• Computers and Concrete
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• v.33 no.4
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• pp.341-348
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• 2024

Ultra-high-performance concrete (UHPC) has received remarkable attentions in civil infrastructure due to its unique mechanical characteristics and durability. UHPC gains increasingly dominant in essential structural elements, while its unique properties pose challenges for traditional inspection methods, as damage may not always manifest visibly on the surface. As such, the need for robust inspection techniques for detecting cracks in UHPC members has become imperative as traditional methods often fall short in providing comprehensive and timely evaluations. In the era of artificial intelligence, computer vision has gained considerable interest as a powerful tool to enhance infrastructure condition assessment with image and video data collected from sensors, cameras, and unmanned aerial vehicles. This paper presents a computer vision-based approach employing deep learning to detect cracks in UHPC beams, with the aim of addressing the inherent limitations of traditional inspection methods. This work leverages computer vision to discern intricate patterns and anomalies. Particularly, a convolutional neural network architecture employing transfer learning is adopted to identify the presence of cracks in the beams. The proposed approach is evaluated with image data collected from full-scale experiments conducted on UHPC beams subjected to flexural and shear loadings. The results of this study indicate the applicability of computer vision and deep learning as intelligent methods to detect major and minor cracks and recognize various damage mechanisms in UHPC members with better efficiency compared to conventional monitoring methods. Findings from this work pave the way for the development of autonomous infrastructure health monitoring and condition assessment, ensuring early detection in response to evolving structural challenges. By leveraging computer vision, this paper contributes to usher in a new era of effectiveness in autonomous crack detection, enhancing the resilience and sustainability of UHPC civil infrastructure.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.29 no.4
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• pp.327-334
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• 2016

This study proposed a framework for an infrastructure asset management manual which can be adjusted by different ordering authorities to develop their own manuals. For this, The necessity of asset management manual was examined through analysis of the current status and insufficiencies and limitations in the asset management manuals of the domestic government and ordering authorities. Second, the systems and characteristics of infrastructure asset management manuals in developed countries such as Australia, the United Kingdom and the United States were examined and compared. Finally, based on the domestic infrastructure asset management characteristics and foreign infrastructure asset management manuals, a framework for an infrastructure asset management manual that can be utilized by the ordering authorities was proposed considering generality of asset management manual, asset management maturity of ordering authorities, serviceability of manual, and cyclic processes of asset management.

• Ecology and Resilient Infrastructure
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• v.3 no.4
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• pp.213-214
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• 2016

• Water for future
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• v.56 no.1
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• pp.50-58
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• 2023

• International conference on construction engineering and project management
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• 2022.06a
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• pp.83-90
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• 2022

Public-private partnerships (PPP) projects are becoming popular in both developed and developing countries due to their ability to access new financing sources and transfer certain project risks to the private sector. PPP has been an active research area where the concept of Critical Success Factors (CSF) is often discussed by researchers. This study aims to identify the CSFs for various PPP infrastructure projects that have been explored in previous CSF studies. This article reviewed the literature about CSF in PPP projects from the years 2002 to 2021, compared the findings of studies regarding the identified CSFs, and consolidated the CSFs that can be applied to various PPP infrastructure projects. The results showed that dominant research focused on general infrastructure, where CSFs can be applied to all infrastructure sectors rather than any specific sector. The most identified CSFs from the study are favorable and efficient legal frameworks, appropriate risk allocation and sharing, a robust and reliable private consortium, a competitive and transparent procurement process, and political support and stability. The findings from the study can provide an overview of CSFs that are relevant to specific PPP infrastructure sectors like building infrastructure, transportation, water, etc. as well as for general infrastructure. In addition, the results can also be used for further empirical analysis.

• Smart Structures and Systems
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• v.23 no.6
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• pp.579-587
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• 2019

In this paper, pounding tuned mass dampers (PTMDs) were designed to mitigate the multi-mode vortex-induced vibration (VIV) of stay cable utilizing the viscous-elastic material's energy-dissipated ability. The PTMD device consists of a cantilever metal rod beam, a metal mass block and a specially designed damping element covered with viscous-elastic material layer. Wind-tunnel experiment on VIV of stay cable model was set up to validate the effectiveness of the PTMD on multi-mode VIV mitigation of stay cable. By analyzing and comparing testing results of all testing cases, it could be verified that the PTMD with viscous-elastic pounding boundary can obviously mitigate the VIV amplitude of the stay cable. Moreover, the installed location and the design parameters of the PTMD device based on the controlled modes of the primary stay cable, would have a certain extent suppression on the other modal vibration of the stay cable, which means that the designed PTMDs are effective among a large band of frequency for the multi-mode VIV control of the stay cable.

• Journal of Construction Engineering and Project Management
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• v.2 no.1
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• pp.27-34
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• 2012

Recent requirements in component management of building systems have focused on the requirement for improving methods and metric tools to support component condition assessment and appropriate decisions for infrastructure owned facilities. Although engineers and researchers have focused on developing methodologies for component assessment in recent years but there is not enough attention dedicate to facilities and components that have been constructed. This paper is a literature study of scientific papers within the topic of component condition index system (CCIS) in the period 1976 to 2009. Infrastructure component condition index had existed for some 40 years. The purpose of this paper is to provide an overview of CCIS to identify the suitable method for component condition assessment during its service life. This paper finds that the focus of CCIS, surveyed in several aspects during the 40 years that have been investigated, from technology to measurement and from assessment function to component maintenance as an integrated part of the infrastructure component management. This study offers help to researchers in understanding the selection of an appropriate method for component condition assessment in building and non-building systems.

• Smart Structures and Systems
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• v.17 no.4
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• pp.669-690
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• 2016

Advances in sensor technologies have led to the instrumentation of sensor networks for bridge monitoring and management. For a dense sensor network, enormous amount of sensor data are collected. The data need to be managed, processed, and interpreted. Data management issues are of prime importance for a bridge management system. This paper describes a data management infrastructure for bridge monitoring applications. Specifically, NoSQL database systems such as MongoDB and Apache Cassandra are employed to handle time-series data as well the unstructured bridge information model data. Standard XML-based modeling languages such as OpenBrIM and SensorML are adopted to manage semantically meaningful data and to support interoperability. Data interoperability and integration among different components of a bridge monitoring system that includes on-site computers, a central server, local computing platforms, and mobile devices are illustrated. The data management framework is demonstrated using the data collected from the wireless sensor network installed on the Telegraph Road Bridge, Monroe, MI.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.725-730
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• 2007

A framework of multi layered database is proposed for the integrated operation of civil infrastructure information in this study. The multi-layered database is a logically integrated database composed of standardized information layers. The framework of multi-layered database is defined by three axes, national assets, lifetime, and data levels. The axis of national assets indicates civil infrastructures such as bridges, dams, tunnels and power plants that can be considered as national key structures. The axes of lifetime and data levels indicate the standardized information layers generated from the life-phase of civil infrastructure and the priority of data in the information layers, respectively. The standardized information layers are basically composed of reusable data sets defined by information models. A prototype of standard database for steel bridges is constructed based on the framework as a proof of concept. Demonstration examples such as data consistency check and automatic generation of a FEA model show that the proposed concept can assure the sustainable interoperability of civil infrastructure information as well as design information of steel bridges.

• Smart Structures and Systems
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• v.30 no.5
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• pp.501-511
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• 2022

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced; the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.