Advanced Engineering Informatics最新文献

{"title":"Hierarchical fair competition-based differential evolution algorithm for global optimization and application in LED spectral matching coefficients searching","authors":"Yongjun Wang ,&nbsp;Xinglei Xu ,&nbsp;Chengliang Jin ,&nbsp;Luhao Cao ,&nbsp;Qiming Tian ,&nbsp;Shiwei Xu","doi":"10.1016/j.aei.2026.104338","DOIUrl":"10.1016/j.aei.2026.104338","url":null,"abstract":"<div><div>Evolutionary optimization algorithms continue to attract attention for addressing challenging global optimization (GO) problems owing to their flexibility and adaptability. This study proposed an improved differential evolution (DE)-based algorithm within the hierarchical fair competition (HFC) framework, namely HFCDE. In contrast to traditional DE, which has only one phase, layer, and population, HFCDE allows individuals to evolve across multi-phases and -layers in sub-populations: in each hierarchical layer of each phase, each sub-population evolves iteratively using traditional DE operators until the termination condition is reached. In addition, the parameters such as the number of phases, the number of layers, and the portion coefficients (used to regulate the maximum iterations in each phase) can be adjusted independently for specific problems. Specifically, a typical version of this approach, a two-layer, three-phase HFCDE algorithm, was investigated in detail. Experiments were conducted on 70 benchmark functions (including 13 high-dimensional ones) as well as a complex optimization problem in industrial lighting systems involving the spectral coefficient of a light-emitting diode (LED). Numerical results demonstrated that an accelerated global convergence speed, greater robustness, and higher solution accuracy were achieved, compared with some state-of-the-art evolutionary optimization methods. The percentage of cases where HFCDE outperformed competitors ranged between 71 and 100%. The key parameter settings were also investigated and discussed in detail, showing the relaxed parameter tuning in HFCDE. Furthermore, the HFCDE framework—with its flexible parameter setting mechanism and extensibility to different layers/phases, integration with an adaptive or dynamic parameter adjustment strategy, and replacement of DE operators by other optimization operators—has great potential for addressing GO challenges.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104338"},"PeriodicalIF":9.9,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977712","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 transformer-based framework for cross-material in situ monitoring in extrusion-based bioprinting","authors":"Jiayi Zhang ,&nbsp;Kaicheng Yu ,&nbsp;Yifeng Yao ,&nbsp;Lihua Lu ,&nbsp;Qiang Gao ,&nbsp;Peng Zhang ,&nbsp;Guoyin Shang ,&nbsp;Swee Leong Sing","doi":"10.1016/j.aei.2026.104323","DOIUrl":"10.1016/j.aei.2026.104323","url":null,"abstract":"<div><div>Additive manufacturing is advancing toward intelligent and functionally reliable fabrication, particularly in biomedical applications. In extrusion-based three-dimensional (3D) bioprinting, machine learning (ML)-enabled <em>in situ</em> monitoring is crucial for improving print quality and ensuring the functional performance of tissue engineering constructs. This study proposes a transformer-based transfer learning framework for cross-material monitoring that efficiently transfers knowledge across diverse polymer systems under limited-data conditions. The model extracts geometric features of extruded filaments from in situ monitoring images and achieves 99.55% classification accuracy on PLCL and over 98% accuracy for PCL/<span><math><mi>β</mi></math></span>-TCP and GelMA datasets with less than 0.1% trainable parameters. Beyond visual monitoring, the predicted filament process states were quantitatively correlated with downstream mechanical performance, demonstrating a 24.6% improvement in tensile strength and enhanced geometric fidelity under optimal-heating conditions. Furthermore, <em>in vivo</em> wound-healing experiments using the bioprinted constructs verified the biological relevance and translational potential of the proposed monitoring strategy. Constructs fabricated under optimal conditions promoted accelerated tissue regeneration and vascularization, achieving faster wound closure within 10 days compared with suboptimal printing conditions. Overall, the proposed transformer-based cross-material framework establishes a generalizable and biologically validated paradigm for vision-guided process monitoring, providing a key step toward intelligent and adaptive bioprinting.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104323"},"PeriodicalIF":9.9,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977715","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":"2026-01-16","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":"Efficiency-aware seismic fragility analysis of super-high arch dam using unsupervised ground motion clustering with probabilistic representation","authors":"Yingbo Chen ,&nbsp;Mingchao Li ,&nbsp;Qiubing Ren ,&nbsp;Zhiyong Qi ,&nbsp;Hui Liang","doi":"10.1016/j.aei.2026.104318","DOIUrl":"10.1016/j.aei.2026.104318","url":null,"abstract":"<div><div>Machine Learning (ML)-driven approaches have been employed to replace computationally intensive seismic simulations of hydraulic engineering structures. For the complex seismic responses of arch dams, constructing a metamodel that captures the nonlinear relationship between ground motion inputs and structural response outputs using a limited set of numerical simulations can significantly reduce the computational cost. However, conventional deterministic predictions and fragility analyses fail to account for the high aleatory and epistemic uncertainties inherent in the seismic response of arch dams. To this end, this paper proposes an efficient fragility analysis method for arch dams that integrates probabilistic ML algorithms with the traditional Incremental Dynamic Analysis. By constructing a Natural Gradient Boosting (NGBoost) metamodel for the arch dam dynamic response, not only can the predicted mean value of each response sample be obtained, but also its conditional probability distribution. Superimpose the simulation data with the response distribution predicted by NGBoost, and the binary parameters of the fragility function are estimated, thereby generating both the fragility curve and the uncertain fragility interval of arch dam. Additionally, representative Ground Motion Records (GMRs) for the arch dam are selected using the Partitioning Around Medoids (PAM) unsupervised clustering technique, determining the minimum subset proportion that effectively represents the whole GMR dataset. The effectiveness of the proposed method is validated in a super-high arch dam. The 40% GMR proportion is found to adequately reproduce the fragility curves of the whole dataset, with the reference curve falling within the derived uncertainty interval, achieving a 56.8% reduction in computational cost. The 60% GMR proportion ensured fragility curves with balanced accuracy and effectiveness, exhibiting maximum mean differences of 0.058 and maximum standard deviation differences of 0.031 from reference curves across all damage levels, while reducing computational cost by 39.7%. Comparative results demonstrate the superiority of NGBoost and PAM over existing deterministic metamodels and GMRs selection techniques, respectively. The efficient fragility analysis method proposed in this study ultimately enables the direct characterization of uncertainties in arch dam seismic responses.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104318"},"PeriodicalIF":9.9,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978250","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":"Pilot-driven deep learning based RIS-assisted beamforming for secrecy rate maximization","authors":"Natasha Elizabeth Francis,&nbsp;Khoa Tran Phan,&nbsp;Peng Cheng","doi":"10.1016/j.aei.2026.104352","DOIUrl":"10.1016/j.aei.2026.104352","url":null,"abstract":"<div><div>Secure beamforming in reconfigurable intelligent surface (RIS)-assisted multiuser downlink systems is challenging due to high computational complexity and complex channel state information (CSI) estimation. This work proposes a pilot-driven beamforming network (PilotBeamNet) that jointly designs base-station (BS) transmit beamforming and quantized RIS phases directly from uplink pilot received signals with legitimate-user location cues to capture geometry. The framework avoids explicit channel estimation and slow iterative algorithms. A convolutional module reads each pilot frame, a long short term memory (LSTM) block with lightweight temporal attention aggregates them, and two simple heads output the beamformers and the discrete RIS phases. The location cues are embedded and fused with the features extracted from the pilot frames by the convolutional, LSTM, and temporal attention modules. Training maximizes ergodic secrecy rate (ESR) through Monte Carlo sampling of unknown eavesdropper channels, enabling robustness without requiring eavesdropper CSI. Once trained, PilotBeamNet performs single-pass inference with latency determined only by network depth. Across all tested conditions, PilotBeamNet achieves 10%–30% ESR improvement depending on the signal-to-noise ratio (SNR), pilot length, and RIS size, while reducing inference latency by more than an order of magnitude compared to alternating optimization (AO) and outperforming multilayer perceptron (MLP) baselines. It also maintains consistent performance under phase quantization and delivers higher secrecy rates across all evaluated configurations.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104352"},"PeriodicalIF":9.9,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977717","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":"DirA-Net: Directional awareness enhancement network for long-distance road crack detection","authors":"Chenglong Mi,&nbsp;Yanling Chen,&nbsp;Quan Qi,&nbsp;Huaibin Qin","doi":"10.1016/j.aei.2026.104344","DOIUrl":"10.1016/j.aei.2026.104344","url":null,"abstract":"<div><div>To tackle the challenges of long-range dependency modeling and noise interference in road crack detection, this paper presents the directional awareness enhancement network (DirA-Net). Conventional CNNs struggle to capture the linear continuity of cracks due to limited receptive fields. To address this, an eight-directional spatial state module (8-SSM) captures directional structures through multi-directional state propagation, effectively modeling elongated crack patterns. To mitigate background noise and enhance multi-scale feature fusion, a median receptive field fusion module (MRF) integrates median filtering with multi-scale dilated convolutions, improving noise suppression and receptive field integration. A global shuffle attention module (GSA) further strengthens long-range feature dependencies by combining channel attention, channel shuffling, and spatial attention. Furthermore, a spatial position awareness module (SPA) leverages coordinate-guided attention and global spatial context to enhance crack localization and structural perception. Experiments on five public datasets show that the proposed method outperforms eight state-of-the-art models in terms of average F1-score and mIoU across different thresholds, while achieving an average inference speed of 12.77 FPS, demonstrating both accuracy and efficiency.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104344"},"PeriodicalIF":9.9,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978248","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":"Depth-guided cross-modal fusion and diffusion-based enhancement for robust pavement defect segmentation","authors":"Yihui Shan ,&nbsp;Wei Li ,&nbsp;Jiaqi Shi ,&nbsp;Yansong Wang ,&nbsp;Zhenzhen Xing ,&nbsp;Jiangang Ding ,&nbsp;Lili Pei","doi":"10.1016/j.aei.2026.104339","DOIUrl":"10.1016/j.aei.2026.104339","url":null,"abstract":"<div><div>Accurate and efficient perception of pavement conditions is essential for maintaining transportation infrastructure and ensuring driving safety. In real-world road environments, visual and depth data collected by inspection or autonomous vehicles are often affected by modality imbalance, sensor noise, and image degradation, which compromise the reliability of defect segmentation. To address these challenges, this study proposes a cross-modal segmentation framework that integrates depth-guided fusion with generative latent feature enhancement to achieve robust pavement defect perception under diverse conditions. A defect-centric and class-aware depth prompting strategy is developed to transform geometric priors into explicit guidance for the intensity stream, enabling background suppression before encoding and boundary refinement within intermediate layers. In parallel, a latent feature enhancement module aligns the Segment Anything Model (SAM) feature space with a pretrained diffusion latent space and performs efficient one-step denoising, restoring structural consistency while avoiding the heavy overhead of iterative diffusion sampling. The overall design preserves the efficiency and generalization of SAM while introducing lightweight trainable adapters and low-rank diffusion updates. Experimental evaluations on multimodal pavement datasets demonstrate that the proposed approach achieves higher segmentation accuracy and robustness compared with state-of-the-art fusion methods. The results highlight the potential of the proposed framework to support intelligent pavement inspection, condition assessment, and maintenance decision-making.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104339"},"PeriodicalIF":9.9,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978246","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":"Motion-prior and Confidence-aware Gaussian Splatting (MCGS) SLAM for 3D scene reconstruction of indoor built environments","authors":"Yuanyuan Deng,&nbsp;Vincent J.L. Gan","doi":"10.1016/j.aei.2026.104317","DOIUrl":"10.1016/j.aei.2026.104317","url":null,"abstract":"<div><div>3D Gaussian Splatting SLAM is an emerging area for 3D scene reconstruction, yet it faces challenges in maintaining motion and temporal consistency under rapid motion, illumination changes, and dynamic object occlusion in the built environment. This paper proposes a motion-prior and confidence-aware Gaussian Splatting (MCGS) SLAM, which hardnesses a probabilistic motion-prior framework that enforces kinematic constraints and adapts to varying motion states. Secondly, the confidence estimation mechanism evaluates the reliability of Gaussian primitives based on temporal, geometric, photometric, and structural indicators to guide a balanced spatial representation. An adaptive keyframe selection method further optimizes keyframe density and improves temporal coherence by dynamically adjusting keyframe frequency. Lastly, multi-task optimization is undertaken, which combines photometric and geometric loss, probabilistic motion constraints, and confidence-based weighting, enabling joint optimization of pose tracking accuracy and mapping quality. Experiments show that MCGS-SLAM achieves 83% trajectory error reduction and 3D mapping quality gains while maintaining a competitive frame rate.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104317"},"PeriodicalIF":9.9,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978247","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":"Dynamic reliability informed adaptive task scheduling for multirobot manufacturing system","authors":"Jian Zhou ,&nbsp;Hang Zhang ,&nbsp;Buyun Tang ,&nbsp;Lianyu Zheng ,&nbsp;Yiwei Wang","doi":"10.1016/j.aei.2026.104335","DOIUrl":"10.1016/j.aei.2026.104335","url":null,"abstract":"<div><div>Efficient and reliable scheduling is critical for multirobot manufacturing systems, yet existing performance diagnosis and reliability evaluation methods fail to capture the dynamic evolution of system states, and current scheduling often neglects reliability information. This paper proposes a collaborative scheduling method integrating dynamic reliability assessment. First, multisource operational signals are collected and fused to identify degradation stages. Based on a classification probability mapping mechanism, the service state is then converted into probabilistic attributes applicable to modeling and scheduling. Subsequently, a system model incorporating structure, state, and behavior is constructed, and reliability indicators of the system are dynamically evaluated through logical model evolution. On this basis, a heuristic multiagent reinforcement learning scheduling algorithm is designed, using reliability attributes and constraint graph structures as inputs to achieve collaborative scheduling of multiple robots. Finally, during task execution, real-time state changes are dynamically perceived, and the scheduling plan is adaptively updated by triggering rescheduling based on the real-time evaluation results, thus forming a reliability-informed closed-loop scheduling mechanism. Case studies demonstrate 18.2% reduction in task completion time for static allocation compared to four baseline methods, along with 17.1% improvement for dynamic rescheduling against engineering practices. These quantitative results confirm the method’s significant enhancements in scheduling responsiveness to degradation, adaptive task optimization, and overall system stability and efficiency.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104335"},"PeriodicalIF":9.9,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977718","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":"Enabling AI-driven modular building design: an auto-decoder approach for IFC 3D geometry representation","authors":"Sang Du ,&nbsp;Lei Hou ,&nbsp;Guomin (Kevin) Zhang ,&nbsp;Yang Zou ,&nbsp;Haosen Chen","doi":"10.1016/j.aei.2026.104326","DOIUrl":"10.1016/j.aei.2026.104326","url":null,"abstract":"<div><div>Modular building design requires numerous context-dependent component variants that traditional constraint-based methods cannot exhaustively enumerate. Industry Foundation Classes (IFC) models encode rich spatial and semantic context from completed modular projects. This context could enable Artificial Intelligence (AI) models to generate component variants and complement constraint-based methods. However, IFC 3D geometry that carries spatial context is not directly usable by AI models. This stems from IFC’s complex data structure. To address this limitation, this paper proposes a readily deployable auto-decoder method that produces AI-compatible vectors from IFC geometry. First, an IFC export strategy that retains component spatial context is employed. Second, a sampling method that pairs 3D points with their distances to the nearest surface is applied. Third, an auto-decoder neural network that jointly optimises per-component vectors and the model weights is presented, yielding context-aware representation vectors for modular components. Finally, an octree-based decoder for accurate geometry recovery from vectors is employed. Experiments on real-world modular project data demonstrate that the resulting vectors preserve geometric fidelity and support component variant generation. Geometric fidelity is confirmed by the mean and maximum surface reconstruction errors of 14.57 mm and 51.94 mm, sufficient for modular building design analysis. Support for component variant generation is evidenced by geometric interpolation linearity exceeding 0.98 out of 1, showing excellent variant generation suitability. This method makes IFC spatial context accessible to AI-driven modular design methods, transforming Design for Manufacture and Assembly (DfMA) data into actionable knowledge. Codes available on GitHub.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104326"},"PeriodicalIF":9.9,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978257","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}