@article{xiong2024calibrating,title={Calibrating subjective data biases and model predictive uncertainties in machine learning-based thermal perception predictions},author={Xiong, Ruoxin and Shi, Ying and Jing, Haoming and Liang, Wei and Nakahira, Yorie and Tang, Pingbo},journal={Building and Environment},volume={247},pages={111053},year={2024},publisher={Elsevier},doi={https://doi.org/10.1016/j.buildenv.2023.111053}}
2023
Fast and reliable map matching from large-scale noisy positioning records
Yanyu Wang , Ruoxin Xiong , Pingbo Tang , and Yongming Liu
@article{wang2023fast,title={Fast and reliable map matching from large-scale noisy positioning records},author={Wang, Yanyu and Xiong, Ruoxin and Tang, Pingbo and Liu, Yongming},journal={Journal of Computing in Civil Engineering},volume={37},number={1},pages={04022040},year={2023},publisher={American Society of Civil Engineers},doi={https://doi.org/10.1061/(ASCE)CP.1943-5487.0001054}}
Dynamic human systems risk prognosis and control of lifting operations during prefabricated building construction
Zhe Sun , Zhufu Zhu , Ruoxin Xiong , Pingbo Tang , and Zhansheng Liu
@article{sun2023dynamic,title={Dynamic human systems risk prognosis and control of lifting operations during prefabricated building construction},author={Sun, Zhe and Zhu, Zhufu and Xiong, Ruoxin and Tang, Pingbo and Liu, Zhansheng},journal={Developments in the Built Environment},pages={100143},year={2023},publisher={Elsevier},doi={https://doi.org/10.1016/j.dibe.2023.100143}}
Quantifying the reliability of defects located by bridge inspectors through human observation behavioral analysis
Pengkun Liu , Ying Shi , Ruoxin Xiong , and Pingbo Tang
@article{liu2023quantifying,title={Quantifying the reliability of defects located by bridge inspectors through human observation behavioral analysis},author={Liu, Pengkun and Shi, Ying and Xiong, Ruoxin and Tang, Pingbo},journal={Developments in the Built Environment},volume={14},pages={100167},year={2023},publisher={Elsevier},doi={https://doi.org/10.1016/j.dibe.2023.100167}}
Predicting separation errors of air traffic controllers through integrated sequence analysis of multimodal behaviour indicators
Ruoxin Xiong , Yanyu Wang , Pingbo Tang , Nancy J Cooke , Sarah V Ligda , Christopher S Lieber , and Yongming Liu
@article{xiong2023predicting,title={Predicting separation errors of air traffic controllers through integrated sequence analysis of multimodal behaviour indicators},author={Xiong, Ruoxin and Wang, Yanyu and Tang, Pingbo and Cooke, Nancy J and Ligda, Sarah V and Lieber, Christopher S and Liu, Yongming},journal={Advanced Engineering Informatics},volume={55},pages={101894},year={2023},publisher={Elsevier},doi={https://doi.org/10.1016/j.aei.2023.101894},}
2022
A semantic embedding methodology for motor vehicle crash records: A case study of traffic safety in Manhattan Borough of New York City
Yuxuan Wang , Ruoxin Xiong , Hao Yu , Jie Bao , and Zhao Yang
@article{wang2022semantic,title={A semantic embedding methodology for motor vehicle crash records: A case study of traffic safety in Manhattan Borough of New York City},author={Wang, Yuxuan and Xiong, Ruoxin and Yu, Hao and Bao, Jie and Yang, Zhao},journal={Journal of Transportation Safety \& Security},volume={14},number={11},pages={1913--1933},year={2022},publisher={Taylor \& Francis},doi={https://doi.org/10.1080/19439962.2021.1994681}}
2021
Pose guided anchoring for detecting proper use of personal protective equipment
@article{xiong2021pose,title={Pose guided anchoring for detecting proper use of personal protective equipment},author={Xiong, Ruoxin and Tang, Pingbo},volume={130},pages={103828},year={2021},publisher={Elsevier},doi={https://doi.org/10.1016/j.autcon.2021.103828},}
Machine learning using synthetic images for detecting dust emissions on construction sites
Ruoxin Xiong , and Pingbo Tang
Smart and Sustainable Built Environment. Invited paper from the CONVR 2020 , 2021
@article{xiong2021machine,title={Machine learning using synthetic images for detecting dust emissions on construction sites},author={Xiong, Ruoxin and Tang, Pingbo},journal={Smart and Sustainable Built Environment},volume={10},number={3},pages={487--503},year={2021},publisher={Emerald Publishing Limited},doi={https://doi.org/10.1108/SASBE-04-2021-0066},}
2019
Onsite video mining for construction hazards identification with visual relationships
Ruoxin Xiong , Yuanbin Song , Heng Li , and Yuxuan Wang
Widely-used video monitoring systems provide a large corpus of unstructured image data on construction sites. Although previous developed vision-based approaches can be used for hazards recognition in terms of detecting dangerous objects or unsafe operations, such detection capacity is often limited due to lack of semantic representation of visual relationships between/among the components or crews in the workplace. Accordingly, the formal representation of textural criteria for checking improper relationships should also be improved. In this regard, an Automated Hazards Identification System (AHIS) is developed to evaluate the operation descriptions generated from site videos against the safety guidelines extracted from the textual documents with the assistance of the ontology of construction safety. In particular, visual relationships are modeled as a connector between site components/operators. Moreover, both visual descriptions of site operations and semantic representations of safety guidelines are coded in the three-tuple format and then automatically converted into Horn clauses for reasoning out the potential risks. A preliminary implementation of the system was tested on two separate onsite video clips. The results showed that two types of crucial hazards, i.e., failure to wear a helmet and walking beneath the cane, were successfully identified with three rules from Safety Handbook for Construction Site Workers. In addition, the high-performance results of Recall@50 and Recall@100 demonstrated that the proposed visual relationship detection method is promising in enriching the semantic representation of operation facts extracted from site videos, which may lead to better automation in the detection of construction hazards.
@article{xiong2019onsite,title={Onsite video mining for construction hazards identification with visual relationships},author={Xiong, Ruoxin and Song, Yuanbin and Li, Heng and Wang, Yuxuan},journal={Advanced Engineering Informatics},volume={42},pages={100966},year={2019},publisher={Elsevier},doi={https://doi.org/10.1016/j.aei.2019.100966}}
Pose guided anchoring for detecting proper use of personal protective equipment. More Information can be found here , 2021
Machine learning using synthetic images for detecting dust emissions on construction sitesSmart and Sustainable Built Environment. Invited paper from the CONVR 2020 , 2021
