Jingjing Guo 
- (2024): Feature selection for chillers fault diagnosis from the perspectives of machine learning and field application. In: Energy and Buildings, v. 307 (März 2024).
- (2024): Surface defect detection of civil structures using images: Review from data perspective. In: Automation in Construction, v. 158 (Februar 2024).
- (2024): A transformer and self-cascade operation-based architecture for segmenting high-resolution bridge cracks. In: Automation in Construction, v. 158 (Februar 2024).
- (2023): Informativeness‐guided active learning for deep learning–based façade defects detection. In: Computer-Aided Civil and Infrastructure Engineering, v. 38, n. 17 (November 2023).
- (2023): Implicit function‐based continuous representation for meticulous segmentation of cracks from high‐resolution images. In: Computer-Aided Civil and Infrastructure Engineering, v. 39, n. 4 (November 2023).
- (2023): A crack-segmentation algorithm fusing transformers and convolutional neural networks for complex detection scenarios. In: Automation in Construction, v. 152 (August 2023).
- (2022): Few-shot classification of façade defects based on extensible classifier and contrastive learning. In: Automation in Construction, v. 141 (September 2022).
- (2022): An adaptive down-sampling method of laser scan data for scan-to-BIM. In: Automation in Construction, v. 135 (März 2022).
- (2022): Multi‐defect segmentation from façade images using balanced copy–paste method. In: Computer-Aided Civil and Infrastructure Engineering, v. 37, n. 11 (August 2022).
- (2022): Benefits of Terrestrial Laser Scanning for Construction QA/QC: A Time and Cost Analysis. In: Journal of Management in Engineering (ASCE), v. 38, n. 2 (März 2022).
- (2022): Human-Related Uncertainty Analysis for Automation-Enabled Façade Visual Inspection: A Delphi Study. In: Journal of Management in Engineering (ASCE), v. 38, n. 2 (März 2022).
- (2021): Effects of dataset characteristics on the performance of fatigue detection for crane operators using hybrid deep neural networks. In: Automation in Construction, v. 132 (Dezember 2021).
- (2021): Evaluation-oriented façade defects detection using rule-based deep learning method. In: Automation in Construction, v. 131 (November 2021).
- (2021): Semi‐supervised learning based on convolutional neural network and uncertainty filter for façade defects classification. In: Computer-Aided Civil and Infrastructure Engineering, v. 36, n. 3 (November 2021).
- (2019): Compression after impact behavior of composite foam-core sandwich panels. In: Composite Structures, v. 225 (Oktober 2019).
- (2020): Façade defects classification from imbalanced dataset using meta learning‐based convolutional neural network. In: Computer-Aided Civil and Infrastructure Engineering, v. 35, n. 12 (12 November 2020).
- (2020): Geometric quality inspection of prefabricated MEP modules with 3D laser scanning. In: Automation in Construction, v. 111 (März 2020).
- (2020): Automatic classification of common building materials from 3D terrestrial laser scan data. In: Automation in Construction, v. 110 (Februar 2020).
- (2020): Time and cost analysis of geometric quality assessment of structural columns based on 3D terrestrial laser scanning. In: Automation in Construction, v. 110 (Februar 2020).
- (2019): Theoretical Research and Simulation Analysis on the Cutter Spacing of Double Disc Cutters Breaking Rock. In: KSCE Journal of Civil Engineering, v. 23, n. 7 (Mai 2019).
Structurae kooperiert mit
