Mapping textual descriptions to condition ratings to assist bridge inspection and condition assessment using hierarchical attention

Li, Tianshu; Alipour, Mohamad; Harris, Devin K.

doi:10.1016/j.autcon.2021.103801

Cited by 13 publications

(5 citation statements)

References 32 publications

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“…While inspection methods vary from jurisdiction to jurisdiction, traditional infrastructure inspection methods rely primarily on manual techniques to collect infrastructure damage data, analyze the data, and prioritize maintenance efforts [41]. These methods are subjective, time-consuming, costly, and potentially dangerous [68][69][70][71][72][73][74]. Manual inspection techniques require individuals from the project team or third-party inspectors to physically visit the site (i.e., bridges, roads) and examine each element of the infrastructure, searching for and identifying visible damage [44].…”

Section: Virtual Inspectionmentioning

confidence: 99%

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Lifecycle Applications of Building Information Modeling for Transportation Infrastructure Projects

Belcher,

Abraham

2023

Buildings

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Infrastructure plays a pivotal role in a nation’s economic stability, security, and long-term sustainability. For decades, United States infrastructure has been rated as inadequate and the resources required for its restoration continue to grow. Despite the transformative potential of Building Information Modeling (BIM) coupled with digital technologies in revolutionizing the building industry, its widespread adoption within infrastructure projects remains limited. This study explores the application of BIM and emerging technologies in transportation projects’ lifecycle, identifies research gaps, and discusses the use of technology to address current infrastructure challenges. Employing a systematic review approach, this research synthesizes BIM application studies based on well-defined criteria. The synthesis of the literature underscores the comprehensive integration of BIM throughout the lifecycle of transportation assets, catalyzed by collaborative, information-driven processes. However, challenges pertaining to interoperability, role delineation, standards enforcement, and cost persist. This study advocates for a deeper exploration of the synergy between BIM, digital technologies, and infrastructure, envisioning an enriched transportation lifecycle in the future.

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Section: Virtual Inspectionmentioning

confidence: 99%

“…These opportunities allowed decision-makers to prioritize spending based on predictive analytics and promoted planned maintenance schedules. Other applications include structural damage identification [71], condition assessment [74], and bridge inspection [69].…”

Section: Big Datamentioning

confidence: 99%

Lifecycle Applications of Building Information Modeling for Transportation Infrastructure Projects

Belcher,

Abraham

2023

Buildings

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“…In the QA system of building regulations proposed by Zhong et al [ 39 ], the BERT pretraining model is used for feature extraction questions. Li et al [ 40 ] used narrative descriptions in bridge inspection reports as data sources and proposed a data-driven framework to support automatic condition recommendation and real-time quality control. Xia et al [ 41 ] proposed a data-driven bridge condition assessment framework to effectively predict the future condition of bridges.…”

Section: Related Workmentioning

confidence: 99%

Complex Knowledge Base Question Answering for Intelligent Bridge Management Based on Multi-Task Learning and Cross-Task Constraints

Yang

et al. 2022

Entropy

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In the process of bridge management, large amounts of domain information are accumulated, such as basic attributes, structural defects, technical conditions, etc. However, the valuable information is not fully utilized, resulting in insufficient knowledge service in the field of bridge management. To tackle these problems, this paper proposes a complex knowledge base question answering (C-KBQA) framework for intelligent bridge management based on multi-task learning (MTL) and cross-task constraints (CTC). First, with C-KBQA as the main task, part-of-speech (POS) tagging, topic entity extraction (TEE), and question classification (QC) as auxiliary tasks, an MTL framework is built by sharing encoders and parameters, thereby effectively avoiding the error propagation problem of the pipeline model. Second, cross-task semantic constraints are provided for different subtasks via POS embeddings, entity embeddings, and question-type embeddings. Finally, using template matching, relevant query statements are generated and interaction with the knowledge base is established. The experimental results show that the proposed model outperforms compared mainstream models in terms of TEE and QC on bridge management datasets, and its performance in C-KBQA is outstanding.

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“…In addition to these studies, a new body of research using bridge inspection data from databases such as the National Bridge Inventory (NBI) database and the Korean Bridge Management System (KOBMS) has begun to emerge [7,[15][16][17][18][19][20]. The objectives of these studies can be generally categorized into two: predictive and informative (or descriptive) [21].…”

Section: Introductionmentioning

confidence: 99%

“…The objective of predictive data mining studies is to solve a specific problem by forecasting the future values of one or more database fields using data from other fields [4]. An example of such studies includes the work of Li et al (2021) wherein they used the descriptive text from more than 8000 inspection reports to develop a neural network model aimed at promoting consistency and reliability in bridge condition assessment among inspectors [19]. Xia et al (2021) developed a fully data-driven, regionallevel bridge maintenance management framework using data extracted from inspection reports [22].…”

Section: Introductionmentioning

confidence: 99%

A Big Data Approach for Investigating Bridge Deterioration and Maintenance Strategies in Taiwan

2023

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Due to the dwindling maintenance budget and lack of qualified bridge inspectors, bridge-management agencies in Taiwan need to develop cost-effective maintenance and inspection strategies to preserve the safety and functionality of their aging, natural disaster-prone bridges. To inform the development of such a strategy, this study examined the big data stored in the Taiwan Bridge Management System (TBMS) using the knowledge discovery in databases (KDD) process. Cluster and association algorithms were applied to the inventory and five-year inspection data of 2849 bridges to determine the bridge structural configurations and components that are prone to deterioration. Bridge maintenance agencies can use the results presented to reevaluate their current maintenance and inspection strategies and concentrate their limited resources on bridges and components most prone to deterioration.

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Mapping textual descriptions to condition ratings to assist bridge inspection and condition assessment using hierarchical attention

Cited by 13 publications

References 32 publications

Lifecycle Applications of Building Information Modeling for Transportation Infrastructure Projects

Lifecycle Applications of Building Information Modeling for Transportation Infrastructure Projects

Complex Knowledge Base Question Answering for Intelligent Bridge Management Based on Multi-Task Learning and Cross-Task Constraints

A Big Data Approach for Investigating Bridge Deterioration and Maintenance Strategies in Taiwan

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