Ping Huang
- Optimizing gypsum particleboard properties: An orthogonal analysis of pennisetum giganteum and phosphogypsum composites. In: Case Studies in Construction Materials, v. 20 (July 2024). (2024):
- Study on the combination effect of tunnel slope and longitudinal fire location on the asymmetric flow fields in a naturally ventilated tunnel. In: Tunnelling and Underground Space Technology, v. 146 (April 2024). (2024):
- Utilizing waste stone powder for improving properties of phosphogypsum-based composite prepared by semi-dry method. In: Construction and Building Materials, v. 426 (May 2024). (2024):
- Experimental study on the temperature distribution of impingement flow in a double slope roof generated by jet fire. In: Journal of Building Engineering, v. 82 (April 2024). (2024):
- Fluidity, mechanical properties, shrinkage of alkali-activated slag/stainless steel slag mortars with composite activators. In: Journal of Building Engineering, v. 75 (September 2023). (2023):
- Application of sugar cane bagasse ash as filler in ultra-high performance concrete. In: Journal of Building Engineering, v. 71 (July 2023). (2023):
- Reservoir CO2 evasion flux and controlling factors of carbon species traced by δ13CDIC at different regulating phases of a hydro-power dam. In: Science of The Total Environment, v. 698 (January 2020). (2020):
- Study on an emergency evacuation model considering information transfer and rerouting: Taking a simplified H-shape metro station hall as an example. In: Tunnelling and Underground Space Technology, v. 124 (June 2022). (2022):
- Use of sugar cane bagasse ash in ultra-high performance concrete (UHPC) as cement replacement. In: Construction and Building Materials, v. 317 (January 2022). (2022):
- (2019): Statistical delay distribution analysis on high-speed railway trains. In: Journal of Modern Transportation, v. 27, n. 3 (January 2019).
- Forecasting primary delay recovery of high-speed railway using multiple linear regression, supporting vector machine, artificial neural network, and random forest regression. In: Canadian Journal of Civil Engineering / Revue canadienne de génie civil, v. 46, n. 5 (May 2019). (2019):