Civil and Environmental Engineering Commons^™

Artificial Intelligence Driven Infrastructure Management And Maintenance Plan, Julian Jesso

Doctoral Dissertations and Master's Theses

Rapid development in trucking technology and increasing demands in freight transportation has led to longer and heavier vehicles traveling on Florida’s highway system. Vehicles with gross vehicle weight (GVW) over 80,000 pounds, or permit vehicles, have significant effects on infrastructure, thus requiring an approved permit prior to departure. The combination of these increasing loads and harsh environmental conditions that Florida is subject to requires an enhanced infrastructure management program. Additionally, there is a need to eliminate inconsistencies in permit applications and derive a uniform maintenance practice for Florida’s infrastructure. In this research, the focus was to develop an analytical procedure …

Machine Learning Methods To Map Stabilizer Effectiveness Based On Common Soil Properties, Amit Gajurel, Bhaskar Chittoori, Partha Sarathi Mukherjee, Mojtaba Sadegh

Civil Engineering Faculty Publications and Presentations

Most chemical stabilization guidelines for subgrade/base use unconfined compressive strength (UCS) of treated soils as the primary acceptance criteria for selecting optimum stabilizer in laboratory testing. Establishing optimal additive content to augment UCS involves a resource-intensive trial-and-error procedure. Also, samples collected from discrete locations for laboratory trials may not be representative of the overall site. This study aims to minimize the number of laboratory trials and help strategize sampling locations by developing spatial maps of UCS at different treatment levels for lime and cement. These spatial maps were developed using machine-learning techniques, and using a database compiled from various reported …

Review Of Forecasting Univariate Time-Series Data With Application To Water-Energy Nexus Studies & Proposal Of Parallel Hybrid Sarima-Ann Model, Cory Sumner Yarrington

Graduate Theses, Dissertations, and Problem Reports (ETD)

The necessary materials for most human activities are water and energy. Integrated analysis to accurately forecast water and energy consumption enables the implementation of efficient short and long-term resource management planning as well as expanding policy and research possibilities for the supportive infrastructure. However, the integral relationship between water and energy (water-energy nexus) poses a difficult problem for modeling. The accessibility and physical overlay of data sets related to water-energy nexus is another main issue for a reliable water-energy consumption forecast. The framework of urban metabolism (UM) uses several types of data to build a global view and highlight issues …

Editorial: Predictive Modeling Of Human Microbiota And Their Role In Health And Disease, Hyun-Seob Song, Stephen R. Lindemann, Dong-Yup Lee

Department of Agricultural and Biological Systems Engineering: Faculty Publications

No abstract provided.

To Automate Detecting, Quantifying And Mapping Of Delamination Via Arial Thermography, Ziqi Shen, Chongsheng Cheng, Ri Na, Zhexiong Shang

Nebraska Department of Transportation: Research Reports

The goal of this project is to develop a UAV-based Arial thermographic method to detect and map the delamination on concrete bridge decks. Several image processing methods such as level-set and machine-learning were developed in this project. The developed method is high-efficient and low-cost and requires no-traffic-closure during the surveying process. The coring outcomes from four different concrete bride decks validated the performance of this developed method. The coring results suggested that this method outperformed several existing methods in detecting delamination in concrete bridge decks in terms of efficiency, accuracy, cost, and safety.

Traffic Time Headway Prediction And Analysis: A Deep Learning Approach, Saumik Sakib Bin Masud

Theses and Dissertations

In the modern world of Intelligent Transportation System (ITS), time headway is a key traffic flow parameter affecting ITS operations and planning. Defined as “the time difference between any two successive vehicles when they cross a given point”, time headway is used in various traffic and transportation engineering research domains, such as capacity analysis, safety studies, car-following, and lane-changing behavior modeling, and level of service evaluation describing stochastic features of traffic flow. Advanced travel and headway information can also help road users avoid traffic congestion through dynamic route planning, for instance. Hence, it is crucial to accurately model headway distribution …

Enhanced Traffic Incident Analysis With Advanced Machine Learning Algorithms, Zhenyu Wang

Computational Modeling & Simulation Engineering Theses & Dissertations

Traffic incident analysis is a crucial task in traffic management centers (TMCs) that typically manage many highways with limited staff and resources. An effective automatic incident analysis approach that can report abnormal events timely and accurately will benefit TMCs in optimizing the use of limited incident response and management resources. During the past decades, significant efforts have been made by researchers towards the development of data-driven approaches for incident analysis. Nevertheless, many developed approaches have shown limited success in the field. This is largely attributed to the long detection time (i.e., waiting for overwhelmed upstream detection stations; meanwhile, downstream stations …

Development Of A Low Power, Low Cost Rural Railway Intersection Smart Detection And Warning System, Sara Ahmed, Samer Dessouky, Raymond Downing

Data

This project explores a different approach to provide preemptive warning for train detection at grade-crossings to increase safety and reduce motor vehicle congestion. The development of a novel, low cost, low power, and off rail right-of-way (ROW) detection and warning system will be presented. A background of track circuits, which is the rail industries standard for train detection, will also be provided to highlight the benefits and challenges of the rail industry installing a system at every grade-crossings that lack any type of active warning. The benefits of using thermal imaging instead of traditional video for computer vision will also …

Development Of A Low Power, Low Cost Rural Railway Intersection Smart Detection And Warning System, Sara Ahmed, Samer Dessouky, Raymond Downing

Publications

This project explores a different approach to provide preemptive warning for train detection at grade-crossings to increase safety and reduce motor vehicle congestion. The development of a novel, low cost, low power, and off rail right-of-way (ROW) detection and warning system will be presented. A background of track circuits, which is the rail industries standard for train detection, will also be provided to highlight the benefits and challenges of the rail industry installing a system at every grade-crossings that lack any type of active warning. The benefits of using thermal imaging instead of traditional video for computer vision will also …

Assessing The Prevalence Of Suspicious Activities In Asphalt Pavement Construction Using Algorithmic Logics And Machine Learning, Mostofa Najmus Sakib

Boise State University Theses and Dissertations

Quality Control (QC) and Quality Assurance (QA) is a planned systematic approach to secure the satisfactory performance of Hot mix asphalt (HMA) construction projects. Millions of dollars are invested by government and state highway agencies to construct large-scale HMA construction projects. QC/QA is statistical approach for checking the desired construction properties through independent testing. The practice of QC/QA has been encouraged by the Federal Highway Administration (FHWA) since the mid 60’s. However, the standard QC/QA practice is often criticized on how effective such statistical tests and how representative the reported material tests are. Material testing data alteration in the HMA …

A Data-Driven Approach For Winter Precipitation Classification Using Weather Radar And Nwp Data, Bong Chul Seo

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

This study describes a framework that provides qualitative weather information on winter precipitation types using a data-driven approach. The framework incorporates the data retrieved from weather radars and the numerical weather prediction (NWP) model to account for relevant precipitation microphysics. To enable multimodel-based ensemble classification, we selected six supervised machine learning models: k-nearest neighbors, logistic regression, support vector machine, decision tree, random forest, and multi-layer perceptron. Our model training and cross-validation results based on Monte Carlo Simulation (MCS) showed that all the models performed better than our baseline method, which applies two thresholds (surface temperature and atmospheric layer thickness) for …

Evaluation Of Temporal Damage Progression In Concrete Structures Affected By Asr Using Data-Driven Methods, Vafa Soltangharaei

Theses and Dissertations

Alkali-silica reaction (ASR) is a chemical reaction, which causes damage in concrete structures such as bridges, dams, and nuclear containments and powerplant structures. The ASR-induced damage may endanger the integrity and serviceability of structures. Several methods such as visual inspection, petrographic analysis, demountable mechanical strain gauges, and cracking index have been utilized for study the effect of ASR on structures, which are not always efficient in early damage detection and some are destructive and prohibited in nuclear structures. Nondestructive methods and structural health monitoring techniques can be alternatives for the condition assessment of structures. Among the nondestructive methods, acoustic emission …

Data-Driven And Model-Based Methods With Physics-Guided Machine Learning For Damage Identification, Zhiming Zhang

LSU Doctoral Dissertations

Structural health monitoring (SHM) has been widely used for structural damage diagnosis and prognosis of a wide range of civil, mechanical, and aerospace structures. SHM methods are generally divided into two categories: (1) model-based methods; (2) data-driven methods. Compared with data-driven SHM, model-based methods provide an updated physics-based numerical model that can be used for damage prognosis when long-term data is available. However, the performance of model-based methods is susceptible to modeling error in establishing the numerical model, which is usually unavoidable due to model simplification and omission. The major challenge of data-driven SHM methods lies in data insufficiency, e.g., …

Exploiting Earth Observation Data To Impute Groundwater Level Measurements With An Extreme Learning Machine, Steven Evans, Gustavious P. Williams, Norman L. Jones, Daniel P. Ames, E. James Nelson

Faculty Publications

Groundwater resources are expensive to develop and use; they are difficult to monitor and data collected from monitoring wells are often sporadic, often only available at irregular, infrequent, or brief intervals. Groundwater managers require an accurate understanding of historic groundwater storage trends to effectively manage groundwater resources, however, most if not all well records contain periods of missing data. To understand long-term trends, these missing data need to be imputed before trend analysis. We present a method to impute missing data at single wells, by exploiting data generated from Earth observations that are available globally. We use two soil moisture …

Prediction Of Flotation Efficiency Of Metal Sulfides Using An Original Hybrid Machine Learning Model, Rachel Cook, Keitumetse Cathrine Monyake, Muhammad Badar Hayat, Aditya Kumar, Lana Alagha

Materials Science and Engineering Faculty Research & Creative Works

Froth flotation process is extensively used for selective separation of base metal sulfides from uneconomic mineral resources. Reliable prediction of process outcomes (metal recovery and grade) is vital to ensure peak performance. This work employs an innovative hybrid machine learning (ML) model—constructed by combining the random forest model and the firefly algorithm—to predict froth flotation efficiency of galena and chalcopyrite in relation to various experimental process parameters. The hybrid model's prediction performance was rigorously evaluated and compared against four different standalone ML models. The outcomes of this study illustrate that the hybrid ML model has the prediction ability to process …

Truck Trailer Classification Using Side-Fire Light Detection And Ranging (Lidar) Data, Olcay Sahin

Civil & Environmental Engineering Theses & Dissertations

Classification of vehicles into distinct groups is critical for many applications, including freight and commodity flow modeling, pavement management and design, tolling, air quality monitoring, and intelligent transportation systems. The Federal Highway Administration (FHWA) developed a standardized 13-category vehicle classification ruleset, which meets the needs of many traffic data user applications. However, some applications need high-resolution data for modeling and analysis. For example, the type of commodity being carried must be known in the freight modeling framework. Unfortunately, this information is not available at the state or metropolitan level, or it is expensive to obtain from current resources.

Nevertheless, using …

Predicting Escherichia Coli Loads In Cascading Dams With Machine Learning: An Integration Of Hydrometeorology, Animal Density And Grazing Pattern, Olufemi P. Abimbola, Aaron R. Mittelstet, Tiffany Messer, Elaine D. Berry, Shannon L. Bartelt-Hunt, Samuel Hansen

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Accurate prediction of Escherichia coli contamination in surface waters is challenging due to considerable uncertainty in the physical, chemical and biological variables that control E. coli occurrence and sources in surface waters. This study proposes a novel approach by integrating hydro-climatic variables as well as animal density and grazing pattern in the feature selection modeling phase to increase E. coli prediction accuracy for two cascading dams at the USMeat Animal Research Center (USMARC), Nebraska. Predictive models were developed using regression techniques and an artificial neural network (ANN). Two adaptive neuro-fuzzy inference system (ANFIS) structures including subtractive clustering and fuzzy c-means …

Predicting Escherichia Coli Loads In Cascading Dams With Machine Learning: An Integration Of Hydrometeorology, Animal Density And Grazing Pattern, Olufemi P. Abimbola, Aaron R. Mittelstet, Tiffany Messer, Elaine D. Berry, Shannon L. Bartelt-Hunt, Samuel Hansen

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Accurate prediction of Escherichia coli contamination in surface waters is challenging due to considerable uncertainty in the physical, chemical and biological variables that control E. coli occurrence and sources in surface waters. This study proposes a novel approach by integrating hydro-climatic variables as well as animal density and grazing pattern in the feature selection modeling phase to increase E. coli prediction accuracy for two cascading dams at the US Meat Animal Research Center (USMARC), Nebraska. Predictive models were developed using regression techniques and an artificial neural network (ANN). Two adaptive neuro-fuzzy inference system (ANFIS) structures including subtractive clustering and fuzzy …

Vegetation Detection Using Deep Learning And Conventional Methods, Bulent Ayhan, Chiman Kwan, Bence Budavari, Liyun Kwan, Yan Lu, Daniel Perez, Jiang Li, Dimitrios Skarlatos, Marinos Vlachos

Electrical & Computer Engineering Faculty Publications

Land cover classification with the focus on chlorophyll-rich vegetation detection plays an important role in urban growth monitoring and planning, autonomous navigation, drone mapping, biodiversity conservation, etc. Conventional approaches usually apply the normalized difference vegetation index (NDVI) for vegetation detection. In this paper, we investigate the performance of deep learning and conventional methods for vegetation detection. Two deep learning methods, DeepLabV3+ and our customized convolutional neural network (CNN) were evaluated with respect to their detection performance when training and testing datasets originated from different geographical sites with different image resolutions. A novel object-based vegetation detection approach, which utilizes NDVI, computer …

Truck Activity Pattern Classification Using Anonymous Mobile Sensor Data, Taslima Akter

Graduate Theses and Dissertations

To construct, operate, and maintain a transportation system that supports the efficient movement of freight, transportation agencies must understand economic drivers of freight flow. This is a challenge since freight movement data available to transportation agencies is typically void of commodity and industry information, factors that tie freight movements to underlying economic conditions. With recent advances in the resolution and availability of big data from Global Positioning Systems (GPS), it may be possible to fill this critical freight data gap. However, there is a need for methodological approaches to enable usage of this data for freight planning and operations.

To …

Development Of A Machine Learning-Based Prediction Model For Construction And Environmental Costs Of Trenchless Spray-Applied Pipe Linings, Cured-In-Place Pipe, And Sliplining Methods In Large Diameter Culverts, Ramtin Serajiantehrani

Civil Engineering Dissertations - Archive

According to U.S. Department of Transportation, Federal Highway Administration there are more than 4.1 million miles of road in total length, making it the world’s longest and biggest road network with millions of culverts hidden underneath the road in the United States. The development of underground infrastructure, concerns about environmental impacts, and economic trends are influencing society, resulting in the advancement of technology for more environment-friendly and cost-effective pipeline rehabilitation. Trenchless technologies employ innovative methods, materials, and equipment that require minimum surface excavation and access area for rehabilitation of old and deteriorated culverts. Trenchless technologies can be used when other …

High‑Throughput Analysis Of Leaf Physiological And Chemical Traits With Vis–Nir–Swir Spectroscopy: A Case Study With A Maize Diversity Panel, Yufeng Ge, Abbas Atefi, Huichun Zhang, Chenyong Miao, Raghuprakash Kastoori Ramamurthy, Brandi Sigmon, Jinliang Yang, James C. Schnable

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Hyperspectral reflectance data in the visible, near infrared and shortwave infrared range (VIS–NIR– SWIR, 400–2500 nm) are commonly used to nondestructively measure plant leaf properties. We investigated the usefulness of VIS–NIR–SWIR as a high-throughput tool to measure six leaf properties of maize plants including chlorophyll content (CHL), leaf water content (LWC), specific leaf area (SLA), nitrogen (N), phosphorus (P), and potassium (K). This assessment was performed using the lines of the maize diversity panel. Data were collected from plants grown in greenhouse condition, as well as in the field under two nitrogen application regimes. Leaf-level hyperspectral data were collected with …

Recent Advances In Low-Cost Particulate Matter Sensor: Calibration And Application, Jiayu Li

McKelvey School of Engineering Graduate Student Theses & Dissertations

Particulate matter (PM) has been monitored routinely due to its negative effects on human health and atmospheric visibility. Standard gravimetric measurements and current commercial instruments for field measurements are still expensive and laborious. The high cost of conventional instruments typically limits the number of monitoring sites, which in turn undermines the accuracy of real-time mapping of sources and hotspots of air pollutants with insufficient spatial resolution. The new trends of PM concentration measurement are personalized portable devices for individual customers and networking of large quantity sensors to meet the demand of Big Data. Therefore, low-cost PM sensors have been studied …

Diagnostic Imaging Of Structural Concrete Using Ground Penetrating Radar And Ultrasonic Array, Sina Mehdinia, Thomas Schumacher, Eric Wan, Xubo Song

Student Research Symposium

Structural concrete is the most widely used construction material in the world. Many structures critical to a society such as bridges, hospitals, and airports are built with concrete. While this material is well understood from a mechanical design point of view, still no accurate quantitative tools exist to assess it for damage and deterioration. This is of particular concern for an urban area like Portland with a mega-thrust earthquake waiting to occur. Non-destructive evaluation tools that can quickly and accurately give a full picture of the integrity of structural concrete elements will be key to help plan effective and safe …

Designing An On-Demand Dynamic Crowdshipping Model And Evaluating Its Ability To Serve Local Retail Delivery In New York City, Shirin Najaf Abadi

Dissertations and Theses

Nowadays city mobility is challenging, mainly in populated metropolitan areas. Growing commute demands, increase in the number of for-hire vehicles, enormous escalation in several intra-city deliveries and limited infrastructure (road capacities), all contribute to mobility challenges. These challenges typically have significant impacts on residents’ quality-of-life particularly from an economic and environmental perspective. Decision-makers have to optimize transportation resources to minimize the system externalities (especially in large-scale metropolitan areas). This thesis focus on the intra-city mobility problems experienced by travelers (in the form of congestion and imbalance taxi resources) and businesses (in the form of last-mile delivery), while taking into consideration …

Machine Learning Methods To Map Stabilizer Effectiveness Based On Common Soil Properties, Amit Gajurel

Boise State University Theses and Dissertations

Unconfined compressive strength (UCS) has been widely used as one of the primary criteria for the selection of optimum type and amount of chemical stabilizer for subgrade/base stabilization. Guidelines established by various state and federal agencies aid in selecting these optimum values by recommending an initial type and amount based on a wide range of soil index properties. A significant number of laboratory trials have to be done to establish the optimum type and amount of stabilizer for a given target strength. This process takes a copious amount of time, money, and the workforce. In addition to that, the finite …

Crash Severity Analysis Of Rear-End Crashes In California Using Statistical And Machine Learning Classification Methods, Alidad Ahmadi, Arash Jahangiri, Vincent Berardi, Sahar Ghanipoor Machiani

Psychology Faculty Articles and Research

Investigating drivers’ injury level and detecting contributing factors that aggravate the damage level imposed on drivers and vehicles is a critical subject in the field of crash analysis. In this study, a comprehensive vehicle-by-vehicle crash data set is developed by integrating 5 years of data from California crash, vehicles involved, and road databases. The data set is used to model the severity of rear-end crashes for comparing three analytic techniques: multinomial logit, mixed multinomial logit, and support vector machine (SVM). The results of the crash severity models and the role of contributing factors to the severity outcome of rear-end crashes …

Machine Learning Tools For Optimization Of Fuel Consumption At Signalized Intersections In Connected/Automated Vehicles Environment, Saleh Ragab Mousa

LSU Doctoral Dissertations

Researchers continue to seek numerous techniques for making the transportation sector more sustainable in terms of fuel consumption and greenhouse gas emissions. Among the most effective techniques is Eco-driving at signalized intersections. Eco-driving is a complex control problem where drivers approaching the intersections are guided, over a period of time, to optimize fuel consumption. Eco-driving control systems reduce fuel consumption by optimizing vehicle trajectories near signalized intersections based on information of the SpaT (Signal Phase and Timing). Developing Eco-driving applications for semi-actuated signals, unlike pre-timed, is more challenging due to variations in cycle length resulting from fluctuations in traffic demand. …

Water Quality Factor Prediction Using Supervised Machine Learning, Kathleen Joslyn

REU Final Reports

The objective of this research is to explore prediction accuracy of water quality factors, with techniques and algorithms in machine learning consisting of a variation of support vector machines - Support Vector Regression (SVR) and the gradient boosting algorithm Extreme Gradient Boosting (XGBoost). Both the XGBoost and SVR algorithms were used to predict nine different factors with success rates ranging from 79% to 99%. Parameters of these algorithms were also explored to test the prediction accuracy levels of individual water quality factors. These parameters included normalizing the data, filling missing data points, and training and testing on a large set …

Building Intelligence In The Automated Traffic Signal Performance Measures With Advanced Data Analytics, Tingting Huang, Subhadipto Poddar, Chris Aguilar, Anuj Sharma, Edward J. Smaglik, Sirisha Kothuri, Peter Koonce

Civil and Environmental Engineering Faculty Publications and Presentations

Automated traffic signal performance measures (ATSPMs) are an effort to equip traffic signal controllers with high-resolution data-logging capabilities and utilize this data to generate performance measures. These measures allow practitioners to improve operations as well as to maintain and operate their systems in a safe and efficient manner. Although these measures have changed the way that operators manage their systems, several shortcomings of the tool, identified by talking with signal operators, are a lack of data quality control and the extent of resources required to properly use the tool for system-wide management. To address these shortcomings, intelligent traffic signal performance …