Niannian Wang 
- : Attention‐optimized 3D segmentation and reconstruction system for sewer pipelines employing multi‐view images. Dans: Computer-Aided Civil and Infrastructure Engineering.
- (2024): Intelligent prediction model of a polymer fracture grouting effect based on a genetic algorithm-optimized back propagation neural network. Dans: Tunnelling and Underground Space Technology, v. 148 (juin 2024).
- (2024): Mechanical characteristics of concrete pipes under the coupled effects of the pressure, seepage, and flow fields. Dans: Structures, v. 61 (mars 2024).
- (2024): Shear behavior of a two-component non-water reactive foamed polyurethane (TNFPU) grouting material under different stress levels. Dans: Construction and Building Materials, v. 411 (janvier 2024).
- (2024): An automatic defect classification and segmentation method on three-dimensional point clouds for sewer pipes. Dans: Tunnelling and Underground Space Technology, v. 143 (janvier 2024).
- (2023): Integrating fluid–solid coupling domain knowledge with deep learning models: An automatic and interpretable diagnostic system for the silting disease of drainage pipelines. Dans: Tunnelling and Underground Space Technology, v. 142 (décembre 2023).
- (2023): Defects identification and location of underground space for ground penetrating radar based on deep learning. Dans: Tunnelling and Underground Space Technology, v. 140 (octobre 2023).
- (2023): A low-cost 3D reconstruction and measurement system based on structure-from-motion (SFM) and multi-view stereo (MVS) for sewer pipelines. Dans: Tunnelling and Underground Space Technology, v. 141 (novembre 2023).
- (2023): 3D reconstruction and segmentation system for pavement potholes based on improved structure-from-motion (SFM) and deep learning. Dans: Construction and Building Materials, v. 398 (septembre 2023).
- (2023): Experimental and practical investigation of reinforcement mechanism on permeable polymer in loose area of drainage pipeline. Dans: Tunnelling and Underground Space Technology, v. 140 (octobre 2023).
- (2023): Modeling automatic pavement crack object detection and pixel-level segmentation. Dans: Automation in Construction, v. 150 (juin 2023).
- (2023): Transformer‐optimized generation, detection, and tracking network for images with drainage pipeline defects. Dans: Computer-Aided Civil and Infrastructure Engineering, v. 38, n. 15 (avril 2023).
- (2023): Automatic Damage Segmentation Framework for Buried Sewer Pipes Based on Machine Vision: Case Study of Sewer Pipes in Zhengzhou, China. Dans: Journal of Infrastructure Systems, v. 29, n. 1 (mars 2023).
- (2023): Failure experiment on CFRP-strengthened prestressed concrete cylinder pipe with broken wires. Dans: Tunnelling and Underground Space Technology, v. 135 (mai 2023).
- (2023): Numerical study on mechanical properties of pipeline installed via horizontal directional drilling under static and dynamic traffic loads. Dans: Tunnelling and Underground Space Technology, v. 136 (juin 2023).
- (2022): Impact of erosion voids and internal corrosion on concrete pipes under traffic loads. Dans: Tunnelling and Underground Space Technology, v. 130 (décembre 2022).
- (2022): Automatic defogging, deblurring, and real-time segmentation system for sewer pipeline defects. Dans: Automation in Construction, v. 144 (décembre 2022).
- (2022): Automatic damage segmentation in pavement videos by fusing similar feature extraction siamese network (SFE-SNet) and pavement damage segmentation capsule network (PDS-CapsNet). Dans: Automation in Construction, v. 143 (novembre 2022).
- (2022): A Feature Selection–Based Intelligent Framework for Predicting Maximum Depth of Corroded Pipeline Defects. Dans: Journal of Performance of Constructed Facilities (ASCE), v. 36, n. 5 (octobre 2022).
- (2022): Innovative method for pavement multiple damages segmentation and measurement by the Road-Seg-CapsNet of feature fusion. Dans: Construction and Building Materials, v. 324 (mars 2022).
- (2022): Research on 3D Defect Information Management of Drainage Pipeline Based on BIM. Dans: Buildings, v. 12, n. 2 (18 janvier 2022).
- (2022): Study of Damage Quantification of Concrete Drainage Pipes Based on Point Cloud Segmentation and Reconstruction. Dans: Buildings, v. 12, n. 2 (18 janvier 2022).
- (2021): A Multi-defect detection system for sewer pipelines based on StyleGAN-SDM and fusion CNN. Dans: Construction and Building Materials, v. 312 (décembre 2021).
- (2019): Novel System for Rapid Investigation and Damage Detection in Cultural Heritage Conservation Based on Deep Learning. Dans: Journal of Infrastructure Systems, v. 25, n. 3 (septembre 2019).
- (2018): Damage Classification for Masonry Historic Structures Using Convolutional Neural Networks Based on Still Images. Dans: Computer-Aided Civil and Infrastructure Engineering, v. 33, n. 12 (novembre 2018).
- (2019): CNN-based statistics and location estimation of missing components in routine inspection of historic buildings. Dans: Journal of Cultural Heritage, v. 38 (juillet 2019).
- (2020): Autonomous damage segmentation and measurement of glazed tiles in historic buildings via deep learning. Dans: Computer-Aided Civil and Infrastructure Engineering, v. 35, n. 3 (5 février 2020).
- (2019): Experimental and numerical study on seismic performance of deficient interior RC joints retrofitted with prestressed high-strength steel strips. Dans: Engineering Structures, v. 190 (juillet 2019).
- (2019): Automatic damage detection of historic masonry buildings based on mobile deep learning. Dans: Automation in Construction, v. 103 (juillet 2019).
- (2019): Bolt loosening angle detection technology using deep learning. Dans: Structural Control and Health Monitoring, v. 26, n. 1 (janvier 2019).
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