Niannian Wang 
- (2024): Interface bonding characteristics of the Cured-in-Place Pipe lined steel plates and parameter sensitivity analysis. In: Structures, v. 69 (November 2024).
- (2024): Prediction model of maximum stress for concrete pipes based on XGBoost-PSO algorithm. In: Structures, v. 68 (Oktober 2024).
- (2024): A CFD-DEM investigation into hydraulic transport and retardation response characteristics of drainage pipeline siltation using intelligent model. In: Tunnelling and Underground Space Technology, v. 152 (Oktober 2024).
- (2024): Mechanical behavior and parameter sensitivity analysis of water supply steel pipes under complex service load combinations. In: Structures, v. 67 (September 2024).
- : Attention‐optimized 3D segmentation and reconstruction system for sewer pipelines employing multi‐view images. In: 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. In: Tunnelling and Underground Space Technology, v. 148 (Juni 2024).
- (2024): Mechanical characteristics of concrete pipes under the coupled effects of the pressure, seepage, and flow fields. In: Structures, v. 61 (März 2024).
- (2024): An automatic defect classification and segmentation method on three-dimensional point clouds for sewer pipes. In: Tunnelling and Underground Space Technology, v. 143 (Januar 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. In: Tunnelling and Underground Space Technology, v. 142 (Dezember 2023).
- (2023): Defects identification and location of underground space for ground penetrating radar based on deep learning. In: Tunnelling and Underground Space Technology, v. 140 (Oktober 2023).
- (2023): A low-cost 3D reconstruction and measurement system based on structure-from-motion (SFM) and multi-view stereo (MVS) for sewer pipelines. In: Tunnelling and Underground Space Technology, v. 141 (November 2023).
- (2023): 3D reconstruction and segmentation system for pavement potholes based on improved structure-from-motion (SFM) and deep learning. In: Construction and Building Materials, v. 398 (September 2023).
- (2023): Experimental and practical investigation of reinforcement mechanism on permeable polymer in loose area of drainage pipeline. In: Tunnelling and Underground Space Technology, v. 140 (Oktober 2023).
- (2023): Modeling automatic pavement crack object detection and pixel-level segmentation. In: Automation in Construction, v. 150 (Juni 2023).
- (2023): Automatic Damage Segmentation Framework for Buried Sewer Pipes Based on Machine Vision: Case Study of Sewer Pipes in Zhengzhou, China. In: Journal of Infrastructure Systems, v. 29, n. 1 (März 2023).
- (2023): Failure experiment on CFRP-strengthened prestressed concrete cylinder pipe with broken wires. In: 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. In: Tunnelling and Underground Space Technology, v. 136 (Juni 2023).
- (2022): Impact of erosion voids and internal corrosion on concrete pipes under traffic loads. In: Tunnelling and Underground Space Technology, v. 130 (Dezember 2022).
- (2022): Automatic defogging, deblurring, and real-time segmentation system for sewer pipeline defects. In: Automation in Construction, v. 144 (Dezember 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). In: Automation in Construction, v. 143 (November 2022).
- (2022): A Feature Selection–Based Intelligent Framework for Predicting Maximum Depth of Corroded Pipeline Defects. In: Journal of Performance of Constructed Facilities (ASCE), v. 36, n. 5 (Oktober 2022).
- (2022): Innovative method for pavement multiple damages segmentation and measurement by the Road-Seg-CapsNet of feature fusion. In: Construction and Building Materials, v. 324 (März 2022).
- (2021): A Multi-defect detection system for sewer pipelines based on StyleGAN-SDM and fusion CNN. In: Construction and Building Materials, v. 312 (Dezember 2021).
- (2019): Novel System for Rapid Investigation and Damage Detection in Cultural Heritage Conservation Based on Deep Learning. In: Journal of Infrastructure Systems, v. 25, n. 3 (September 2019).
- (2018): Damage Classification for Masonry Historic Structures Using Convolutional Neural Networks Based on Still Images. In: Computer-Aided Civil and Infrastructure Engineering, v. 33, n. 12 (November 2018).
- (2019): CNN-based statistics and location estimation of missing components in routine inspection of historic buildings. In: Journal of Cultural Heritage, v. 38 (Juli 2019).
- (2020): Autonomous damage segmentation and measurement of glazed tiles in historic buildings via deep learning. In: Computer-Aided Civil and Infrastructure Engineering, v. 35, n. 3 (5 Februar 2020).
- (2019): Experimental and numerical study on seismic performance of deficient interior RC joints retrofitted with prestressed high-strength steel strips. In: Engineering Structures, v. 190 (Juli 2019).
- (2019): Automatic damage detection of historic masonry buildings based on mobile deep learning. In: Automation in Construction, v. 103 (Juli 2019).
- (2019): Bolt loosening angle detection technology using deep learning. In: Structural Control and Health Monitoring, v. 26, n. 1 (Januar 2019).
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