wildfire and hurricane Ian case study. This modular framework automates and accelerates the object-level building damage assessment process, demonstrating significant potential for real-time building damage evaluation and resilient infrastructure planning. The code and dataset are available at <span><span>https://github.com/WangYong921/BDAChat</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104320"},"PeriodicalIF":9.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TFD-Trans: Time-frequency hierarchical decomposition transformer for mechanical fault diagnosis","authors":"Huan Wang ,&nbsp;Zi-Hao Ren ,&nbsp;Junyu Qi","doi":"10.1016/j.aei.2025.104303","DOIUrl":"10.1016/j.aei.2025.104303","url":null,"abstract":"<div><div>Rolling bearings are critical components in rotating machinery, and their health directly affects the safety and stability of industrial equipment. To improve fault identification under strong noise and complex operating conditions, this paper proposes a Time–Frequency Decomposition Transformer (TFD-Trans) for robust rolling bearing fault diagnosis. TFD-Trans integrates wavelet transform and Fourier transform within a signal-processing-informed deep architecture. A wavelet-driven frequency decomposition module hierarchically partitions vibration signals into multiple sub-bands, enabling fine-grained multi-scale feature extraction. A Fourier-driven autocorrelation encoding module then models global periodic dependencies in the frequency domain and enhances fault-sensitive representations. Extensive experiments on two real-world rolling bearing datasets show that TFD-Trans consistently achieves higher accuracy and stronger noise robustness than existing mainstream methods across multiple operating and signal-to-noise conditions.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104303"},"PeriodicalIF":9.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145895823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 two-phase cost sensitive-based domain adversarial neural network for anomaly detection in mass customized production","authors":"Yuan Gao ,&nbsp;Thong Ngee Goh ,&nbsp;Qingan Cui ,&nbsp;Qing Zhang ,&nbsp;Zhen He","doi":"10.1016/j.aei.2026.104371","DOIUrl":"10.1016/j.aei.2026.104371","url":null,"abstract":"<div><div>In the context of globalization and intense market competition, detecting anomalies in products is vital for enterprises to improve quality, enhance efficiency, and reduce costs. Enterprises are also pursuing mass customization to meet personalized customer needs. However, customized products are typically produced in small batches. Furthermore, modern manufacturing processes typically exhibit low defective rates. Additionally, quality results are only available after the inspections. These characteristics lead to the challenges of small sample sizes, imbalanced data, and delayed label acquisition for production data, potentially undermining the effectiveness of existing anomaly detection models. To cope with these challenges, this study proposes a two-phase cost sensitive domain adversarial neural network framework that leverages transfer learning to apply knowledge from similar mass-standardized products to customized products. When process parameters are available but inspection results are not, an unsupervised domain adversarial neural network integrated with cost-sensitive learning is employed to address the issues of small sample sizes and unlabeled data. Within this network, the cost-sensitive learning component simultaneously tackles data imbalance by biasing the label predictor towards the minority class through higher loss assignment compared to the majority class. Once inspection results become available for some customized products, the label predictor loss for these labeled samples is incorporated to further enhance anomaly detection. The experimental results demonstrate that the proposed method outperforms other state-of-the-art anomaly detection methods in practical smartphone speaker and laptop solid-state drive manufacturing processes.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104371"},"PeriodicalIF":9.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 forecasting of tunnel deformation in underground coal mines using a dynamic swarm-tuned adaptive neuro-fuzzy inference system for knowledge-driven ground control","authors":"Satar Mahdevari","doi":"10.1016/j.aei.2025.104287","DOIUrl":"10.1016/j.aei.2025.104287","url":null,"abstract":"<div><div>Timely and accurate forecasting of tunnel deformation is vital for ensuring safety and operational continuity in underground coal mining and other geotechnical infrastructures. However, conventional empirical design practices often lack the formalization, precision, and adaptability required for modern decision-support systems. This study presents an informatics-driven framework, the Dynamic Swarm-Tuned Adaptive Neuro-Fuzzy Inference System (DST-ANFIS), which formalizes empirical geomechanical knowledge by embedding expert-derived fuzzy rules into an adaptive hybrid computational intelligence model. The framework adopts a dual-phase metaheuristic optimization strategy—Improved Lion Optimization Algorithm (ILOA) for global exploration and Dynamic Particle Swarm Optimization (DPSO) for local refinement—to achieve a robust and interpretable representation of complex rock mass behavior. A case study using geomechanical data from the Tabas coal mine demonstrates that the proposed DST-ANFIS model consistently outperforms both conventional ANFIS-based variants and established machine learning benchmarks—including GA-ANFIS, PSO-ANFIS, XGBoost, SVR, and RF. On the test set, DST-ANFIS achieved the highest predictive accuracy, with an R² of 0.952 and an RMSE of 12.530. It notably surpassed the best-performing benchmark, XGBoost, improving R² by 2.8% (0.952 vs. 0.926) and reducing RMSE by 19.9% (12.530 vs. 15.640). The model also outperformed PSO-ANFIS, yielding a 2.7% increase in R² and a 24.8% decrease in RMSE, further confirming its robustness and precision across comparative metrics. Beyond predictive performance, the framework generates an interpretable fuzzy rule base that clarifies causal relationships between geomechanical parameters and deformation responses, transforming raw monitoring data into formalized, actionable engineering knowledge for intelligent and proactive ground control. This research advances engineering informatics by providing a scalable methodology for embedding empirical expertise into adaptive computational intelligence, thereby contributing to knowledge formalization and intelligent decision-support in engineering. While demonstrated in mining, the methodology generalizes to adaptive infrastructure management and complex geotechnical systems, underscoring the broader potential of dual-phase hybrid neuro-fuzzy models for engineering informatics.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104287"},"PeriodicalIF":9.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trend extrapolation for technology forecasting: Leveraging LSTM neural networks for trend analysis of space exploration vessels","authors":"Peng-Hung Tsai,&nbsp;Daniel Berleant","doi":"10.1016/j.aei.2025.104226","DOIUrl":"10.1016/j.aei.2025.104226","url":null,"abstract":"<div><div>Forecasting technological advancement in complex domains such as space exploration presents significant challenges due to the intricate interaction of technical, economic, and policy-related factors. The field of technology forecasting has long relied on quantitative trend extrapolation techniques, such as growth curves (e.g., Moore’s law) and time series models, to project technological progress. To assess the current state of these methods, we conducted an updated systematic literature review (SLR) that incorporates recent advances. This review highlights a growing trend toward machine learning-based hybrid models.</div><div>Motivated by this review, we developed a forecasting model that combines long short-term memory (LSTM) neural networks with an augmentation of Moore’s law to predict spacecraft lifetimes. Operational lifetime is an important engineering characteristic of spacecraft and a potential proxy for technological progress in space exploration. Lifetimes were modeled as depending on launch date and additional predictors.</div><div>Our modeling analysis introduces a novel advance in the recently introduced Start Time End Time Integration (STETI) approach. STETI addresses a critical right censoring problem known to bias lifetime analyses: the more recent the launch dates, the shorter the lifetimes of the spacecraft that have failed and can thus contribute lifetime data. Longer-lived spacecraft are still operating and therefore do not contribute data. This systematically distorts putative lifetime versus launch date curves by biasing lifetime estimates for recent launch dates downward. STETI mitigates this distortion by interconverting between expressing lifetimes as functions of launch time and modeling them as functions of failure time. This study is the first to apply STETI within a neural network framework. Hyperparameter tuning via Bayesian optimization identified the best-performing model, which outperforms a regression-based baseline. The model’s predictions across hypothetical scenarios highlight the influence of factors such as spacecraft launch mass, mission destination, and country of manufacture. The results provide insights relevant to space mission planning and policy decision-making.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104226"},"PeriodicalIF":9.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145792110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}