- Research progress of loess reinforcement technology: A bibliometric network analysis. In: Construction and Building Materials, v. 436 (July 2024). (2024):
- Relational database for building strong motion recordings used for seismic impact assessments. In: Earthquake Spectra, v. 39, n. 2 (29 April 2023). (2023):
- Investigating the effectiveness of carbon nanotubes for the compressive strength of concrete using AI-aided tools. In: Case Studies in Construction Materials, v. 20 (July 2024). (2024):
- Review of enhanced heat and mass transfer by additives. In: Science and Technology for the Built Environment, v. 28, n. 10 (August 2022). (2022):
- A relational database to support post-earthquake building damage and recovery assessment. In: Earthquake Spectra, v. 38, n. 2 (27 January 2022). (2022):
- Development of a Generalized Cross-Building Structural Response Reconstruction Model Using Strong Motion Data. In: Journal of Structural Engineering (ASCE), v. 148, n. 6 (June 2022). (2022):
- Machine learning applications for building structural design and performance assessment: State-of-the-art review. In: Journal of Building Engineering, v. 33 (January 2021). (2021):
- Reconstructing seismic response demands across multiple tall buildings using kernelābased machine learning methods. In: Structural Control and Health Monitoring, v. 26, n. 7 (May 2019). (2019):
- A machine learning framework for assessing post-earthquake structural safety. In: Structural Safety, v. 72 (May 2018). (2018):
- Estimating aftershock collapse vulnerability using mainshock intensity, structural response and physical damage indicators. In: Structural Safety, v. 68 (September 2017). (2017):
- Interbuilding interpolation of peak seismic response using spatially correlated demand parameters. In: Earthquake Engineering and Structural Dynamics, v. 47, n. 5 (25 April 2018). (2018):