Civil & Environmental Engineering
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Item DATA-DRIVEN RISK MODELING FOR INFRASTRUCTURE PROJECTS USING ARTIFICIAL INTELLIGENCE TECHNIQUES(2023) Erfani, Abdolmajid; Cui, Qingbin; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Managing project risk is a key part of the successful implementation of any large project and is widely recognized as a best practice for public agencies to deliver infrastructures. The conventional method of identifying and evaluating project risks involves getting input from subject matter experts at risk workshops in the early phases of a project. As a project moves through its life cycle, these identified risks and their assessments evolve. Some risks are realized to become issues, some are mitigated, and some are retired as no longer important. Despite the value provided by conventional expert-based approaches, several challenges remain due to the time-consuming and expensive processes involved. Moreover, limited is known about how risks evolve from ex-ante to ex-post over time. How well does the project team identify and evaluate risks in the initial phase compared to what happens during project execution? Using historical data and artificial intelligence techniques, this study addressed these limitations by introducing a data-driven framework to identify risks automatically and to examine the quality of early risk registers and risk assessments. Risk registers from more than 70 U.S. major transportation projects form the input dataset. Firstly, the study reports a high degree of similarity between risk registers for different projects in the entire document of the risk register, and the probability and consequence of each risk item, suggesting that it is feasible to develop a common risk register. Secondly, the developed data-driven model for identifying common risks has a recall of over 66% and an F1 score of 0.59 for new projects, i.e., knowledge and experience of similar previous projects can help identify more than 66% of risks at the start. Thirdly, approximately 65% of ex-ante identified risks actually occur in projects and are mitigated, while more than 35% do not occur and are retired. The categorization of risk management styles illustrates that identifying risks early on is important, but it is not sufficient to achieve successful project delivery. During project execution, a project team demonstrating positive doer behavior (by actively monitoring and identifying risks) performed better. Finally, this study proposes using a data-driven approach to unify and summarize existing risk documents to create a comprehensive risk breakdown structure (RBS). Study results suggest that acquired knowledge from previous projects helps project teams and public agencies identify risks more effectively than starting from scratch using solely expert judgments.Item A BIG-DATA-DRIVEN FRAMEWORK FOR SPATIOTEMPORAL TRAVEL DEMAND ESTIMATION AND PREDICTION(2023) Hu, Songhua; Schonfeld, Paul; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Traditional travel demand models heavily rely on travel surveys, simplify future demand forecasting, and show low sensitivity in response to spatiotemporal dynamics. This study proposes a deep-learning-driven framework based on mobile device location data (MDLD) for estimating and predicting large-scale travel demand at both individual and aggregated levels. This study first introduces how raw MDLD should be parsed to distill trip rosters and estimate population flow. Based on derived information, this study reexamines relations between average population flow and its determinants such as built environment, socioeconomics, and demographics, via a set of explainable machine learning (EML) models. Different interpretation approaches are employed and compared to understand nonlinear and interactive relations learned by EML models. Next, this study proposes a Multi-graph Multi-head Adaptive Temporal Graph Convolutional Network (Multi-ATGCN), a general deep learning framework that fuses multi-view spatial structures, multi-head temporal patterns, and various external effects, for multi-step citywide population flow forecasting. Multi-ATGCN is designed to comprehensively address challenges such as complex spatial dependency, diverse temporal patterns, and heterogeneous external effects in spatiotemporal population flow forecasting. Last, at an individual level, this study proposes a Hierarchical Activity-based Framework (HABF) for simultaneously predicting the activity, departure time, and location of the origin and destination of the next trip, incorporating both internal (individual characteristics) and external (calendar, point-of-interests (POIs)) information. For each individual, HABF first predicts activities via an Interpretable Hierarchical Transformer (IHTF). IHTF can efficiently handle big data benefiting from its transformer-based design to avoid recursion. Meanwhile, loss functions used in semantic segmentation are introduced into IHTF to address imbalanced distributions of activity types. Then, a local plus global probabilistic generator is designed to generate locations based on predicted activities and historical places, allowing individuals to visit new or historically-sparse places. Analyses are performed on several real-world datasets to demonstrate the model's capability in forecasting large-scale high-resolution human mobility in a timely and credible manner. Altogether, this study provides sound evidence, practically and theoretically, of the feasibility and reliability of realizing data-driven travel demand estimation and prediction at different spatiotemporal resolutions and scales.Item National-Level Origin-Destination Estimation Based on Passively Collected Location Data and Machine Learning Methods(2021) Pan, Yixuan; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Along with the development of information and positioning technologies, there emerges passively collected location data that contain location observations with time information from various types of mobile devices. Passive location data are known for their large sample size and continuous behavior observations. However, passive location data also require careful and comprehensive data processing and modeling algorithms for privacy protection and practical applications.In the meantime, the travel demand estimation of origin-destination tables is fundamental in transportation planning. There lacks a national origin-destination estimation that provides time-dependent travel behaviors for all travel modes. Passive collected location data appeal to researchers with the potential of serving as the data source for large-scale multimodal travel demand estimation and monitoring. This research proposes a comprehensive set of methods for passive location data processing including data cleaning, activity location and purpose identification, trip-level information identification, social demographic imputation, sample weighting and expansion, and demand validation. For each task, the thesis evaluates the state-of-the-practice and state-of-the-art algorithms, and develops an applicable method jointly considering the different features of various passive location data sources and the imputation accuracy. The thesis further examines the viability of the method kit in a national-level case study and successfully derives the national-level origin-destination estimates with additional data products, such as trip rate and vehicle miles traveled, at different geographic levels and temporal resolutions.Item INTRODUCING A GRAPH-BASED NEURAL NETWORK FOR NETWORKWIDE TRAFFIC VOLUME ESTIMATION(2021) Zahedian, Sara; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Traffic volumes are an essential input to many highway planning and design models; however, collecting this data for all the roads in a network is not practical nor cost-effective. Accordingly, transportation agencies must find ways to leverage limited ground truth count data to obtain reasonable estimates at scale on all the network segments. One of the challenges that complicate this estimation is the complex spatial dependency of the links’ traffic state in a transportation network. A graph-based model is proposed to estimate networkwide traffic volumes to address this challenge. This model aims to consider the graph structure of the network to extract its spatial correlations while estimating link volumes. In the first step, a proof-of-concept methodology is presented to indicate how adding the simple spatial correlation between the links in the Euclidian space improves the performance of a state-of-the-art volume estimation model. This methodology is applied to the New Hampshire road network to estimate statewide hourly traffic volumes. In the next step, a Graph Neural Network model is introduced to consider the complex interdependency of the road network in a non-Euclidean domain. This model is called Fine-tuned Spatio-Temporal Graph Neural Network (FSTGCN) and applied to various Maryland State networks to estimate 15-minute traffic volumes. The results illustrate significant improvement over the existing state-of-the-art models used for networkwide traffic volume estimation, namely ANN and XGBoost.Item SMART STRUCTURAL CONDITION ASSESSMENT METHODS FOR CIVIL INFRASTRUCTURES USING DEEP LEARNING ALGORITHM(2018) Liu, Heng; Zhang, Yunfeng; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Smart Structural Health Monitoring (SHM) technique capable of automated and accurate structural health condition assessment is appealing since civil infrastructural resilience can be enhanced by reducing the uncertainty involved in the process of assessing the condition state of civil infrastructures and carrying out subsequent retrofit work. Over the last decade, deep learning has seen impressive success in traditional pattern recognition applications historically faced with long-time challenges, which motivates the current research in integrating the advancement of deep learning into SHM applications. This dissertation research aims to accomplish the overall goal of establishing a smart SHM technique based on deep learning algorithm, which will achieve automated structural health condition assessment and condition rating prediction for civil infrastructures. A literate review on structural health condition assessment technologies commonly used for civil infrastructures was first conducted to identify the special need of the proposed method. Deep learning algorithms were also reviewed, with a focus on pattern recognition application, especially in the computer vision field in which deep learning algorithms have reported great success in traditionally challenging tasks. Subsequently, a technical procedure is established to incorporate a particular type of deep learning algorithm, termed Convolutional Neural Network which was found behind the many success seen in computer vision applications, into smart SHM technologies. The proposed method was first demonstrated and validated on an SHM application problem that uses image data for structural steel condition assessment. Further study was performed on time series data including vibration data and guided Lamb wave signals for two types of SHM applications - brace damage detection in concentrically braced frame structures or nondestructive evaluation (NDE) of thin plate structures. Additionally, discrete data (neither images nor time series data), such as the bridge condition rating data from National Bridge Inventory (NBI) data repository, was also investigated for the application of bridge condition forecasting. The study results indicated that the proposed method is very promising as a data-driven structural health condition assessment technique for civil infrastructures, based on research findings in the four distinct SHM case studies in this dissertation.Item A discrete-continuous modeling approach with applications to vehicle holding and use(2012) Tremblay, Jean-Michel; Cirillo, Cinzia; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Transportation and automobile use is a major concern today in the United- States. The use of automobile has impacts on congestion, urban dynamics, environ- ment and on the economy in general. Good indicators of transportation demand are the number of vehicles owned by a household and the total number of miles traveled. This thesis aims at building a model that can predict the total vehicle miles traveled and number of cars owned by households, simultaneously. The discrete- continuous model that we present correlates the error terms of a utility-based probit with the error term of an ordinary regression. The objective is to capture the relationship between preferred ownership alternatives and miles traveled. We successfully show that households with high utility for owning a lot of cars also drive more and that households with high utility for owning few cars drive less. The correlation is between utilities and miles traveled. It also correlates the two transportation demand indicators without assuming that one precedes the other and, thus, does not suffer from circular variable inclusions. The thesis ends by incorporating sampling weights into the model before pa- rameters are estimated. We find slight changes in parameters' values calculated with weights. The difference however, is more quantitative than qualitative since the general analysis we make with the weighted coefficients remains the same, only the magnitude of the effects changeItem Cyber-security Risk Assessment(2011) Panjwani, Susmit; Baecher, Gregory B; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Cyber-security domain is inherently dynamic. Not only does system configuration changes frequently (with new releases and patches), but also new attacks and vulnerabilities are regularly discovered. The threat in cyber-security is human, and hence intelligent in nature. The attacker adapts to the situation, target environment, and countermeasures. Attack actions are also driven by attacker's exploratory nature, thought process, motivation, strategy, and preferences. Current security risk assessment is driven by cyber-security expert's theories about this attacker behavior. The goal of this dissertation is to automatically generate the cyber-security risk scenarios by: * Capturing diverse and dispersed cyber-security knowledge * Assuming that there are unknowns in the cyber-security domain, and new knowledge is available frequently * Emulating the attacker's exploratory nature, thought process, motivation, strategy, preferences and his/her interaction with the target environment * Using the cyber-security expert's theories about attacker behavior The proposed framework is designed by using the unique cyber-security domain requirements identified in this dissertation and by overcoming the limitations of current risk scenario generation frameworks. The proposed framework automates the risk scenario generation by using the knowledge as it becomes available (or changes). It supports observing, encoding, validating, and calibrating cyber-security expert's theories. It can also be used for assisting the red-teaming process. The proposed framework generates ranked attack trees and encodes the attacker behavior theories. These can be used for prioritizing vulnerability remediation. The proposed framework is currently being extended for developing an automated threat response framework that can be used to analyze and recommend countermeasures. This framework contains behavior driven countermeasures that uses the attacker behavior theories to lead the attacker away from the system to be protected.