Somin Park
- Natural language instructions for intuitive human interaction with robotic assistants in field construction work. In: Automation in Construction, v. 161 (May 2024). (2024):
- Proposal of a correction factor for predicting the thermal transmittance of building envelopes in existing buildings accounting for aging and environmental conditions. In: Building and Environment, v. 243 (September 2023). (2023):
- A comparative assessment of in-situ measurement methods for thermal resistance of building walls under mild climate conditions. In: Journal of Building Engineering, v. 77 (October 2023). (2023):
- A Comprehensive Evaluation of Factors Influencing Acceptance of Robotic Assistants in Field Construction Work. In: Journal of Management in Engineering (ASCE), v. 39, n. 3 (May 2023). (2023):
- Conditional Generative Adversarial Networks with Adversarial Attack and Defense for Generative Data Augmentation. In: Journal of Computing in Civil Engineering, v. 36, n. 3 (May 2022). (2022):
- Synthetic data generation using building information models. In: Automation in Construction, v. 130 (October 2021). (2021):
- Computer Vision–Based Estimation of Flood Depth in Flooded-Vehicle Images. In: Journal of Computing in Civil Engineering, v. 35, n. 2 (March 2021). (2021):
- Image augmentation to improve construction resource detection using generative adversarial networks, cut-and-paste, and image transformation techniques. In: Automation in Construction, v. 115 (July 2020). (2020):
- Encoder–decoder network for pixel‐level road crack detection in black‐box images. In: Computer-Aided Civil and Infrastructure Engineering, v. 34, n. 8 (March 2019). (2019):
- Image retrieval using BIM and features from pretrained VGG network for indoor localization. In: Building and Environment, v. 140 (August 2018). (2018):
- Vision-based nonintrusive context documentation for earthmoving productivity simulation. In: Automation in Construction, v. 102 (June 2019). (2019):
- Patch-Based Crack Detection in Black Box Images Using Convolutional Neural Networks. In: Journal of Computing in Civil Engineering, v. 33, n. 3 (May 2019). (2019):