Civil & Environmental Engineering
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Item Prediction of Marine Timber Pile Damage Ratings Using a Gradient Boosted Regression Model(2023) Willmott, Carly; Attoh-Okine, Nii O.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Marine pilings are critical structural elements exposed to harsh environmental conditions. Specialized routine inspection and regular maintenance are essential to keep marine facilities in good working condition. These activities generate data that can be exploited for knowledge gain with machine learning tools. A gradient boosted random forest regressor machine learning algorithm, XGBoost, was applied to datasets that contain timber pile underwater inspection and repair data over a period of 23 years. First, the data was visualized to show the longevity of different timber pile repair types. An XGBoost model was then tuned and trained on a dataset for timber piles at one pier. Variables in the dataset were evaluated for feature importance in predicting damage ratings assigned during routine underwater inspections. Next, an ensemble of XGBoost models was trained and applied to a second dataset containing the same features for an adjacent pier. This dataset was reserved for testing to demonstrate whether the ensemble trained on one pier’s data could be generalized to predict timber pile damage ratings at a nearby but separate pier. Finally, the ensemble was used to predict damage ratings on piles that had earlier data but were not rated in the two most recent inspection events. Results suggest that the ensemble is capable of predicting timber pile damage ratings to approximately +/- one damage rating on both the training and test datasets. Feature importances revealed that half of the variables (time since the first event, repair type, exposed pile length, and time since the last repair) contributed to two thirds of the relative importance in predicting damage ratings. Data visualization showed that a few repair types, such as pile replacements and encapsulations, appeared to be most successful over the long term compared with shorter-lived repairs like wraps and encasements. These results are promising indications of the advantages machine learning algorithms can offer in processing and gleaning new insights from structural repair and inspection data. Economic benefits to marine facility owners can potentially be realized through earlier anticipation of repairs and more targeted inspection and rehabilitation efforts. There are also opportunities for better understanding of deterioration rates if more data is gathered over the lifespans of structures, as well as more detailed data that can be introduced as new features.Item SUSTAINABILITY, ACCEPTANCE RISK ANALYSIS AND MACHINE LEARNING IN ASSESSING MECHANICAL PROPERTIES AND THE IMPACT OF HIGHWAY MATERIALS IN TRANSPORTATION INFRASTRUCTURE(2023) Zhao, Yunpeng; Goulias, Dimitrios G; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Improving the performance and extending the service life of transportation infrastructure is a long standing goal of Federal Highway Administration (FHWA) and the transportation community. Accurate prediction of the mechanical properties of highway materials are indispensable for enhancing the sustainability and resilience of transportation infrastructure since it provides accurate inputs for pavement mechanistic-empirical (ME) design and prediction of pavement distresses, helping to optimally allocate the maintenance needs and reduce testing frequencies which account for costly expenditures. Accurate prediction of materials properties can also reduce the acceptance risks during quality assurance (QA) without conducting extensive testing. Concrete plays an important role in the construction of transportation infrastructure. Developing an empirical and/or statistical model for accurately predicting compressive strength remains challenging and requires extensive experimental work. Thus, the objective of the study was to improve the prediction of concrete compressive strength using ML algorithms. A ML pipeline was proposed in which a two-layer stacked model was developed by combining seven individual ML models. Feature engineering was implemented, and feature importance was evaluated to provide better interpretability of the data and the model. This study promotes a more thorough assessment of alternative ML algorithms for predicting material properties. In addition, the quality of highway materials and construction translate directly to performance. To develop a statistically sound QA specification, the risks to the agency and contractor must be well understood. In this study, a Monte Carlo simulation model was developed to systematically assess the acceptance risks and the implications on pay factors (PF). The simulation was conducted using typical acceptance quality characteristics (AQCs), such as strength, for Portland cement (PCC) pavements. The analysis indicated that specific combinations of contractor and agency sample sizes and population characteristics have a greater impact on acceptance risks and may provide inconsistent PF. The proposed methodology aids both agencies and producers to better understand and evaluate the impact of sample sizes and population characteristics on the acceptance risks and PF. Finally, the use of recycled materials is a key element in generating sustainable pavement designs to save natural resources, reduce energy, greenhouse gas (GHG) emissions and costs. This study proposed a methodological life cycle assessment (LCA) framework to quantify the environmental and economic impacts of using recycled materials in pavement construction and rehabilitation. The LCA was conducted on two roadway projects with innovative recycled materials, such as construction and demolition waste (CDW) and rock dust. The proposed LCA framework can be used elsewhere to quantify the environmental and economic benefits of using recycled materials in pavements.Item STRUCTURAL PERFORMANCE ASSESSMENT ON PREVENTIVE MAINTENANCE/REHABILITATION OF STEEL GIRDER BRIDGE SYSTEMS(2022) Zhu, Yifan; Fu, Chung C.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Bridge maintenance, including preventive maintenance, rehabilitation, and replacement keeps the structure safe in its service life. Bridge maintenance methods have developed and expanded to bridge inspection, bridge condition assessments, structural health monitoring technologies, service life prediction, and maintenance with new materials or technologies. This dissertation proposes two structural performance assessments, (1) a rapid machine learning assessment classifying whether the design is under an acceptable range; and (2) comparing the structural health monitoring data with engineers’ predictions to evaluate the current structural performance.The first part of this dissertation focuses on preventive maintenance evaluation. This part discusses and plans a specific topic to instrument a newly constructed link slab system on a multiple simply-supported bridge. Before any simulation and field test of the general steel I-girder bridge model was conducted, literature regarding bridge maintenance, performance assessment, the durability of bridges, structural health monitoring method, current condition assessment methods, and research on the material and structural behavior of link slabs were reviewed and investigated. Then comprehensive experimental programs on the new materials HPFRC and ECC were conducted, and the extensive hands-on laboratory results update the nonlinearity and accuracy of the structure model. Moreover, a series of data analyses of the current steel bridge in the United States was conducted for further database establishment. Two sets of simulation-based finite element parametric analyses of the bridge with protective maintenance or structural repairing were introduced to generate preferred designs and further be used for rapid performance assessment. The inputs are the configuration of the bridge and the proposed work or deteriorated location, which generate the dataset for the training, validating, and testing of the evaluation model. The resulting regression model allows for quick assessment of the function developed by taking advantage of machine learning. This research verifies the assessment’s results using the Maryland Transportation Authority (MDTA) pilot HPFRC link slab system on the bridge overpass I-95 as a case study by comparing the prediction and actual structure health monitoring data after the assessment’s results were verified, and its performance was evaluated. This dissertation also examines one case from the Maryland State Highway Administration (MDSHA) bridge over the Patapsco River, which has several frozen expansion rocker bearings. This restrained the longitudinal movement of the superstructures due to thermal expansion and contraction. It has partially recovered to its normal condition after repairing and strengthening the pier under this bearing. In this study, we combined the finite element analysis and monitoring data to simulate structural behavior, and evaluate repair work using the proposed methods. Finally, This research evaluates the repaired bridge with partially strengthened structural components (i.e., deck, girders, or piers) and forecasts its wear and tear. In this study, the original and deteriorated bridges were numerically modeled first and simplified to 3D grid models. Then, the current condition rating process was used to determine the structural performance at the element level. After establishing the evaluation criteria and investigating the corresponding condition ratings, the rapid assessment models for the repaired bridges were carried out. The condition prediction relied on historical ratings and (if any) monitoring data.Item Effects of Slope Ratio, Straw Mulching, and Compost Amendment on Vegetation Establishment and Runoff Generation(2020) Owen, Dylan; Davis, Allen P; Aydilek, Ahmet; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Soil erosion management is a major environmental challenge facing highway construction. This study was undertaken to evaluate the effectiveness of compost use in lieu of topsoil for final grade turfgrass establishment on highway slopes. Two compost types, biosolids and greenwaste, and four compost/topsoil blends were compared with a topsoil standard (TS; with straw and fertilizer application) in their ability to reduce soil and nutrient loss and improve vegetation establishment. A series of greenhouse studies and field tests were conducted to analyze the effects of slope ratio, straw mulching, and compost mixing ratio on runoff by observing green vegetation (GV) establishment, runoff volume generation, and nutrient and sediment export. GV was measured using an innovative image segmentation and classification algorithm coupled with machine learning approaches with varying block size and classification acceptance thresholds. Algorithm classifications were compared to manual coverage classifications with R-squared values of 0.86 for GV, 0.87 for straw/dormant vegetation, and 0.96 for exposed soil, respectively. Straw mulching (≥95% straw cover) reduced evaporation rates and soil sealing and increased soil roughness and field capacity (FC), which significantly reduced volume runoff (34-99%) and mass export of sediment and nutrients (81-91%). With mulching, no statistical differences were found in GV establishment among the compost and TS treatments (≥95% cover in 60 days) while non-mulched media cover reached a maximum of 35%, due to limited moisture availability. Composted material (excluding 2:1 compost: topsoil mixtures) had higher hydraulic conductivity, FC, and shear strength than TS which, combined with straw mulching, reduced total runoff volume by 33-72%. This led to sediment and nutrient mass reductions of 57-97% and 6-82%, respectively, from standard TS. A general increase in runoff generation and decrease in GV was seen with slope ratio increase (41-96% more nutrient and sediment export and 81-97% lower GV from 20:1 to 2:1 slopes). However, benefits displayed at 25% slope were reduced at shallower slopes and enhanced at greater slopes. The use of compost as an additive or replacement to TS, with straw mulching, was seen to reduce runoff generation and improve runoff quality from the TS standard and is suggested as possible alternatives.Item NATIONWIDE ANNUAL AVERAGE DAILY TRAFFIC (AADT) ESTIMATION ON NON-FEDERAL AID SYSTEM (NFAS) ROADS BY MACHINE LEARNING WITH DATA MINING OF BUILT-IN ENVIRONMENT(2020) Sun, Qianqian; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This study aims to address the nationwide gap in AADT data on NFAS roads in U.S. With a Spatial Autoregressive Model as a benchmark, two machine-learning approaches, i.e. Artificial Neural Network and Random Forest, show notable improvement in the accuracy of estimating AADT according to five measures, i.e. MSE, RSQ, RMSE, MAE, and MAPE. A data-mining of the built-in environment from three perspectives, i.e. on-road and off-road features, network centralities, and neighboring influences, paves the way for AADT estimation, which covers 87 variables in centrality, neighboring traffic, demographics, employment, land-use diversity, road network density, urban design, destination accessibility, etc. Data integration using different buffering sizes and statistical analysis of linearity and monotonicity promote the variable selection for estimation. When implementing the machine-learning approaches, not only the estimation performance is analyzed, but also the relationship between each variable and AADT, the interplays among variables, variable importance measures are thoroughly discussed.Item ARTERIAL PROBABILISTIC TRAFFIC MODELING AND REAL-TIME TRAVEL TIME PREDICTION WITH VEHICLE PROBE DATA USING MACHINE LEARNING(2018) Zarin, Bahar; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This study proposes a probabilistic modeling framework for the estimation and prediction of link-based arterial travel time distribution using GPS data. The spatiotemporal correlations of the network are modeled using a directional acyclic graphical model, and several external variables in the prediction model are included to yield a better prediction in a variety of situations. This study also aims to investigate the effects of each factor on the travel time and the uncertainty associated with it. In the proposed model, factors such as weather conditions, seasons, time of day, and day of the week are added as external variables in the graphical model. After determining the structure of the model, Streaming Variational Bayes (SVB) is used for training and parameter inference; this offers a valuable option when constant streaming data is utilized. SVB adaptively changes its parameters gradually with a lower computational cost, which makes the process less time-consuming and more efficient. The analysis shows that incorporating external variables can improve the model performance. The data used in this study is INRIX vehicle trajectory raw data from four months - February, June, July, and October of 2015 - which makes it possible to take into account the effects of seasons and weather conditions on travel time and its uncertainty. One of the products of this study is a framework for vehicle trajectory data cleaning process including trip identification, removing outliers, and cleaning the trips data. Once the data are cleaned and ready to use, they should be mapped to the roads. The Hidden Markov Model (HMM) map matching algorithm is used to map the GPS latitude/longitude data to the Open Street Map (OSM) base map and find the traversed links between each pair of GPS points of vehicle trajectories. Finally, a novel procedure to compare any travel time prediction model with any available commercial routing API is proposed and tested to compare the proposed model with Google API.