Project Abstracts, Spring Semester 2022

[ Project 1 ]: Prediction of Bridge Condition Rating using Neural Networks
[ Project 2 ]: Machine Learning Algorithms to Predict the Strength of Concrete
[ Project 3 ]: Short-term Freeway Speed Prediction: A Spatio-Temporal LSTM Model
[ Project 4 ]: Impact of Hurricanes on Eviction Rates
[ Project 5 ]: Predicting Power Output from Solar Panel Data Across the U.S.
[ Project 6 ]: Predicting COVID-19 Vaccination Levels of ZCTAs Using Socioeconomic and Demographic Data
[ Project 7 ]: Estimation of Pedestrian Volume for Brooklyn Bridge
[ Project 8 ]: Solving Plate Buckling Problems with Neural Networks

PROJECT 1

Title: Prediction of Bridge Condition Rating using Neural Networks
Authors: Ainur Abilbayeva, and Naiyi Li

Abstract: Deterioration of bridge structures plays an essential role in maintaining the functionality of transportation networks as aging infrastructure becomes more prevalent. Due to the complexity of bridge deterioration, prediction models derived using analytical methods struggle to provide accurate prediction of the deterioration process.

This study utilizes neural networks to perform prediction on the condition rating of the superstructure and substructures of bridges. The dataset is obtained from two sources:

  1. The National Bridge Inventory (NBI) for bridge information from 1992 to 2020.
  2. The Storm Events Database from the National Oceanic and Atmospheric Administration (NOAA) is added to incorporate the effects of extreme weather events on the condition rating of the bridges. The events are primarily storms and floods.

This study aims to investigate the combined effects of natural and structural impacts on the deterioration of bridge structures. In theory, the NBI dataset would reflect the natural deterioration rate of bridges and the Storm Events Database may indicate sudden drops in bridge condition ratings due to extreme weather events, for instance, scour due to floods.

Figure 1a shows the bridge records in each condition states (CS1: best ~ CS4: worst).

Figure 1a. ...

Table 1 shows a snippet of the NBI dataset.

Table 1a. ...

Figure 1b shows the survival analysis using Kaplan–Meier estimator, which can be used as a reference to the neural network.

Figure 1b. Survival analysis using Kaplan-Meier estimator.

Data Sources

References

  1. Cavalline, T. L., Whelan, M. J., Tempest, B. Q., Goyal, R., & Ramsey, J.D. (2016).
    Determination of Bridge Deterioration Models and Bridge User Costs for the NCDOT Bridge Management System.
  2. Goyal, R. (2015). Development of a Survival-based Framework for Bridge Deterioration
    Modeling with Large-Scale Application to the North Carolina Bridge Management System.
  3. Hosmer, D. W., Lemeshow, S., & May, S. (2008). Applied Survival Analysis:
    Regression Modeling of Time-to-event Data. John Wiley & Sons.
  4. Madanat, S., Mishalani, R., & Ibrahim, W. H. (1995).
    Estimation of infrastructure transition probabilities from condition rating data.
    Journal of Infrastructure Systems, 1(2), 120–125. https://doi.org/10.1061/(asce)1076-0342(1995)1:2(120)
  5. U.S. Department of Transportation Federal Highway Administration. (n.d.).
    LTBP InfoBridgeTM. Long-Term Bridge Performance (LTBP). https://infobridge.fhwa.dot.gov/.
  6. Snaiki, R., Wu, T., Whittaker, A.S., & Atkinson, J.F. (2020).
    Hurricane Wind and storm surge effects on coastal bridges under a changing climate.
    Transportation Research Record: Journal of the Transportation Research Board, 2674(6), 23–32. https://doi.org/10.1177/0361198120917671
  7. Lee, J.-W., Irish, J., Bensi, M., & Marcy, D. (2021).
    Rapid prediction of peak storm surge from tropical cyclone
    track time series using Machine Learning. https://doi.org/10.31223/x5ns6h

PROJECT 2

Title: Machine Learning Algorithms to Predict the Strength of Concrete
Author: Jing Jing

Abstract: Concrete is one of the important building materials in construction -- it is widely used all over the world. Although we have a mature way to calculate its strength in order to evaluate its performance, this project aims at finding another way using machine learning algorithms to predict its strength by using its parameter data. What’s more, compare to the traditional way, we also want to get to know its strength after use it for years so that we can have a new way to monitor concrete component’s health.

This project will investigate the use of linear regression and multi-layer perceptron regression models to predict concrete strength.

Figure 2a. Parameters for linear regression model.

Data Sources

References

  1. I-Cheng Yeh, Modeling of strength of high performance concrete using artificial neural networks,
    Cement and Concrete Research, Vol. 28, No. 12, pp. 1797-1808 (1998)
  2. I-Cheng Yeh, Modeling Concrete Strength with Augment-Neuron Networks,
    J. of Materials in Civil Engineering, ASCE, Vol. 10, No. 4, pp. 263-268 (1998).
  3. I-Cheng Yeh, Design of High Performance Concrete Mixture Using Neural Networks,
    J. of Computing in Civil Engineering, ASCE, Vol. 13, No. 1, pp. 36-42 (1999).
  4. I-Cheng Yeh, Prediction of Strength of Fly Ash and Slag Concrete By The Use of Artificial Neural Networks,
    Journal of the Chinese Institute of Civil and Hydraulic Engineering, Vol. 15, No. 4, pp. 659-663 (2003).
  5. I-Cheng Yeh, A mix Proportioning Methodology for Fly Ash and Slag Concrete Using Artificial Neural Networks,
    Chung Hua Journal of Science and Engineering, Vol. 1, No. 1, pp. 77-84 (2003).
  6. I-Cheng Yeh, Analysis of strength of concrete using design of experiments and neural networks,
    Journal of Materials in Civil Engineering, ASCE, Vol.18, No.4, pp.597-604 (2006).

PROJECT 3

Title: Short-term Freeway Speed Prediction: A Spatio-Temporal LSTM Model
Authors: Yi-Ting Lin, and Yen-Lin Huang

Abstract: Freeway speed prediction is one of a key components in the Intelligent Transportation System (ITS), a robust prediction model can provide useful information for both the vehicle drivers and traffic management agencies.

Figure 3a. Speed matrix of traffic flow vs time.

In response to such needs, temporal indications and spatial features are extracted from the GPS-based speed data (i.e., probe speed), and a LSTM model, which has been widely proven to be effective in modeling complex traffic flow problems, is proposed to forecast the speed under recurring traffic and non-recurring traffic conditions.

Data Sources

References

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    Transportation Research Record. No. 722, pp. 1-9.
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    IEEE 19th International Conference of Intelligent Transportation System. Nov., pp. 1610-1615.
  3. Z. H. Mir and F. Filali. 2016. An adaptive Kalman filter based traffic prediction algorithm
    for urban road network. 12th International Conference of Innovative Information Technology. Nov., pp. 1-6.
  4. K.C. Chu, R. Saigal, and K. Saitou. 2016. Stochastic Lagrangian traffic flow modeling and real-time
    traffic prediction. IEEE international Conference of Automatic Science. Aug., pp. 213-218.
  5. B. Sun, W. Cheng, P. Goswami, and G. Bai. 2017. Flow-aware WPT K-nearest neighbours regression for
    short-term traffic prediction. IEEE Symposium on Computers and Communications. Jul., pp. 48-53.
  6. J. Tang, H. Wang, Y. Wang, X. Liu, and F. Liu. 2014. Hybrid prediction approach based on weekly
    similarities of traffic flow for different temporal scales. Transportation Research Record. No. 2443, pp. 21-31.
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    Neurocomputing. Vol. 74, No. 12-13, pp. 2096-2107.
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    prediction under typical and atypical traffic conditions. Expert System Application. Vol. 36, No. 3, pp. 6164-6173.
  9. S.H. Huang and B. Ran. 2003. An application of neural network on traffic speed prediction under adverse
    weather condition. Transportation Research Board Annual Meeting.
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    neural network approach. Journal of Transportation Engineering. Vol. 132, pp. 114-121.
  11. A.J. Toestes, T. H. Silva, R. Assuncao, F. L. P. Duarte-Figueiredo, and A. A. F. Loureiro. 2016.
    STRIP: A short-term traffic jam prediction based on logistic regression.
    IEEE 84th Vehicular Technology Conference. Sep., pp. 1-5.
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    prediction considering periodic characteristic.
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    IEEE 19th International Conference of Intelligent Transportation System . Nov., pp. 1217-1222.
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    traffic speed prediction using remote microwave sensor data.
    Transportation Research Part C: Emerging Technologies. Vol. 54, pp. 187-197.
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    prediction using deep learning method. IET Intelligent Transportation System. Vol. 11, No. 9, pp. 531-536.
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    prediction using deep learning theory. PLos ONE. Vol. 10, No. 3.
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    neural network for large-scale transportation network speed prediction. Sensors. Vol 17, No. 4, p. 818.
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    traffic condition forecasting. [Online].
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    traffic forecasting. In. Conf. Learn. Represent.
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    using mobile phone GPS data. Transportation Research Part C: Emerging Technologies. Vol. 75, pp. 30-44.
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    Journal of Public Transportation. Vol. 17, No. 2, pp. 45-65.
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    prediction using GPS bus data: case study in Dublin.
    IEEE International Conference Intelligent Transportation Systems. Anchorge, pp. 1620-1625.
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    An ensemble Kalman filtering approach to highway traffic estimation using GPS enabled mobile devices.
    IEEE Conference of Decision and Congrol. Mexico, pp. 5062-5068.
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    with taxi GPS traces. IEEE Global Communications Conference. Austin, pp. 2745-2750.
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    Tools with Artificial Intelligence. Cyprus, pp. 922-929.
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    state-space neural networks under missing data.
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    Markov chains. Transportation Part B: Methodological. Vol. 42, No. 4, pp. 325-338.
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    non-recurrent congestion with neural networks. Neural Computation Application. Vol. 23, No. 6, pp. 325-338.
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    freeway travel time prediction. Transportation Research Part C: Emerging Technologies. Vol. 19, No. 6, pp. 1611-1629.
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    deep belief networks with multitask learning.
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    IEEE Transaction of Intelligent Transportation System. Vol. 15, No. 2, pp. 794-804.

PROJECT 4

Title: Impact of Hurricanes on Eviction Rates
Author: Adriana Bryant and Caitlin Kenney

Abstract: The goal of this project is to identify any patterns between hurricane and tropical storm severity/intensity on county eviction rates. The hypothesis is that the number of evictions increases due to storm intensity, given that the damage inflicted by the storm forces many to leave their homes. Unable to return until the damage can be fixed, we hypothesize that the response time (time between storm and aid funding is received) and the amount of FEMA aid may contribute to the eviction rate.

Figure 4a. Hurricane trajectories over South Carolina, circa 2004-2018.

This project will focus on South Carolina county data ranging from 2004 -2018; SC has one of the highest rates of evictions in the US over the past 20 years; they also have had a number of tropical storms and hurricanes during that time period. The outcome of this project will be a visualization based on data classifiers using Weka.

Data Sources

References

  1. Funding from FEMA (post storm) (circa 2004 -2018).
  2. Beyond Disasters: A Longitudinal Analysis of Natural Hazards’ Unequal Impacts on Residential Instability.

PROJECT 5

Title: Predicting Power Output from Solar Panel Data Across the U.S.
Authors: Timothy Henderson

Abstract: The goal of this project is to use a dataset collected from various sites with solar panels across the U.S. to predict power output levels. The U.S. is currently in a transitory period between nonrenewable and renewable energy. This process has only begun, so determining the optimal placement of solar panels throughout such an enormous country can prove to be challenging.

Figure 5a. Solar generation capacity throughout the U.S. in 2017.

My project aims to create a model to predict the power output of a solar panel setup based on various factors such as location, weather, temperature etc. If the model proves to be accurate, it could be used as an indicator for energy companies, governments, or individuals for where in the U.S. to place solar panels for maximum efficiency.

Data Sources

References

  1. https://www.emerald.com/insight/content/doi/10.1016/j.aci.2019.11.002/full/html
  2. https://www.energy.gov/eere/solar/solar-forecasting-2
  3. https://forecast.solar/

PROJECT 6

Title: Predicting COVID-19 Vaccination Levels of ZCTAs Using Socioeconomic and Demographic Data
Author: Fei Wu

Abstract: Since the start of COVID-19 vaccine rollout following FDA’s permission in December 2020, vaccination progress has shown pronounced inequality across different areas in the country, which is partly responsible for differences in COVID hospitalization and fatality rates. Researchers have found such inequality attributable to socioeconomic and demographic factors like income level, race composition, and education level, using relatively simple regression methods. In this project, we intend to train a prediction model that estimates the level of COVID-19 vaccination progress at the ZIP code level, with inputs of socioeconomic and demographic variables. Among the available demographic and vaccination datasets, data of New York City, Chicago and California will be the training set, while data of Massachusetts will be the validation set.

Figure 1a. County-level rates of vaccination for COVID-19 in New York City.

We will try two or three data mining and machine learning methods and compare their performances. When using decision trees including the J48 classifier, bagging method will be applied to the training set. Neural networks with no or one hidden layer will also be applied with optimizable weights on input attributes for better predictions of pre-stratified vaccination levels. Using the model with the highest prediction accuracy, we may identify the most indicative input attributes and estimate the ZIP-level vaccination levels of states and cities where recorded ZIP-level vaccination data are absent.

Data Sources

Items 3 through 6 record numbers and percentages of people who are partially and fully vaccinated in each ZCTA.

References

  1. Bor, J., Assoumou, S.A., Lane, K., Diaz, Y., Ojikutu, B., Raifman, J., and Levy, J.I. (2022).
    Inequities in COVID-19 vaccine and booster coverage across Massachusetts ZIP codes:
    large gaps persist after the 2021/22 Omicron wave. medRxiv.
  2. Mofleh, D., Almohamad, M., Osaghae, I., Bempah, S., Zhang, Q., Tortolero, G., ... and Sharma, S. V. (2022).
    Spatial Patterns of COVID-19 Vaccination Coverage by Social Vulnerability Index and Designated COVID-19
    Vaccine Sites in Texas. Vaccines, 10(4), 574.
  3. Rich, J., Miech, E., Bilal, U., and Corbin, T. (2022). How Education and Racial Segregation Intersect in
    Neighborhoods with Persistently Low COVID-19 Vaccination Rates in Philadelphia.
  4. Sacarny, A., and Daw, J. R. (2021, September).
    Inequities in COVID-19 vaccination rates in the 9 largest US cities.
    In JAMA Health Forum (Vol. 2, No. 9, pp. e212415-e212415). American Medical Association.
  5. Sen-Crowe, B., Lin, I. C., Alfaro, R., McKenney, M., and Elkbuli, A. (2021).
    COVID-19 fatalities by zip codes and socioeconomic indicators across various US regions.
    Annals of Medicine and Surgery, 67, 102471.

PROJECT 7

Title: Estimation of Pedestrian Volume for Brooklyn Bridge
Author: Hanson Liao

Abstract: The United States is experiencing an unprecedented pedestrian fatality crisis, with pedestrian and bicyclist fatalities increasing by 53% and 36% respectively since 2015 (Wallace et. al., 2021). As of May 2021, pedestrian deaths in NYC had surged from 26 to 41. This is a 58% increase in fatalities (Governors Highway Safety Association, 2022). While probable causes ranges from speeding, distracted driving, larger vehicles, etc, the bottom line is that cities in the US need accurate and predictable assessments of pedestrian volume, in order to implement traffic solutions; Specifically, a machine to predict pedestrian volumes for upcoming days.

This project will use data mining techniques to estimate pedestrian volume for the Brooklyn Bridge.

Figure 7a. Aerial view of Brooklyn Bridge.

We will use The Brooklyn Bridge as a representative case study, called the Brooklyn Bridge Automated Pedestrian Counts (2017 - 2019) -- see details below.

Figure 7b. Preliminary framework for estimation of pedestrian density.

Current challenges include:

We will utilize WEKA for the data mining. 75% of the data will be used for training in for the J48 Tree. The remaining 25% of the data will be used for testing. The machine will learn to cluster weather, weekday type, and holiday characteristics to levels of visitor volume.

If successful, this study could be repeated for for more crowded/higher risk intersections of NYC, thereby improving our understanding of traffic patterns and reduction of crashes/accidents

Data Sources

References

  1. Wallace, M., Kitson, C., Ormstrup, M., Cherian, J., & Saleh, J. H. (2021). Pedestrian and light transit accidents: an examination of street redesigns
    in Atlanta and their safety outcomes. Case studies on transport policy, 9(2), 538-554.
  2. Levulytė, L., Baranyai, D., Sokolovskij, E., & Török, Á. (2017).
    Pedestrians’ role in road accidents. International Journal for Traffic and Transport Engineering, 7(3), 328-341.
  3. Governors Highway Safety Association, and the NYC department of Public transportation (2021)
  4. Mohamed, M. G., Saunier, N., Miranda-Moreno, L. F., & Ukkusuri, S. V. (2013).
    A clustering regression approach: A comprehensive injury severity analysis of
    pedestrian–vehicle crashes in New York, US and Montreal, Canada. Safety science, 54, 27-37.
  5. Yasmin, S., Eluru, N., & Ukkusuri, S. V. (2014). Alternative ordered response frameworks for
    examining pedestrian injury severity in New York City. Journal of Transportation Safety & Security, 6(4), 275-300.
  6. Song, L., Fan, W. D., Li, Y., & Wu, P. (2021). Exploring pedestrian injury severities at pedestrian-vehicle crash hotspots with an annual upward trend:
    A spatiotemporal analysis with latent class random parameter approach. Journal of safety research, 76, 184-196.

PROJECT 8

Title: Solving Plate Buckling Problems with Neural Networks
Author: Ahmet Mehdi Darilmaz

Abstract: Understanding the behavior of structural elements is a key aspect of making the most efficient and economic design.

An alternative way to fulfill this task is to use Physics Informed Neural Networks (PINNs). Having the computer learn from the analytical and experimental data brings a broader understanding to the actual physical problems. This may help improving empirical equations used in structural design or discover different aspects of physical phenomena.

Figure 8a. Modeling plate buckling behavior with physics-informed neural networks.

The purpose of this project will be to examine the plate buckling problem with an eye to achieving a deeper understanding for how PINNs might be applied to the solution of structural analysis problems.

The neural network was trained to understand the governing equation that maps out the position of points on the plate after buckling. A custom loss function involving the residual values coming from boundary conditions, initial conditions and the governing equation was used for optimizing the network. The usage of PINNs for analyzing structural elements can be extended to more complex problems and real-life applications.

References

  1. Becque, J. (2010). Inelastic Plate buckling. Journal of Engineering Mechanics, 136(9), 1123–1130. https://doi.org/10.1061/(asce)em.1943-7889.0000075
  2. Bleich, F. (1952). Buckling strength of metal structures. McGraw-Hill.
  3. Duong, H. T., Phan, H. C., Tran, T. M., & Dhar, A. S. (2021). Assessment of critical buckling load of functionally graded plates using artificial neural network modeling. Neural Computing and Applications, 33(23), 16425–16437. https://doi.org/10.1007/s00521-021-06238-6
  4. Lagaris, I. E., Likas, A., & Fotiadis, D. I. (1998). Artificial neural networks for solving ordinary and partial differential equations. IEEE Transactions on Neural Networks, 9(5), 987–1000. https://doi.org/10.1109/72.712178
  5. Lu, L., Meng, X., Mao, Z., & Karniadakis, G. E. (2021). DeepXDE: A deep learning library for solving differential equations. SIAM Review, 63(1), 208–228. https://doi.org/10.1137/19m1274067
  6. Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed Neural Networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686–707. https://doi.org/10.1016/j.jcp.2018.10.045
  7. Timoshenko, S., & Gere, J. M. (2009). Theory of elastic stability. Dover Publications.
  8. Wu, X., Ghaboussi, J., & Garrett, J. H. (1992). Use of neural networks in detection of structural damage. Computers & Structures, 42(4), 649–659. https://doi.org/10.1016/0045-7949(92)90132-j
  9. Zhao, X., Lee, Y. Y., & Liew, K. M. (2009). Mechanical and thermal buckling analysis of functionally graded plates. Composite Structures, 90(2), 161–171. https://doi.org/10.1016/j.compstruct.2009.03.005

Last Modified: April 26, 2022,
Copyright © 2022, Department of Civil and Environmental Engineering, University of Maryland.