Fangyu Liu
- Automated, economical, and environmentally-friendly asphalt mix design based on machine learning and multi-objective grey wolf optimization. Dans: Journal of Traffic and Transportation Engineering (English Edition), v. 11, n. 3 (juin 2024). (2024):
- Multiple-type distress detection in asphalt concrete pavement using infrared thermography and deep learning. Dans: Automation in Construction, v. 161 (mai 2024). (2024):
- Transfer learning-based encoder-decoder model with visual explanations for infrastructure crack segmentation: New open database and comprehensive evaluation. Dans: Underground Space, v. 17 (août 2024). (2024):
- Involving prediction of dynamic modulus in asphalt mix design with machine learning and mechanical-empirical analysis. Dans: Construction and Building Materials, v. 407 (décembre 2023). (2023):
- Exploring the mechanism of micro-nano bubble water in enhancing the mechanical properties of sulfoaluminate cement-based materials. Dans: Construction and Building Materials, v. 411 (janvier 2024). (2024):
- PI-LSTM: Physics-informed long short-term memory network for structural response modeling. Dans: Engineering Structures, v. 292 (octobre 2023). (2023):
- Asphalt pavement fatigue crack severity classification by infrared thermography and deep learning. Dans: Automation in Construction, v. 143 (novembre 2022). (2022):
- Improving asphalt mix design considering international roughness index of asphalt pavement predicted using autoencoders and machine learning. Dans: Construction and Building Materials, v. 360 (décembre 2022). (2022):
- Optimizing asphalt mix design through predicting the rut depth of asphalt pavement using machine learning. Dans: Construction and Building Materials, v. 356 (novembre 2022). (2022):
- Improving asphalt mix design by predicting alligator cracking and longitudinal cracking based on machine learning and dimensionality reduction techniques. Dans: Construction and Building Materials, v. 354 (novembre 2022). (2022):
- UNet-based model for crack detection integrating visual explanations. Dans: Construction and Building Materials, v. 322 (mars 2022). (2022):
- Deep learning and infrared thermography for asphalt pavement crack severity classification. Dans: Automation in Construction, v. 140 (août 2022). (2022):
- Deep transfer learning-based vehicle classification by asphalt pavement vibration. Dans: Construction and Building Materials, v. 342 (août 2022). (2022):
- Optimizing asphalt mix design through predicting effective asphalt content and absorbed asphalt content using machine learning. Dans: Construction and Building Materials, v. 325 (mars 2022). (2022):
- An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete containing fly ash and slag power. Dans: Frontiers of Structural and Civil Engineering, v. 14, n. 6 (août 2020). (2020):
- Experimental investigation on the tensile behavior of hybrid steel-PVA fiber reinforced concrete containing fly ash and slag powder. Dans: Construction and Building Materials, v. 241 (avril 2020). (2020):
- Experimental investigation on the flexural behavior of hybrid steel-PVA fiber reinforced concrete containing fly ash and slag powder. Dans: Construction and Building Materials, v. 228 (décembre 2019). (2019):
- An experimental investigation on the integral waterproofing capacity of polypropylene fiber concrete with fly ash and slag powder. Dans: Construction and Building Materials, v. 212 (juillet 2019). (2019):