Engineering Commons^™

Automated Approach For The Enhancement Of Scaffolding Structure Monitoring With Strain Sensor Data, Sayan Sakhakarmi

UNLV Theses, Dissertations, Professional Papers, and Capstones

Construction researchers have made a significant effort to improve the safety of scaffolding structures, as a large proportion of workers are involved in construction activities requiring scaffolds. However, most past studies focused on design and planning aspects of scaffolds. While limited studies investigated scaffolding safety during construction, they are limited to simple cases only with limited failure modes and simple scaffolds. In response to this limitation, this study aims to develop an automated scaffold monitoring approach capable of monitoring large scaffolds. Accordingly, this study developed an automated scaffold safety monitoring framework that leverages sensor data collected from a scaffold, scaffold …

Predicting Water Quality Vulnerability Under Climate Change With Machine Learning, Khanh Thi Nhu Nguyen

Doctoral Dissertations

Water quality deterioration is a global and pervasive issue due to pollution caused by industrialization, urbanization, agriculturalization, and human population growth in the modern era. This issue is even more challenging in the context of climate change due to warming temperatures and the intensification of precipitation. Therefore, assessing the potential impacts of climate change on water quality is a concern. Assessment is necessary so that planners can prepare for and reduce the negative impacts on water quality. At present, climate change impact assessment frameworks are relatively adolescent. Most studies rely on climate projections from General Circulation Models for simulations of …

Artificial Intelligence In Civil Infrastructure Health Monitoring—Historical Perspectives, Current Trends, And Future Visions, Tarutal Ghosh Mondal, Genda Chen

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Over the past 2 decades, the use of artificial intelligence (AI) has exponentially increased toward complete automation of structural inspection and assessment tasks. This trend will continue to rise in image processing as unmanned aerial systems (UAS) and the internet of things (IoT) markets are expected to expand at a compound annual growth rate of 57.5% and 26%, respectively, from 2021 to 2028. This paper aims to catalog the milestone development work, summarize the current research trends, and envision a few future research directions in the innovative application of AI in civil infrastructure health monitoring. A blow-by-blow account of the …

Ag-Iot For Crop And Environment Monitoring: Past, Present, And Future, Nipuna Chamara, Md Didarul Islam, Geng Bai, Yeyin Shi, Yufeng Ge

Department of Biological Systems Engineering: Papers and Publications

CONTEXT: Automated monitoring of the soil-plant-atmospheric continuum at a high spatiotemporal resolution is a key to transform the labor-intensive, experience-based decision making to an automatic, data-driven approach in agricultural production. Growers could make better management decisions by leveraging the real-time field data while researchers could utilize these data to answer key scientific questions. Traditionally, data collection in agricultural fields, which largely relies on human labor, can only generate limited numbers of data points with low resolution and accuracy. During the last two decades, crop monitoring has drastically evolved with the advancement of modern sensing technologies. Most importantly, the introduction …

Multimodal Imaging Of Structural Concrete Using Image Fusion And Deep Learning, Sina Mehdinia

Dissertations and Theses

Concrete structures may be exposed to a variety of loads and environments during their service life. Non-destructive testing (NDT) techniques can be helpful in evaluating the condition of a structure. Imaging provides a visual representation of the interior of concrete and its condition non-destructively. Ground penetrating radar (GPR) and ultrasonic echo array (UEA) using electromagnetic and stress waves, respectively, provide the data that can be used to reconstruct an image. In this PhD dissertation, image reconstruction and fusion algorithms, simulation, and a deep learning model were investigated with the goal to lay the foundation for enhanced imaging applications for concrete. …

Machine Learning-Enabled Model-Based Condition Assessment Of Water Pipelines By Leveraging Hydraulic Monitoring Data, Ahmad Momeni

All Dissertations

Overpopulation and climate change have direly challenged the freshwater resources, specifically potable water supplied by water distribution networks (WDNs). One aggravating issue associated with the WDNs is associated with the pipeline leakage, which accounts for almost 20% of freshwater loss in WDNs throughout the US. Leakage detection and severity measurement are of top
asset management priorities in water utilities to minimize and mitigate complicated risks attributed to background and burst leakage. Accordingly, decline in other pipe condition parameters such as effective hydraulic diameters and roughness coefficients, which are complex and uncertain in nature, abets leakage by worsening the WDN status …

Evaluation Of Decision-Making Prediction Models For Sewer Pipes Asset Management, Salar Shirkhanloo

Civil Engineering Dissertations

Wastewater collection systems deteriorate over time, requiring continuous adjustments and the development of asset management frameworks on the part of utility owners to maintain the performance of their assets. Any asset management framework should emphasize the importance of asset inspection and condition evaluation for efficient system operation and maintenance. Closed-circuit television (CCTV) is the most widely used tool in the United States for inspecting the interior of sewer pipes, which is a somewhat expensive and time-consuming process given the extensive inventory of pipes in a city. Due to their vast inventory of these pipes, no municipality can inspect every individual …

Learning From Machines: Insights In Forest Transpiration Using Machine Learning Methods, Morgan Tholl

Dissertations and Theses

Machine learning has been used as a tool to model transpiration for individual sites, but few models are capable of generalizing to new locations without calibration to site data. Using the global SAPFLUXNET database, 95 tree sap flow data sites were grouped using three clustering strategies: by biome, by tree functional type, and through use of a k-means unsupervised clustering algorithm. Two supervised machine learning algorithms, a random forest algorithm and a neural network algorithm, were used to build machine learning models that predicted transpiration for each cluster. The performance and feature importance in each model were analyzed and compared …

Coevolution Of Machine Learning And Process-Based Modelling To Revolutionize Earth And Environmental Sciences: A Perspective, Mojtaba Sadegh

Civil Engineering Faculty Publications and Presentations

Machine learning (ML) applications in Earth and environmental sciences (EES) have gained incredible momentum in recent years. However, these ML applications have largely evolved in ‘isolation’ from the mechanistic, process-based modelling (PBM) paradigms, which have historically been the cornerstone of scientific discovery and policy support. In this perspective, we assert that the cultural barriers between the ML and PBM communities limit the potential of ML, and even its ‘hybridization’ with PBM, for EES applications. Fundamental, but often ignored, differences between ML and PBM are discussed as well as their strengths and weaknesses in light of three overarching modelling objectives in …

Digitalization Of Construction Project Requirements Using Natural Language Processing (Nlp) Techniques, Fahad Ul Hassan

All Dissertations

Contract documents are a critical legal component of a construction project that specify all wishes and expectations of the owner toward the design, construction, and handover of a project. A single contract package, especially of a design-build (DB) project, comprises hundreds of documents including thousands of requirements. Precise comprehension and management of the requirements are critical to ensure that all important explicit and implicit requirements of the project scope are captured, managed, and completed. Since requirements are mainly written in a natural human language, the current manual methods impose a significant burden on practitioners to process and restructure them into …

Using Safety Performance Models, Autonomous Vehicle Data, And Machine Learning To Develop Contextual Complexity Criteria To Establish A Standardized Process For On-Road Evaluation Of Medically At-Risk Drivers Considering Static And Dynamic Factors Of The Roadway Environment, Vijay Bendigeri

All Dissertations

The field of transportation engineering has an opportunity to positively impact the medical community, specifically the clinicians who evaluate, train, and rehabilitate at-risk drivers. Driving Rehabilitation Specialists (DRSs) have an essential role in making roads safer for medically-at-risk drivers, their passengers, and other road users. DRSs conduct on-road driving evaluations, which are considered the gold standard to make fitness-to-drive decisions due to their high face validity. Most DRSs use a fixed route, meaning the exact same route is used to evaluate each client. When a DRS develops a fixed route, that clinician identifies characteristics of the roadway they think are …

Automated Quality Control For In-Situ Water Temperature Sensors, Leah S. Richardson

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The identification of data not representative of the target subject for outdoor (in-situ) environmental sensors (bad data) is a topic that has been explored in the past. Many tools (such as data filters and computer models) have succeeded in providing an end user with properly identified incorrect data over 95% of the time. However, with the continuous increase in the use of automated data collection, a simple indication of the bad data may no longer provide the end user with enough information to reduce the amount of time that must be spent for manual quality control. The purpose of this …

Data-Driven Framework For Understanding & Modeling Ride-Sourcing Transportation Systems, Bishoy Kelleny

Civil & Environmental Engineering Theses & Dissertations

Ride-sourcing transportation services offered by transportation network companies (TNCs) like Uber and Lyft are disrupting the transportation landscape. The growing demand on these services, along with their potential short and long-term impacts on the environment, society, and infrastructure emphasize the need to further understand the ride-sourcing system. There were no sufficient data to fully understand the system and integrate it within regional multimodal transportation frameworks. This can be attributed to commercial and competition reasons, given the technology-enabled and innovative nature of the system. Recently, in 2019, the City of Chicago the released an extensive and complete ride-sourcing trip-level data for …

Quantitative Evaluation Of Steel Corrosion Induced Deterioration In Rubber Concrete By Integrating Ultrasonic Testing, Machine Learning And Mesoscale Simulation, Jinrui Zhang, Mengxi Zhang, Biqin Dong, Hongyan Ma

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Chloride-induced steel corrosion seriously affects the durability of reinforced concrete structures. Rubber concrete, an environmentally friendly construction material in which waste rubber is recycled as a concrete component, has demonstrated superior resistance to chloride-induced steel corrosion and the subsequent concrete deterioration. However, quantitative evaluation of the degree of deterioration in rubber concrete based on nondestructive detection is challenging due to the complexity of the material. In this paper, reinforced concrete specimens with rubber contents of 0, 10% and 20% are subjected to the electrochemically accelerated corrosion experiments and monitored by ultrasonic testing. Six machine learning models are trained by the …

Wastewater Aeration Process Dynamic Modelling: Combined Mechanistic And Machine Learning Approach, Yuehe Pan

Electronic Thesis and Dissertation Repository

The aeration process is the largest energy consumer in wastewater treatment plants (WWTPs), and the optimization of the process based on computational models can offer significant savings for the plant. Recent theoretical developments have revealed that many of the parameters commonly assumed as constants in aeration modelling, in fact, have a dynamic nature; however, there still lacks a universal way to model these factors in an easy, accurate and timely manner. This work proposed a machine learning-based modelling approach to offer real-time estimations of the oxygen transfer rate, airflow demand, and energy consumption.

Utilizing the field data collected from Adelaide …

Application Of Machine Learning To Predict The Performance Of An Emipg Reactor Using Data From Numerical Simulations, Owen Sedej, Eric G. Mbonimpa, Trevor Sleight, Jeremy Slagley

Faculty Publications

Microwave-driven plasma gasification technology has the potential to produce clean energy from municipal and industrial solid wastes. It can generate temperatures above 2000 K (as high as 30,000 K) in a reactor, leading to complete combustion and reduction of toxic byproducts. Characterizing complex processes inside such a system is however challenging. In previous studies, simulations using computational fluid dynamics (CFD) produced reproducible results, but the simulations are tedious and involve assumptions. In this study, we propose machine-learning models that can be used in tandem with CFD, to accelerate high-fidelity fluid simulation, improve turbulence modeling, and enhance reduced-order models. A two-dimensional …

Application Of Machine Learning Models With Numerical Simulations Of An Experimental Microwave Induced Plasma Gasification Reactor, Owen D. Sedej

Theses and Dissertations

This thesis aims to contribute to the future development of this technology by providing an in-depth literature review of how this technology physically operates and can be numerically modeled. Additionally, this thesis reviews literature of machine learning models that have been applied to gasification to make accurate predictions regarding the system. Finally, this thesis provides a framework of how to numerically model an experimental plasma gasification reactor in order to inform a variety of machine learning models.

Supervised Machine Learning Techniques Applied To Low-Cost Air Quality Sensor Suites, Peter Wahman

All Undergraduate Theses and Capstone Projects

Low-cost PM sensors have garnered interest for their ability to reduce the cost of investigating PM concentrations in both indoor and outdoor spaces. They perform well in high concentration lab testing with correlation coefficients greater than 0.9. In real-world applications, the correlation coefficients drop significantly because of sensing floors and adverse ambient conditions. There are plenty of supervised machine learning techniques that aim to correct the measurements ranging from linear regression to more advanced neural networks and random forests. This work aims to use those more complicated techniques to adjust the measurements using other data sets gathered by a sensor …

A Citizen-Science Approach For Urban Flood Risk Analysis Using Data Science And Machine Learning, Candace Agonafir

Dissertations and Theses

Street flooding is problematic in urban areas, where impervious surfaces, such as concrete, brick, and asphalt prevail, impeding the infiltration of water into the ground. During rain events, water ponds and rise to levels that cause considerable economic damage and physical harm. The main goal of this dissertation is to develop novel approaches toward the comprehension of urban flood risk using data science techniques on crowd-sourced data. This is accomplished by developing a series of data-driven models to identify flood factors of significance and localized areas of flood vulnerability in New York City (NYC). First, the infrastructural (catch basin clogs, …

Assessing Machine Learning Utility In Predicting Hydrologic And Nitrate Dynamics In Karst Agroecosystems, Timothy Mcgill

Theses and Dissertations--Biosystems and Agricultural Engineering

Seasonal hypoxia in the Gulf of Mexico and harmful algal blooms experienced in many inland freshwater bodies is partially driven due to excessive nitrogen loading seen from agricultural watersheds. Within the Mississippi/Atchafalaya River Basin, many areas are underlain with karst features, and efforts to reduce nitrogen contributions from these areas have had varying success, due to lacking a complete understanding of nutrient dynamics in karst agricultural systems. To improve the understanding of nitrogen cycling in these systems, 35 months of high resolution in situ water quality and atmospheric data were collected and fed into a two-hidden layer extreme learning machine …

An Intelligent Approach For Predicting The Strength Of Geosynthetic-Reinforced Subgrade Soil, Muhammad Nouman Amjad Raja, Sanjay K. Shukla, Muhammad Umer Arif Khan

Research outputs 2014 to 2021

In the recent times, the use of geosynthetic-reinforced soil (GRS) technology has become popular for constructing safe and sustainable pavement structures. The strength of the subgrade soil is routinely assessed in terms of its California bearing ratio (CBR). However, in the past, no effort was made to develop a method for evaluating the CBR of the reinforced subgrade soil. The main aim of this paper is to explore and appraise the competency of the several intelligent models such as artificial neural network (ANN), least median of squares regression, Gaussian processes regression, elastic net regularisation regression, lazy K-star, M-5 model …