Signal Processing for Sensing and Monitoring of Civil Infrastructure Systems

2012 ◽  
pp. 267-304 ◽  
Author(s):  
Mustafa Gul ◽  
F. Necati Catbas
Keyword(s):  
Signal Processing ◽  
Civil Infrastructure ◽  
Infrastructure Systems

scholarly journals Robotic Sensing and Systems for Smart Cities

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2963
Author(s):  
Hyun Myung ◽  
Yang Wang
Keyword(s):  
Smart Cities ◽  
Civil Infrastructure ◽  
Infrastructure Systems ◽  
Sensing Systems ◽  
Robotic Sensing

For several decades, various sensors and sensing systems have been developed for smart cities and civil infrastructure systems [...]

Nano Science and Engineering in Mechanics and Materials

Materials ◽  
2003 ◽  
Author(s):  
Ken P. Chong
Keyword(s):  
Information Technology ◽  
New Economy ◽  
Essential Elements ◽  
First Century ◽  
Civil Infrastructure ◽  
Science And Engineering ◽  
Infrastructure Systems ◽  
Research Opportunities ◽  
Carbon Nano Tubes ◽  
Nano Science

The transcendent technologies include nanotechnology, microelectronics, information technology and biotechnology as well as the enabling and supporting civil infrastructure systems and materials. These technologies are the primary drivers of the twenty first century and the new economy. Mechanics and materials are essential elements in all of the transcendent technologies. Research opportunities, education and challenges in mechanics and materials, including nanomechanics, carbon nano-tubes, bio-inspired materials, coatings, fire-resistant materials as well as improved engineering and design of materials are presented and discussed in this paper.

scholarly journals Application of Machine Learning with Impedance Based Techniques for Structural Health Monitoring of Civil Infrastructure

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1139-1148
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Soft Computing ◽  
Health Monitoring ◽  
Multiple Cracks ◽  
Civil Infrastructure ◽  
Structural Health ◽  
Soft Computing Techniques

Increased attentiveness on the environmental and effects of aging, deterioration and extreme events on civil infrastructure has created the need for more advanced damage detection tools and structural health monitoring (SHM). Today, these tasks are performed by signal processing, visual inspection techniques along with traditional well known impedance based health monitoring EMI technique. New research areas have been explored that improves damage detection at incipient stage and when the damage is substantial. Addressing these issues at early age prevents catastrophe situation for the safety of human lives. To improve the existing damage detection newly developed techniques in conjugation with EMI innovative new sensors, signal processing and soft computing techniques are discussed in details this paper. The advanced techniques (soft computing, signal processing, visual based, embedded IOT) are employed as a global method in prediction, to identify, locate, optimize, the damage area and deterioration. The amount and severity, multiple cracks on civil infrastructure like concrete and RC structures (beams and bridges) using above techniques along with EMI technique and use of PZT transducer. In addition to survey advanced innovative signal processing, machine learning techniques civil infrastructure connected to IOT that can make infrastructure smart and increases its efficiency that is aimed at socioeconomic, environmental and sustainable development.

Knowledge Management in Civil Infrastructure Systems

2011 ◽  
pp. 286-292
Author(s):  
Hyung Seok Jeong ◽  
Dolphy M. Abraham ◽  
Dulcy M. Abraham
Keyword(s):  
Knowledge Management ◽  
Social Issues ◽  
Modern Society ◽  
Well Being ◽  
Data Availability ◽  
Civil Infrastructure ◽  
Research And Practice ◽  
Infrastructure Systems ◽  
Conflicting Objectives ◽  
Oil Gas

This article reviews current research and practice of knowledge management (KM) in the management of Civil infrastructure systems. Civil infrastructure systems, such as energy systems (electric power, oil, gas), telecommunications, and water supply, are critical to our modern society. The economic prosperity and social well being of a country is jeopardized when these systems are damaged, disrupted, or unable to function at adequate capacity. The management of these infrastructure systems has to take into account critical management issues such as (Lemer, Chong & Tumay, 1995): • the need to deal with multiple, often conflicting objectives; • the need to accommodate the interests of diverse stakeholders; • the reliance of decision making on uncertain economic and social issues; • the constraints in data availability; and • the limitations posed by institutional structure.

scholarly journals Spatially dependent flood probabilities to support the design of civil infrastructure systems

2019 ◽  
Vol 23 (11) ◽  
pp. 4851-4867 ◽  
Author(s):  
Phuong Dong Le ◽  
Michael Leonard ◽  
Seth Westra
Keyword(s):  
Flood Risk ◽  
Integrated Approach ◽  
Design Flow ◽  
Civil Infrastructure ◽  
Infrastructure Systems ◽  
Railway Infrastructure ◽  
Single Location ◽  
System Risk ◽  
Intensity Duration Frequency ◽  
Evacuation Routes

Abstract. Conventional flood risk methods typically focus on estimation at a single location, which can be inadequate for civil infrastructure systems such as road or railway infrastructure. This is because rainfall extremes are spatially dependent; to understand overall system risk, it is necessary to assess the interconnected elements of the system jointly. For example, when designing evacuation routes it is necessary to understand the risk of one part of the system failing given that another region is flooded or exceeds the level at which evacuation becomes necessary. Similarly, failure of any single part of a road section (e.g., a flooded river crossing) may lead to the wider system's failure (i.e., the entire road becomes inoperable). This study demonstrates a spatially dependent intensity–duration–frequency (IDF) framework that can be used to estimate flood risk across multiple catchments, accounting for dependence both in space and across different critical storm durations. The framework is demonstrated via a case study of a highway upgrade comprising five river crossings. The results show substantial differences in conditional and unconditional design flow estimates, highlighting the importance of taking an integrated approach. There is also a reduction in the estimated failure probability of the overall system compared with the case where each river crossing is treated independently. The results demonstrate the potential uses of spatially dependent intensity–duration–frequency methods and suggest the need for more conservative design estimates to take into account conditional risks.

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