Jia‐Rui Lin

Tsinghua University

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Co-Authors

Zhen‐Zhong Hu

Tsinghua University

Yucheng Zhou

Hangzhou Medical College

Xinzheng Lu

China University of Petroleum, Beijing

Jianping Zhang

East China Jiaotong University

Keyin Chen

Second Affiliated Hospital of Zhejiang University

Zhe Zheng

Moderna Therapeutics (United States)

Yichuan Deng

South China University of Technology

Zhe Zheng

China National Commission for Disaster Reduction

Xiang-Rui Ni

Tsinghua University

Zhe Zheng

Tianjin Normal University

Cooperative Institutions

Tsinghua University

Institute of Seismology

Chinese Academy of Sciences

Huazhong University of Science and Technology

Alexion Pharma (Switzerland)

Geomechanica (Canada)

Sun Yat-sen University

Shanghai Jiao Tong University

Nanchang University

Shantou University

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Parameterized FEA Model Generation and Simulation of Underground Structures Based on BIM: A Case Study

Computing in Civil Engineering (2022)

Sheng-Yu Song Jia‐Rui Lin Yucheng Zhou Wenqi Ding

Building information modeling (BIM) and finite element analysis (FEA) are widely used technologies for underground structure design; however, they failed to coordinate work well. Currently, existing BIM-to-FEA model conversion methods have a coarse conversion effect and a narrow scope of application. They do not consider the variational working conditions in the construction of underground structures, and thus can hardly be used in the simulations. To address this problem, this research proposes a semi-automatic BIM-to-FEA conversion method for construction simulation of underground structures based on Revit Dynamo and OpenSees software. The method is realized in three steps. Firstly, typical subway stations are selected as the research objects, and the parameterized BIM models are established. Secondly, based on Dynamo visual programming and tool command language (TCL) scripts, a BIM-to-FEA semi-automatic conversion method is formed to carry out FEA calculation. Finally, the method is extended for construction simulation. Based on the BIM model of the station's enveloping structure, an FEA model of diaphragm wall with multiple working conditions is automatically generated, and the structural simulation of the open-cut excavation process is conducted. This method provides an automatic BIM-to-FEA conversion way that can efficiently generate FEA construction simulation models with various working conditions and realizes a time-saving construction simulation framework based on BIM technology.

Solid modeling

10.1061/9780784483893.051

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Citations (1)

A Natural‐Language‐Based Approach to Intelligent Data Retrieval and Representation for Cloud BIM

Computer-Aided Civil and Infrastructure Engineering (2015)

Jia‐Rui Lin Zhen‐Zhong Hu Jianping Zhang Fangqiang Yu

Abstract As the information from diverse disciplines continues to integrate during the whole life cycle of an Architecture, Engineering, and Construction (AEC) project, the BIM (Building Information Model/Modeling) becomes increasingly large. This condition will cause users difficulty in acquiring the information they truly desire on a mobile device with limited space for interaction. The situation will be even worse for personnel without extensive knowledge of Industry Foundation Classes (IFC) or for nonexperts of the BIM software. To improve the value of the big data of BIM, an approach to intelligent data retrieval and representation for cloud BIM applications based on natural language processing was proposed. First, strategies for data storage and query acceleration based on the popular cloud‐based database were explored to handle the large amount of BIM data. Then, the concepts “keyword” and “constraint” were proposed to capture the key objects and their specifications in a natural‐language‐based sentence that expresses the requirements of the user. Keywords and constraints can be mapped to IFC entities or properties through the International Framework for Dictionaries (IFD). The relationship between the user's requirement and the IFC‐based data model was established by path finding in a graph generated from the IFC schema, enabling data retrieval and analysis. Finally, the analyzed and summarized results of BIM data were represented based on the structure of the retrieved data. A prototype application was developed to validate the proposed approach on the data collected during the construction of the terminal of Kunming Airport, the largest single building in China. The case study illustrated the following: (1) relationships between the user requirements and the data users concerned are established, (2) user‐concerned data can be automatically retrieved and aggregated based on the cloud for BIM, and (3) the data are represented in a proper form for a visual view and a comprehensive report. With this approach, users can significantly benefit from requesting for information and the value of BIM will be enhanced.

Building Information Modeling

Information model

Schema (genetic algorithms)

10.1111/mice.12151

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Citations (137)

DesignTracking: Track and Replay BIM-based Design Process

arXiv (Cornell University) (2023)

Xiang-Rui Ni Zhe Zheng Jia‐Rui Lin Zhen‐Zhong Hu Xin Zhang

Among different phases of the life cycle of a building or facility, design is of the utmost importance to ensure safety, efficiency and sustainability of the building or facility. How to control and improve design quality and efficiency has been explored for years, and more studies emerged with the popularization of Building Information Modelling (BIM). However, most of them focused on the extraction of design behaviors, while paying less attention to how a design is formed. Therefore, this study proposes an approach to tracking and replaying the BIM-based design process by integrating data logging and 4D visualization techniques. First of all, potential design behaviors and procedures are analyzed and extracted by observing how a designer designs a BIM model. Meanwhile, the required data for logging design process is defined and a relevant method to collect these data is developed based on the APIs of BIM software. Then, strategies on how to visualize different design procedures are designed and implemented via 4D visualization. Finally, a prototype system is developed based on Autodesk Revit and validated through a case study. Result shows that the proposed approach enables intuitively and interactively review of the design process, and makes it easier to understand design behaviors and even identify potential pitfalls, thus improving the design efficiency and quality.

Building Information Modeling

Design process

10.48550/arxiv.2305.10205

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Deep Learning-Based Instance Segmentation for Indoor Fire Load Recognition

IEEE Access (2021)

Yucheng Zhou Zhen‐Zhong Hu Ke-Xiao Yan Jia‐Rui Lin

Accurate fire load (combustible objects) information is crucial for safety design and resilience assessment of buildings. Traditional fire load acquisition methods, such as fire load survey, which are time-consuming, tedious, and error-prone, failed to adapt to dynamic changed indoor scenes. As a starting point of automatic fire load estimation, fast recognition and detection of indoor fire load are important. Thus, this research proposes a computer vision-based method to automatically detect indoor fire loads using deep learning-based instance segmentation. First, indoor elements are classified into different categories according to their material composition. Next, an image dataset of indoor scenes with instance annotations is developed. Finally, a deep learning model, based on Mask R-CNN, is developed and trained using transfer learning to detect fire loads in images. Experimental results show that our model achieves promising accuracy, as measured by an average precision (AP) of 40.5% and AP ₅₀ of 59.2%, for instance segmentation on the dataset. A comparison with manual detection demonstrates the method's high efficiency as it can detect fire load 1200 times faster than humans. This research contributes to the body of knowledge 1) a novel method of high accuracy and efficiency for automated fire load recognition in indoor environments based on instance segmentation; 2) training techniques for a deep learning model in a relatively small dataset of indoor images which includes complex scenes and a variety of instances; and 3) an image dataset with annotations of indoor fire loads. Although instance segmentation has been applied for several years, this is a pioneering research on using it for automated indoor fire load recognition, which paves the foundation for automatic fire load estimation and resilience assessment for the built environment.

10.1109/access.2021.3124831

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Citations (15)

Exploring the Feasibility of Automated Data Standardization using Large Language Models for Seamless Positioning

arXiv (Cornell University) (2024)

Max J. L. Lee Jia‐Rui Lin Li‐Ta Hsu

We propose a feasibility study for real-time automated data standardization leveraging Large Language Models (LLMs) to enhance seamless positioning systems in IoT environments. By integrating and standardizing heterogeneous sensor data from smartphones, IoT devices, and dedicated systems such as Ultra-Wideband (UWB), our study ensures data compatibility and improves positioning accuracy using the Extended Kalman Filter (EKF). The core components include the Intelligent Data Standardization Module (IDSM), which employs a fine-tuned LLM to convert varied sensor data into a standardized format, and the Transformation Rule Generation Module (TRGM), which automates the creation of transformation rules and scripts for ongoing data standardization. Evaluated in real-time environments, our study demonstrates adaptability and scalability, enhancing operational efficiency and accuracy in seamless navigation. This study underscores the potential of advanced LLMs in overcoming sensor data integration complexities, paving the way for more scalable and precise IoT navigation solutions.

10.48550/arxiv.2408.12080

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SODA: A large-scale open site object detection dataset for deep learning in construction

Automation in Construction (2022)

Rui Duan Hui Deng Mao Tian Yichuan Deng Jia‐Rui Lin

Benchmark (surveying)

10.1016/j.autcon.2022.104499

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Citations (94)

A Text Classification-Based Approach for Evaluating and Enhancing the Machine Interpretability of Building Codes

arXiv (Cornell University) (2023)

Zhe Zheng Yucheng Zhou Keyin Chen Xinzheng Lu Zhong-Tian She

Interpreting regulatory documents or building codes into computer-processable formats is essential for the intelligent design and construction of buildings and infrastructures. Although automated rule interpretation (ARI) methods have been investigated for years, most of them highly depend on the early and manual filtering of interpretable clauses from a building code. While few of them considered machine interpretability, which represents the potential to be transformed into a computer-processable format, from both clause- and document-level. Therefore, this research aims to propose a novel approach to automatically evaluate and enhance the machine interpretability of single clause and building codes. First, a few categories are introduced to classify each clause in a building code considering the requirements for rule interpretation, and a dataset is developed for model training. Then, an efficient text classification model is developed based on a pretrained domain-specific language model and transfer learning techniques. Finally, a quantitative evaluation method is proposed to assess the overall interpretability of building codes. Experiments show that the proposed text classification algorithm outperforms the existing CNN- or RNN-based methods, improving the F1-score from 72.16% to 93.60%. It is also illustrated that the proposed classification method can enhance downstream ARI methods with an improvement of 4%. Furthermore, analyzing the results of more than 150 building codes in China showed that their average interpretability is 34.40%, which implies that it is still hard to fully transform the entire regulatory document into computer-processable formats. It is also argued that the interpretability of building codes should be further improved both from the human side and the machine side.

Interpretability

Code (set theory)

10.48550/arxiv.2309.14374

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A New Method on System Reliability Analysis based on Survival Signature Theory

Yide Zheng Yi Zhang Jia‐Rui Lin

Reliability theory

Signature (topology)

10.3850/978-981-18-5184-1_ms-14-136-cd

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