Engineering Commons^™

Predicting The Indirect Cost Of Construction Projects In Egypt: An Artificial Neural Network Approach, Aya Effat

Theses and Dissertations

Cost estimation is one of the vital processes in construction management that needs to be done early in any project in order to determine the project's budget. The accuracy of the cost estimate is a key factor in the success of construction projects since it enables project managers to successfully control the project’s expenses. Construction costs mainly consist of direct cost and indirect cost. Generally, indirect costs can be categorized into two types: site overheads and general overheads. In a construction project, overheads, particularly site overhead costs, make up a considerable portion of a contractor's budget. Accordingly, accurately estimating the …

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.

Application Of Artificial Neural Networks To Predict Local Bridge Pier Scour, Mia Marrocco

Electronic Theses and Dissertations

Accurate equilibrium scour depth and width estimations are essential to both safe and economic designs of bridge foundations. Review of current scour estimation methods demonstrate that the empirical equations produce scour values that are overestimated, resulting in uneconomical designs. In the current investigation, artificial neural networks (ANNs) were optimized and applied to scour data under laboratory conditions, field conditions, and a combination of the two conditions. Additionally, physics-based parameters – in place of empirical parameters (e.g., shape factors) – and parameters incorporating blockage effects were introduced as input parameters to the ANNs in an attempt to improve scour predictions. Finally, …

Improving Probabilistic Quantitative Precipitation Forecasting Using Machine Learning And Statistical Postprocessing Methods, Mohammadvaghef Ghazvinian

Civil Engineering Dissertations

The objective of this research is to address the limitations inherent in conventional statistical postprocessing schemes to generate probabilistic quantitative precipitation forecasts (PQPFs) and improve postprocessed PQPFs quality through introducing new robust statistical models and machine learning frameworks. This dissertation comprises three main elements. First, a new, two-part scheme for creating PQPFs from single-valued quantitative precipitation forecasts is introduced. This scheme, herein referred to as the Mixed-type Non-Homogenous Regression (MNHR), combines the use of logistic regression for estimating rainfall intermittency, and non-homogeneous regression for estimation of additional parameters of the conditional distribution. The performance of MNHR is evaluated relative to …

Application Of Data-Driven And Process-Based Modeling Approaches For Water Quality Simulation In Lakes And Freshwater Reservoirs, Ali Saber Sichani

UNLV Theses, Dissertations, Professional Papers, and Capstones

Lakes and freshwater reservoirs often serve as the primary drinking and irrigation water sources for surrounding communities. They provide recreational and tourism opportunities, thereby promoting the prosperity of neighboring communities. Reliable estimates of water quality in lakes and reservoirs can improve management practices to protect water resources.

Seasonal water temperature and solar shortwave radiation variations, and their subsequent interactions with water column aquatic life, combined with seasonal variations of mixing intensity throughout the water column, result in variations of water quality constituents with depth during the annual cycle. The complexity of these variations entails the use of advanced water quality …

Forecasting Harmful Algal Blooms For Western Lake Erie Using Data Driven Machine Learning Techniques, Nicholas L. Reinoso

ETD Archive

Harmful algal blooms (HAB) have been documented for more than a century occurring all over the world. The western Lake Erie has suffered from Cyanobacteria blooms for many decades. There are currently two widely available HAB forecasting models for Lake Erie. The first forecasting model gives yearly peak bloom forecast while the second provides weekly short-term forecasting and offers size as well as location. This study focuses on bridging the gap of these two models and improve HAB forecast accuracy in western Lake Erie by letting historical observations tell the behavior of HABs. This study tests two machine learning techniques, …

Comprehensive Neural Network Forecasting System For Ground Level Ozone In Multiple Regions, Gautam Raghavendra Eapi

Civil Engineering Dissertations

A comprehensive neural network daily maximum 8 hour-ozone forecasting model was developed based on five years of data (2010-2014) collected from 50 monitoring sites from the Dallas Fort Worth, Houston-Galveston-Brazoria, Los Angeles, San Joaquin and San Diego regions. This work represents the first neural network developed to forecast ozone in multiple regions, as well as multiple sites in the same region. Previous studies have developed separate neural network models to forecast ozone at each location. Two stages of feature selection were applied to reduce input vector dimension and redundancy. These are Piecewise Linear Orthonormal Floating Search (PLOFS), and Karhunen - …

Vibration Isolation Using In-Filled Geofoam Trench Barriers, Ashref Mohamed A. Alzawi

Electronic Thesis and Dissertation Repository

A significant amount of numerical and experimental research has been conducted to study the vibration isolation by wave barriers considering open trenches, in-filled concrete or bentonite trenches, sheet-pile walls, and rows of piles. A few studies have investigated the use of expanded polystyrene (EPS) geofoam material as wave barriers, which indicated that in-filled geofoam trenches can be used as effective wave barriers. However, no engineering design method is available to date for the design of such type of wave barriers. This dissertation presents comprehensive experimental and numerical investigations on the use of in-filled geofoam trench barriers to scatter machine foundations …

A Probabilistic Approach For Modeling And Real-Time Filtering Of Freeway Detector Data, Shourie Kondagari

LSU Master's Theses

Traffic surveillance systems are a key component for providing information on traffic conditions and supporting traffic management functions. A large amount of data is currently collected from inductive loop detector systems in the form of three macroscopic traffic parameters (speed, volume and occupancy). Such information is vital to the successful implementation of transportation data warehouses and decision support systems. The quality of data is, however, affected by erroneous observations that result from malfunctioning or mis-calibration of detectors. The open literature shows that little effort has been made to establish procedures for screening traffic observations in real-time. This study presents a …

A Hybrid Model-Based And Memory-Based Short-Term Traffic Prediction System, Ciprian Danut Alecsandru

LSU Master's Theses

Short-term traffic forecasting capabilities on freeways and major arterials have received special attention in the past decade due primarily to their vital role in supporting various travelers' trip decisions and traffic management functions. This research presents a hybrid model-based and memory-based methodology to improve freeway traffic prediction performance. The proposed methodology integrates both approaches to strengthen predictions under both recurrent and non-recurrent conditions. The model-based approach relies on a combination of static and dynamic neural network architectures to achieve optimal prediction performance under various input and traffic condition settings. Concurrently, the memory-based component is derived from the data archival system …