Alternative Supplementary Cementitious Materials In Ultra-High Performance Concrete,
2022
New Mexico State University
Alternative Supplementary Cementitious Materials In Ultra-High Performance Concrete, Craig Newtson, Seyedsaleh Mousavinezhad, Gregory J, Gonzales, William K. Toledo, Judit M. Garcia
Data
Ultra-high performance concrete (UHPC) is an emerging material with remarkable mechanical and durability properties that contains large amounts of cementitious materials. Silica fume is a main supplementary cementitious material (SCM) in UHPC, however, it is more expensive than cement and other SCMs, so it is often substituted with inexpensive class F fly ash. Unfortunately, future availability of fly ash is uncertain as the energy industry moves toward renewable energy. Fly ash shortages create an urgent need to find cost-effective and environmentally-friendly alternatives for fly ash. This study investigated replacing cement, fly ash, and silica fume in UHPC mixtures with ground …
Effectiveness Assessment Of E-Ticketing Technology In Construction Of Transportation Projects,
2022
Louisiana State University
Effectiveness Assessment Of E-Ticketing Technology In Construction Of Transportation Projects, Sharareh Kermanshachi, Karthik Subramanya
Data
The construction of highway infrastructure has devoted significant resources towards e-Construction to reduce the paperwork and automate the tasks in daily operations. Electronic Ticketing (e-Ticketing) is one such component of e-Construction that aids in the digital transfer of material tickets such as asphalt and concrete which accounts for more than fifty per cent of construction costs. Despite the benefits of e-Ticketing, many state departments and agencies are unwilling to transition into this technology. No studies have identified the cause of the delay in the implementation process, developed a framework to comprehend the platform's full potential, quantified savings, and suggested strategies …
Development Of A Machine Learning-Based Model To Determine The Optimum And Safe Restriping Timing Of Thermoplastic Pavement Markings In Hot And Humid Climates,
2022
Louisiana State University and Agricultural and Mechanical College
Development Of A Machine Learning-Based Model To Determine The Optimum And Safe Restriping Timing Of Thermoplastic Pavement Markings In Hot And Humid Climates, Momen R. Mousa, Marwa Hassan
Publications
Due to limited budget, most transportation agencies restripe their thermoplastic pavement markings based on a fixed schedule or based on visual inspection instead of monitoring the retroreflectivity and restriping when the retroreflectivity drops below a pre-determined threshold. These strategies are questionable in terms of efficiency and economy. Therefore, previous studies proposed degradation models to predict the retroreflectivity of thermoplastic markings based on key variables. Yet, most of these studies reported low R2 (as low as 0.1), which placed little confidence in these models. Therefore, the objective of this study was to evaluate and predict the field performance of thermoplastics …
Development Of A Machine Learning-Based Model To Determine The Optimum And Safe Restriping Timing Of Thermoplastic Pavement Markings In Hot And Humid Climates,
2022
Louisiana State University and Agricultural and Mechanical College
Development Of A Machine Learning-Based Model To Determine The Optimum And Safe Restriping Timing Of Thermoplastic Pavement Markings In Hot And Humid Climates, Momen R. Mousa, Marwa Hassan
Data
Due to limited budget, most transportation agencies restripe their thermoplastic pavement markings based on a fixed schedule or based on visual inspection instead of monitoring the retroreflectivity and restriping when the retroreflectivity drops below a pre-determined threshold. These strategies are questionable in terms of efficiency and economy. Therefore, previous studies proposed degradation models to predict the retroreflectivity of thermoplastic markings based on key variables. Yet, most of these studies reported low R2 (as low as 0.1), which placed little confidence in these models. Therefore, the objective of this study was to evaluate and predict the field performance of thermoplastics …
Alternative Supplementary Cementitious Materials In Ultra-High Performance Concrete,
2022
New Mexico State University
Alternative Supplementary Cementitious Materials In Ultra-High Performance Concrete, Craig Newtson, Seyedsaleh Mousavinezhad, Gregory J. Gonzales, William K. Toledo, Judit M. Garcia
Publications
Ultra-high performance concrete (UHPC) is an emerging material with remarkable mechanical and durability properties that contains large amounts of cementitious materials. Silica fume is a main supplementary cementitious material (SCM) in UHPC, however, it is more expensive than cement and other SCMs, so it is often substituted with inexpensive class F fly ash. Unfortunately, future availability of fly ash is uncertain as the energy industry moves toward renewable energy. Fly ash shortages create an urgent need to find cost-effective and environmentally-friendly alternatives for fly ash. This study investigated replacing cement, fly ash, and silica fume in UHPC mixtures with ground …
A Deep Learning Tool For The Assessment Of Pavement Smoothness And Aggregate Segregation During Construction,
2022
Louisiana State University
A Deep Learning Tool For The Assessment Of Pavement Smoothness And Aggregate Segregation During Construction, Mostafa Elseifi, Ramchandra Paudel, Md Tanvir Ahmed Sarkar, Hossam Abohamer, Nirmal Dhakal
Data
Pavement construction monitoring and quality assurance (QA) practices are mostly based on costly, discrete, and destructive methods. Most quality assurance programs are based on pavement construction procedures encompassing in-situ coring for layer thickness determination, density measurements, laboratory testing to measure volumetric properties, and smoothness measurements in case of the availability of a profiler. The main objective of this study was to develop a machine learning-based classifier for predicting pavement roughness and aggregate segregation based on digital image analysis, image recognition, and deep learning machine models. The developed Convolution Neural Networks (CNN) models were trained, tested, and validated using 600-pavement surface …
Using Rice Husk Ash (Rha) As Stabilizing Agent For Problematic Subgrade Soils And Embankments,
2022
Arkansas State University
Using Rice Husk Ash (Rha) As Stabilizing Agent For Problematic Subgrade Soils And Embankments, Zahid Hossain, Rifat Bulut, Fares Tarhuni, Hussein Al-Dakheeli
Data
Arkansas produces the most of the rice in the United States. About 20% of poddy is rice husk (RH), which is burnt under controlled conditions to produce rice rusk ash (RHA). The RHA is considered an environmental hazard and a significant challenge for rice millers. However, RHA is rich in pozzolanic material, which is mainly silica. In this study, RHA is used to stabilize poor soils. Another commonly used stabilizer, hydrated lime (HL), has also been evaluated for comparison purposes. Thus, this study aimed to determine the optimum percentages of RHA, HL, or a combination of these two agents by …
Performance Monitoring Leveraging Advanced Ai Technique With Cnn,
2022
University of Texas at Arlington
Performance Monitoring Leveraging Advanced Ai Technique With Cnn, Suyun Ham Ph.D, Stefan Romanoschi, Yin Chao Wu, Dafnik Saril Kumar David, Sanggoo Kang
Publications
The main goal of this project is to study and develop a reliable nondestructive testing (NDT)-based structural performance prediction model framework leveraging the advanced machine learning convolutional neural network (CNN) technique and rapid crack evaluation system. There are two steps of application CNN technique in this project: 1) the first step is to identify delamination, noise, and the unexpected signal produced by the existing damage identification algorithm to improve the accuracy of NDT results. The input image or training data of NDT data for CNN is comprehensively studied with several features, such as the duration of the signal, the starting …
Increasing Bridge Durability And Service Life With Lidar Enhanced Unmanned Aerial Systems (Uas),
2022
University of New Mexico
Increasing Bridge Durability And Service Life With Lidar Enhanced Unmanned Aerial Systems (Uas), Fernando Moreu, Mahsa Sanei, Chris Lippitt
Data
Bridge construction inspections require quantitative measurements and location information. The conventional approach is visual inspection, which in general, is rather time-consuming, expensive due to traffic closure, subjective, and needs special access. Therefore an automated rebar layout detection algorithm was developed to quickly extract quantitative rebar layout information from the LiDAR data. This systematic method can automatically cluster the bridge elements from a 3D point cloud by using LiDAR-equipped UAS data collection and unsupervised machine learning techniques. A new automated inspection system using a LIDAR-equipped UAS can eventually if developed and tested be more reliable as well as less expensive. In …
Performance Monitoring Leveraging Advanced Ai Technique With Cnn,
2022
University of Texas at Arlington
Performance Monitoring Leveraging Advanced Ai Technique With Cnn, Suyun Ham Ph.D, Stefan Romanoschi, Yin Chao Wu, Dafnik Saril Kumar David, Sanggoo Kang
Data
The main goal of this project is to study and develop a reliable nondestructive testing (NDT)-based structural performance prediction model framework leveraging the advanced machine learning convolutional neural network (CNN) technique and rapid crack evaluation system. There are two steps of application CNN technique in this project: 1) the first step is to identify delamination, noise, and the unexpected signal produced by the existing damage identification algorithm to improve the accuracy of NDT results. The input image or training data of NDT data for CNN is comprehensively studied with several features, such as the duration of the signal, the starting …
Development Of Distress Index Prediction Models For Rehabilitation Treatments In Louisiana Using Advanced Machine Learning Techniques,
2022
Louisiana State University and Agricultural and Mechanical College
Development Of Distress Index Prediction Models For Rehabilitation Treatments In Louisiana Using Advanced Machine Learning Techniques, Momen R. Mousa, Marwa Hassan
Data
Performance prediction models are used by state agencies to predict future trends in distress indices, hence, determining the required maintenance and/or rehabilitation treatment as well as the deterioration rate and remaining pavement service life. However, most of these models are based on a limited number of parameters and cannot predict the performance distress indices reliably. Such limitation resulted in having, most of the time, a maximum prediction period of five years. As a solution and coping with the ever-increasing size of pavement data, machine learning techniques have become a promising alternative. The objective of this study was to develop a …
Covid-19 And Traffic Safety: Exploring Exposure, Crash Frequency And Severity, And Roadway And Network Design,
2022
University of New Mexico
Covid-19 And Traffic Safety: Exploring Exposure, Crash Frequency And Severity, And Roadway And Network Design, Nicholas N. Ferenchak Ph.D
Publications
Early COVID-19 lockdowns in the first half of 2020 largely kept people at home, thereby reducing motor vehicle traffic levels. Theoretically, reduced traffic exposure should have resulted in reduced motor vehicle crashes. However, a variety of factors may have complicated this relationship. In order to better understand the impact of COVID-19 lockdowns on traffic safety outcomes, we explore fatalities, injuries, and total crashes before and during the lockdowns on both the national and state levels. We provide descriptive statistics and create negative binomial regressions exploring the role of vehicle, user, and built environment factors on traffic safety outcomes. Findings suggest …
Covid-19 And Traffic Safety: Exploring Exposure, Crash Frequency And Severity, And Roadway And Network Design,
2022
University of New Mexico
Covid-19 And Traffic Safety: Exploring Exposure, Crash Frequency And Severity, And Roadway And Network Design, Nicholas N. Ferenchak Ph.D
Data
Early COVID-19 lockdowns in the first half of 2020 largely kept people at home, thereby reducing motor vehicle traffic levels. Theoretically, reduced traffic exposure should have resulted in reduced motor vehicle crashes. However, a variety of factors may have complicated this relationship. In order to better understand the impact of COVID-19 lockdowns on traffic safety outcomes, we explore fatalities, injuries, and total crashes before and during the lockdowns on both the national and state levels. We provide descriptive statistics and create negative binomial regressions exploring the role of vehicle, user, and built environment factors on traffic safety outcomes. Findings suggest …
Increasing Bridge Durability And Service Life With Lidar Enhanced Unmanned Aerial Systems (Uas),
2022
University of New Mexico
Increasing Bridge Durability And Service Life With Lidar Enhanced Unmanned Aerial Systems (Uas), Fernando Moreu, Mahsa Sanei, Chris Lippitt
Publications
Bridge construction inspections require quantitative measurements and location information. The conventional approach is visual inspection, which in general, is rather time-consuming, expensive due to traffic closure, subjective, and needs special access. Therefore an automated rebar layout detection algorithm was developed to quickly extract quantitative rebar layout information from the LiDAR data. This systematic method can automatically cluster the bridge elements from a 3D point cloud by using LiDAR-equipped UAS data collection and unsupervised machine learning techniques. A new automated inspection system using a LIDAR-equipped UAS can eventually if developed and tested be more reliable as well as less expensive. In …
Krs And Kar Review Of Models As A Legal Contract Document,
2022
University of Kentucky
Krs And Kar Review Of Models As A Legal Contract Document, Bryan Gibson, Pam Clay-Young, Rachel Catchings, Chris Van Dyke
Kentucky Transportation Center Research Report
State departments of transportation (DOTs) are expanding the use of electronic engineering data (EED) throughout highway projects — from design and construction through asset management. Included under the umbrella of EED are technologies such as building information modelling (BIM), digital terrain models (DTMs), and 3D models and plan sets. The Kentucky Transportation Cabinet’s (KYTC) Digital Project Delivery (DPD) Initiative is spearheading the transition to EED in the state. While digital delivery promises to streamline project development and management it does not come without hurdles. This report discusses methods for agency wide implementation of EED and highlights best practices for managing, …
Effectiveness Assessment Of E-Ticketing Technology In Construction Of Transportation Projects,
2022
Louisiana State University
Effectiveness Assessment Of E-Ticketing Technology In Construction Of Transportation Projects, Sharareh Kermanshachi, Karthik Subramanya
Publications
The construction of highway infrastructure has devoted significant resources towards e-Construction to reduce the paperwork and automate the tasks in daily operations. Electronic Ticketing (e-Ticketing) is one such component of e-Construction that aids in the digital transfer of material tickets such as asphalt and concrete which accounts for more than fifty per cent of construction costs. Despite the benefits of e-Ticketing, many state departments and agencies are unwilling to transition into this technology. No studies have identified the cause of the delay in the implementation process, developed a framework to comprehend the platform's full potential, quantified savings, and suggested strategies …
Influence Of Specimen Geometry And Anisotropy On Dynamic Modulus Of Asphalt Mixes In South Carolina,
2022
Clemson University
Influence Of Specimen Geometry And Anisotropy On Dynamic Modulus Of Asphalt Mixes In South Carolina, Srinivasan Nagarajan
All Dissertations
The objective of this study was to characterize the variability of dynamic modulus of asphalt mixes in South Carolina on the basis of geometry and anisotropy. High priority mixes Surface Type B, and C; Intermediate Type B and C and Base Type A from three different days of production were collected from seven different contractors each having a different aggregate source and the dynamic modulus was measured using the Asphalt Mixture Performance Tester (AMPT) at temperatures of 40, 70, 100 and 130℉ (4.4, 21.1, 37.8, and 54.4℃) and at frequencies of 25, 10, 5, 1, 0.5, and 0.1 Hz. One-way …
Development Of Distress Index Prediction Models For Rehabilitation Treatments In Louisiana Using Advanced Machine Learning Techniques,
2022
Louisiana State University and Agricultural and Mechanical College
Development Of Distress Index Prediction Models For Rehabilitation Treatments In Louisiana Using Advanced Machine Learning Techniques, Momen R. Mousa, Marwa Hassan
Publications
Performance prediction models are used by state agencies to predict future trends in distress indices, hence, determining the required maintenance and/or rehabilitation treatment as well as the deterioration rate and remaining pavement service life. However, most of these models are based on a limited number of parameters and cannot predict the performance distress indices reliably. Such limitation resulted in having, most of the time, a maximum prediction period of five years. As a solution and coping with the ever-increasing size of pavement data, machine learning techniques have become a promising alternative. The objective of this study was to develop a …
Using Rice Husk Ash (Rha) As Stabilizing Agent For Problematic Subgrade Soils And Embankments,
2022
Arkansas State University
Using Rice Husk Ash (Rha) As Stabilizing Agent For Problematic Subgrade Soils And Embankments, Zahid Hossain, Rifat Bulut, Fares Tarhuni, Hussein Al-Dakheeli
Publications
Arkansas produces the most of the rice in the United States. About 20% of poddy is rice husk (RH), which is burnt under controlled conditions to produce rice rusk ash (RHA). The RHA is considered an environmental hazard and a significant challenge for rice millers. However, RHA is rich in pozzolanic material, which is mainly silica. In this study, RHA is used to stabilize poor soils. Another commonly used stabilizer, hydrated lime (HL), has also been evaluated for comparison purposes. Thus, this study aimed to determine the optimum percentages of RHA, HL, or a combination of these two agents by …
A Deep Learning Tool For The Assessment Of Pavement Smoothness And Aggregate Segregation During Construction,
2022
Louisiana State University
A Deep Learning Tool For The Assessment Of Pavement Smoothness And Aggregate Segregation During Construction, Mostafa Elseifi, Ramchandra Paudel, Md Tanvir Ahmed Sarkar, Hossam Abohamer, Nirmal Dhakal
Publications
Pavement construction monitoring and quality assurance (QA) practices are mostly based on costly, discrete, and destructive methods. Most quality assurance programs are based on pavement construction procedures encompassing in-situ coring for layer thickness determination, density measurements, laboratory testing to measure volumetric properties, and smoothness measurements in case of the availability of a profiler. The main objective of this study was to develop a machine learning-based classifier for predicting pavement roughness and aggregate segregation based on digital image analysis, image recognition, and deep learning machine models. The developed Convolution Neural Networks (CNN) models were trained, tested, and validated using 600-pavement surface …
