Machine Learning Solutions for Bridge Scour Forecast Based on Monitoring Data

Bridge scour is a challenge throughout the U.S.A. and other countries. Despite the scale of the issue, there is still a substantial lack of robust methods for scour prediction to support reliable, risk-based management and decision making. Throughout the past decade, the use of real-time scour monitoring systems has gained increasing interest among state departments of transportation across the U.S.A. This paper introduces three distinct methodologies for scour prediction using advanced artificial intelligence (AI)/machine learning (ML) techniques based on real-time scour monitoring data. Scour monitoring data included the riverbed and river stage elevation time series at bridge piers gathered from various sources. Deep learning algorithms showed promising in prediction of bed elevation and water level variations as early as a week in advance. Ensemble neural networks proved successful in the predicting the maximum upcoming scour depth, using the observed sensor data at the onset of a scour episode, and based on bridge pier, flow and riverbed characteristics. In addition, two of the common empirical scour models were calibrated based on the observed sensor data using the Bayesian inference method, showing significant improvement in prediction accuracy. Overall, this paper introduces a novel approach for scour risk management by integrating emerging AI/ML algorithms with real-time monitoring systems for early scour forecast.

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A novel approach for phishing URLs detection using lexical based machine learning in a real-time environment

Computer Communications ◽

10.1016/j.comcom.2021.04.023 ◽

2021 ◽

Author(s):

Brij B. Gupta ◽

Krishna Yadav ◽

Imran Razzak ◽

Konstantinos Psannis ◽

Arcangelo Castiglione ◽

...

Keyword(s):

Machine Learning ◽

Real Time ◽

Novel Approach

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Using Machine Learning for Dependable Outlier Detection in Environmental Monitoring Systems

ACM Transactions on Cyber-Physical Systems ◽

10.1145/3445812 ◽

2021 ◽

Vol 5 (3) ◽

pp. 1-30

Author(s):

Gonçalo Jesus ◽

António Casimiro ◽

Anabela Oliveira

Keyword(s):

Machine Learning ◽

Environmental Monitoring ◽

Data Quality ◽

Outlier Detection ◽

Prediction Models ◽

Sensor Data ◽

Natural Phenomenon ◽

Monitoring Systems ◽

Data Errors ◽

Redundant Data

Sensor platforms used in environmental monitoring applications are often subject to harsh environmental conditions while monitoring complex phenomena. Therefore, designing dependable monitoring systems is challenging given the external disturbances affecting sensor measurements. Even the apparently simple task of outlier detection in sensor data becomes a hard problem, amplified by the difficulty in distinguishing true data errors due to sensor faults from deviations due to natural phenomenon, which look like data errors. Existing solutions for runtime outlier detection typically assume that the physical processes can be accurately modeled, or that outliers consist in large deviations that are easily detected and filtered by appropriate thresholds. Other solutions assume that it is possible to deploy multiple sensors providing redundant data to support voting-based techniques. In this article, we propose a new methodology for dependable runtime detection of outliers in environmental monitoring systems, aiming to increase data quality by treating them. We propose the use of machine learning techniques to model each sensor behavior, exploiting the existence of correlated data provided by other related sensors. Using these models, along with knowledge of processed past measurements, it is possible to obtain accurate estimations of the observed environment parameters and build failure detectors that use these estimations. When a failure is detected, these estimations also allow one to correct the erroneous measurements and hence improve the overall data quality. Our methodology not only allows one to distinguish truly abnormal measurements from deviations due to complex natural phenomena, but also allows the quantification of each measurement quality, which is relevant from a dependability perspective. We apply the methodology to real datasets from a complex aquatic monitoring system, measuring temperature and salinity parameters, through which we illustrate the process for building the machine learning prediction models using a technique based on Artificial Neural Networks, denoted ANNODE ( ANN Outlier Detection ). From this application, we also observe the effectiveness of our ANNODE approach for accurate outlier detection in harsh environments. Then we validate these positive results by comparing ANNODE with state-of-the-art solutions for outlier detection. The results show that ANNODE improves existing solutions regarding accuracy of outlier detection.

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Machine-Learning for the Prediction of Lost Circulation Events - Time Series Analysis and Model Evaluation

10.2118/204706-ms ◽

2021 ◽

Author(s):

Arturo Magana-Mora ◽

Mohammad AlJubran ◽

Jothibasu Ramasamy ◽

Mohammed AlBassam ◽

Chinthaka Gooneratne ◽

...

Keyword(s):

Machine Learning ◽

Time Series ◽

Real Time ◽

Large Scale ◽

Model Comparison ◽

Drilling Fluid ◽

Sensor Data ◽

False Alarms ◽

Suitable Model ◽

Lost Circulation

Abstract Objective/Scope. Lost circulation events (LCEs) are among the top causes for drilling nonproductive time (NPT). The presence of natural fractures and vugular formations causes loss of drilling fluid circulation. Drilling depleted zones with incorrect mud weights can also lead to drilling induced losses. LCEs can also develop into additional drilling hazards, such as stuck pipe incidents, kicks, and blowouts. An LCE is traditionally diagnosed only when there is a reduction in mud volume in mud pits in the case of moderate losses or reduction of mud column in the annulus in total losses. Using machine learning (ML) for predicting the presence of a loss zone and the estimation of fracture parameters ahead is very beneficial as it can immediately alert the drilling crew in order for them to take the required actions to mitigate or cure LCEs. Methods, Procedures, Process. Although different computational methods have been proposed for the prediction of LCEs, there is a need to further improve the models and reduce the number of false alarms. Robust and generalizable ML models require a sufficiently large amount of data that captures the different parameters and scenarios representing an LCE. For this, we derived a framework that automatically searches through historical data, locates LCEs, and extracts the surface drilling and rheology parameters surrounding such events. Results, Observations, and Conclusions. We derived different ML models utilizing various algorithms and evaluated them using the data-split technique at the level of wells to find the most suitable model for the prediction of an LCE. From the model comparison, random forest classifier achieved the best results and successfully predicted LCEs before they occurred. The developed LCE model is designed to be implemented in the real-time drilling portal as an aid to the drilling engineers and the rig crew to minimize or avoid NPT. Novel/Additive Information. The main contribution of this study is the analysis of real-time surface drilling parameters and sensor data to predict an LCE from a statistically representative number of wells. The large-scale analysis of several wells that appropriately describe the different conditions before an LCE is critical for avoiding model undertraining or lack of model generalization. Finally, we formulated the prediction of LCEs as a time-series problem and considered parameter trends to accurately determine the early signs of LCEs.

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Intelligent Environmental Monitoring System Based on Multi-Sensor Data Technology

International Journal of Ambient Computing and Intelligence ◽

10.4018/ijaci.2020100104 ◽

2020 ◽

Vol 11 (4) ◽

pp. 57-71

Author(s):

Qiuxia Liu

Keyword(s):

Data Fusion ◽

Environmental Monitoring ◽

Real Time ◽

Monitoring System ◽

Intelligent System ◽

Evidence Synthesis ◽

Environmental Parameters ◽

Monitoring Data ◽

Sensor Data ◽

Environmental Monitoring System

Using multi-sensor data fusion technology, ARM technology, ZigBee technology, GPRS, and other technologies, an intelligent environmental monitoring system is studied and developed. The SCM STC12C5A60S2 is used to collect the main environmental parameters in real time intelligently. The collected data is transmitted to the central controller LPC2138 through the ZigBee module ATZGB-780S5, and then the collected data is transmitted to the management computer through the GPRS communication module SIM300; thus, the real-time processing and intelligent monitoring of the environmental parameters are realized. The structure of the system is optimized; the suitable fusion model of environmental monitoring parameters is established; the hardware and the software of the intelligent system are completed. Each sensor is set up synchronously at the end of environmental parameter acquisition. The method of different value detection is used to filter out different values. The authors obtain the reliability of the sensor through the application of the analytic hierarchy process. In the analysis and processing of parameters, they proposed a new data fusion algorithm by using the reliability, probability association algorithm, and evidence synthesis algorithm. Through this algorithm, the accuracy of environmental monitoring data and the accuracy of judging monitoring data are greatly improved.

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Web-based real time bridge scour monitoring system for disaster management

The Baltic Journal of Road and Bridge Engineering ◽

10.3846/bjrbe.2014.03 ◽

2014 ◽

Vol 9 (1) ◽

pp. 17-25 ◽

Cited By ~ 2

Author(s):

Mirosław Skibniewski ◽

Hui-Ping Tserng ◽

Shen-Haw Ju ◽

Chung-Wei Feng ◽

Chih-Ting Lin ◽

...

Keyword(s):

Real Time ◽

Monitoring System ◽

Disaster Management ◽

Bridge Scour ◽

Web Based ◽

Scour Monitoring

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Using Bayesian networks for the assessment of underwater scour for road and railway bridges

Structural Health Monitoring ◽

10.1177/1475921720956579 ◽

2020 ◽

pp. 147592172095657

Author(s):

Andrea Maroni ◽

Enrico Tubaldi ◽

Dimitri V Val ◽

Hazel McDonald ◽

Daniele Zonta

Keyword(s):

Bayesian Networks ◽

Bayesian Network ◽

River Flow ◽

Flow Characteristics ◽

The United States ◽

Monitoring Systems ◽

Scour Depth ◽

Bridge Scour ◽

Railway Bridges ◽

Scour Monitoring

Flood-induced scour is among the most common external causes of bridge failures worldwide. In the United States, scour is the cause of 22 bridges fails every year, whereas in the UK, it contributed significantly to the 138 collapses of bridges in the last century. Scour assessments are currently based on visual inspections, which are time-consuming and expensive. Nowadays, sensor and communication technologies offer the possibility to assess in real time the scour depth at critical bridge locations; yet, monitoring an entire infrastructure network is not economically feasible. A way to overcome this limitation is to instal scour monitoring systems at critical bridge locations, and then extend the piece of information gained to the other assets exploiting the correlations present in the system. In this article, we propose a scour hazard model for road and railway bridge scour management that utilises information from a limited number of scour monitoring systems to achieve a more confined estimate of the scour risk for a bridge network. A Bayesian network is used to describe the conditional dependencies among the involved random variables and to update the scour depth distribution using data from monitoring of scour and river flow characteristics. This study constitutes the first application of Bayesian networks to bridge scour risk assessment. The proposed probabilistic framework is applied to a case study consisting of several road bridges in Scotland. The bridges cross the same river, and only one of them is instrumented with a scour monitoring system. It is demonstrated how the Bayesian network approach allows to significantly reduce the uncertainty in the scour depth at unmonitored bridges.

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LabelSens: enabling real-time sensor data labelling at the point of collection using an artificial intelligence-based approach

Personal and Ubiquitous Computing ◽

10.1007/s00779-020-01427-x ◽

2020 ◽

Vol 24 (5) ◽

pp. 709-722

Author(s):

Kieran Woodward ◽

Eiman Kanjo ◽

Andreas Oikonomou ◽

Alan Chamberlain

Keyword(s):

Machine Learning ◽

Data Collection ◽

Real Time ◽

High Performance ◽

Poor Performance ◽

Limited Range ◽

Performance Comparison ◽

Mobile App ◽

Sensor Data ◽

New Techniques

Abstract In recent years, machine learning has developed rapidly, enabling the development of applications with high levels of recognition accuracy relating to the use of speech and images. However, other types of data to which these models can be applied have not yet been explored as thoroughly. Labelling is an indispensable stage of data pre-processing that can be particularly challenging, especially when applied to single or multi-model real-time sensor data collection approaches. Currently, real-time sensor data labelling is an unwieldy process, with a limited range of tools available and poor performance characteristics, which can lead to the performance of the machine learning models being compromised. In this paper, we introduce new techniques for labelling at the point of collection coupled with a pilot study and a systematic performance comparison of two popular types of deep neural networks running on five custom built devices and a comparative mobile app (68.5–89% accuracy within-device GRU model, 92.8% highest LSTM model accuracy). These devices are designed to enable real-time labelling with various buttons, slide potentiometer and force sensors. This exploratory work illustrates several key features that inform the design of data collection tools that can help researchers select and apply appropriate labelling techniques to their work. We also identify common bottlenecks in each architecture and provide field tested guidelines to assist in building adaptive, high-performance edge solutions.

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Real-time TDR Field Bridge Scour Monitoring System

Structures Congress 2013 ◽

10.1061/9780784412848.262 ◽

2013 ◽

Cited By ~ 3

Author(s):

Junliang Tao ◽

Xinbao Yu ◽

Xiong (Bill) Yu

Keyword(s):

Real Time ◽

Monitoring System ◽

Bridge Scour ◽

Scour Monitoring

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Influenza forecasting for the French regions by using EHR, web and climatic data sources with an ensemble approach ARGONet

10.1101/19009795 ◽

2019 ◽

Cited By ~ 2

Author(s):

Canelle Poirier ◽

Yulin Hswen ◽

Guillaume Bouzillé ◽

Marc Cuggia ◽

Audrey Lavenu ◽

...

Keyword(s):

Public Health ◽

Machine Learning ◽

Disease Activity ◽

Real Time ◽

Data Sources ◽

Monitoring Systems ◽

Surveillance Systems ◽

Influenza Activity ◽

Public Health Officials ◽

The Right

AbstractEffective and timely disease surveillance systems have the potential to help public health officials design interventions to mitigate the effects of disease outbreaks. Currently, healthcare-based disease monitoring systems in France offer influenza activity information that lags real-time by 1 to 3 weeks. This temporal data gap introduces uncertainty that prevents public health officials from having a timely perspective on the population-level disease activity. Here, we present a machine-learning modeling approach that produces real-time estimates and short-term forecasts of influenza activity for the 12 continental regions of France by leveraging multiple disparate data sources that include, Google search activity, real-time and local weather information, flu-related Twitter micro-blogs, electronic health records data, and historical disease activity synchronicities across regions. Our results show that all data sources contribute to improving influenza surveillance and that machine-learning ensembles that combine all data sources lead to accurate and timely predictions.Author summaryThe role of public health is to protect the health of populations by providing the right intervention to the right population at the right time. In France and all around the world, Influenza is a major public health problem. Traditional surveillance systems produce estimates of influenza-like illness (ILI) incidence rates, but with one-to three-week delay. Accurate real-time monitoring systems of influenza outbreaks could be useful for public health decisions. By combining different data sources and different statistical models, we propose an accurate and timely forecasting platform to track the flu in France at a spatial resolution that, to our knowledge, has not been explored before.

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Monitoring systems for the assessment of water infrastructure hazards due to extreme climatic conditions

10.5194/egusphere-egu2020-10526 ◽

2020 ◽

Author(s):

Manousos Valyrakis ◽

Panagiotis Michalis ◽

Yi Xu ◽

Pablo Gaston Latessa

Keyword(s):

Real Time ◽

Climatic Conditions ◽

Bridge Scour ◽

Civil Infrastructure ◽

Water Infrastructure ◽

Monitoring Method ◽

Infrastructure Systems ◽

International Conference ◽

Scour Monitoring ◽

Climatic Hazards

Ageing infrastructure alongside with extreme climatic conditions pose a major threat for the sustainability of civil infrastructure systems with significant societal and economic impacts [1]. A main issue also arises from the fact that past and existing methods that incorporate the risk of climatic hazards into infrastructure design and assessment methods are based on historical records [2].Major flood incidents are the factor of evolving geomorphological processes, which cause a drastic reduction in the safe capacity of structures (e.g. bridges, dams). Many efforts focused on the development and application of monitoring techniques to provide real-time assessment of geomorphological conditions around structural elements [1, 3, 4]. However, the current qualitative visual inspection practice cannot provide reliable assessment of geomorphological effects at bridges and other water infrastructure.This work presents an analysis of the useful experience and lessons learnt from past monitoring efforts applied to assess geomorphological conditions at bridges and other types of water infrastructure. The main advantages and limitations of each monitoring method is summarized and compared, alongside with the key issues behind the failure of existing instrumentation to provide a solution. Finally, future directions on scour monitoring is presented focusing on latest advances in soil and remote sensing methods to provide modern and reliable alternatives for real-time monitoring and prediction [5, 6] of climatic hazards of infrastructure at risk.&#160;References[1] Michalis, P., Konstantinidis, F. and Valyrakis, M. (2019) The road towards Civil Infrastructure 4.0 for proactive asset management of critical infrastructure systems. Proceedings of the 2nd International Conference on Natural Hazards & Infrastructure (ICONHIC2019), Chania, Greece, 23&#8211;26 June 2019.[2] Pytharouli, S., Michalis, P. and Raftopoulos, S. (2019) From Theory to Field Evidence: Observations on the Evolution of the Settlements of an Earthfill Dam, over Long Time Scales. Infrastructures 2019, 4, 65.[3] Koursari, E., Wallace, S., Valyrakis, M. and Michalis, P. (2019). The need for real time and robust sensing of infrastructure risk due to extreme hydrologic events, 2019 UK/ China Emerging Technologies (UCET), Glasgow, United Kingdom, 2019, pp. 1-3.&#160;doi: 10.1109/UCET.2019.8881865[4] Michalis, P., Saafi, M. and M.D. Judd. (2012) Integrated Wireless Sensing Technology for Surveillance and Monitoring of Bridge Scour. Proceedings of the 6th International Conference on Scour and Erosion, France, Paris, pp. 395-402.[5] Valyrakis, M., Diplas, P., and Dancey, C.L. (2011) Prediction of coarse particle movement with adaptive neuro-fuzzy inference systems, Hydrological Processes, 25 (22). pp. 3513-3524. ISSN 0885-6087, doi:10.1002/hyp.8228.[6] Valyrakis, M., Michalis, P. and Zhang, H. (2015) A new system for bridge scour monitoring and prediction. Proceedings of the 36th IAHR World Congress, The Hague, the Netherlands, pp. 1-4.

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