Development of Void Prediction Models for Kansas Concrete Mixes Used in PCC Pavement

AbstractPermeability of the concrete material used in Portland Cement Concrete (PCC) pavement structures is a major factor for long-term durability assessment. To properly characterize the permeability response of a PCC pavement structure, the Kansas Department of Transportation (KDOT) generally runs the Boil Test (BT) to determine the % void response. The BT typically measures the volume of permeable pore space within the concrete samples over a period of five hours at a concrete age of 7, 28, and 56 days. In this study, backpropagation Artificial Neural Network- (ANN) and Regression-based % void response prediction models for the BT are developed by using the database provided by KDOT in order to reduce the duration of the testing period or ultimately eliminating the need to conduct the BT. The noted excellent prediction accuracy of the developed models proved that the ANN and the Regression models have efficiently characterized the BT response. Therefore, they can be considered as effective and applicable models to predict the permeability (% void response) response of concrete mixes used in PCC pavements

Characterizing the permeability of concrete mixes used in transportation applications: a neuronet approach

Master of ScienceDepartment of Civil EngineeringYacoub M. NajjarReliable and economical design of Portland Cement Concrete (PCC) pavement structural systems relies on various factors, among which is the proper characterization of the expected permeability response of the concrete mixes. Permeability is a highly important factor which strongly relates the durability of concrete structures and pavement systems to changing environmental conditions. One of the most common environmental attacks which cause the deterioration of concrete structures is the corrosion of reinforcing steel due to chloride penetration. On an annual basis, corrosion-related structural repairs typically cost millions of dollars. This durability problem has gotten widespread interest in recent years due to its incidence rate and the associated high repair costs. For this reason, material characterization is one of the best methods to reduce repair costs. To properly characterize the permeability response of PCC pavement structure, the Kansas Department of Transportation (KDOT) generally runs the Rapid Chloride Permeability test to determine the resistance of concrete to penetration of chloride ions as well as the Boil test to determine the percent voids in hardened concrete. Rapid Chloride test typically measures the number of coulombs passing through a concrete sample over a period of six hours at a concrete age of 7, 28, and 56 days. Boil Test measures the volume of permeable pore space of the concrete sample over a period of five hours at a concrete age of 7, 28, and 56 days. In this research, backpropagation Artificial Neural Network (ANN)-based and Regression-based permeability response prediction models for Rapid Chloride and Boil tests are developed by using the databases provided by KDOT in order to reduce or eliminate the duration of the testing period. Moreover, another set of ANN- and Regression-based permeability prediction models, based on mix-design parameters, are developed using datasets obtained from the literature. The backpropagation ANN learning technique proved to be an efficient methodology to produce a relatively accurate permeability response prediction models. Comparison of the prediction accuracy of the developed ANN models and regression models proved that ANN models have outperformed their counterpart regression-based models. Overall, it can be inferred that the developed ANN-Based permeability prediction models are effective and applicable in characterizing the permeability response of concrete mixes used in transportation applications

Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks

Geotechnical investigation plays an indispensable role in site characterization and provides necessary data for various construction projects. However, geotechnical measurements are time-consuming, point-based, and invasive. Non-destructive geophysical measurements (seismic wave velocity) can complement geotechnical measurements to save project money and time. However, correlations between geotechnical and seismic wave velocity are crucial in order to maximize the benefit of geophysical information. In this work, artificial neural networks (ANNs) models are developed to forecast geotechnical parameters from seismic wave velocity. Specifically, published seismic wave velocity, liquid limit, plastic limit, water content, and dry density from field and laboratory measurements are used to develop ANN models. Due to the small number of data, models are developed with and without the validation step in order to use more data for training. The results indicate that the performance of the models is improved by using more data for training. For example, predicting seismic wave velocity using more data for training improves the R2 value from 0.50 to 0.78 and reduces the ASE from 0.0174 to 0.0075, and MARE from 30.75 to 18.53. The benefit of adding velocity as an input parameter for predicting water content and dry density is assessed by comparing models with and without velocity. Models incorporating the velocity information show better predictability in most cases. For example, predicting water content using field data including the velocity improves the R2 from 0.75 to 0.85 and reduces the ASE from 0.0087 to 0.0051, and MARE from 10.68 to 7.78. A comparison indicates that ANN models outperformed multilinear regression models. For example, predicting seismic wave velocity using field plus lab data has an ANN derived R2 value that is 81.39% higher than regression model

Highway Pavement Condition Deterioration Modeling considering Maintenance History

A nation\u2019s economy and prosperity depend on an efficient and safe transportation network for public mobility and freight transportation. A country\u2019s road network is recognized as one of the largest public infrastructure assets. About 93 percent of 2.6 million miles of paved roads and highways in the United States (U.S.) are surfaced with asphalt. Longitudinal roughness, pavement cracking, potholes, and rutting are the major reasons for the rehabilitation of asphalt roads. Billions of dollars are required annually for the maintenance and rehabilitation of road networks. If timely maintenance and rehabilitation are not performed, the pavement damages inflicted by heavy traffic repetitions and environmental impacts may lead to life-threatening conditions for road users. This report is focused on asphalt pavement condition deterioration progression modeling and computational simulations of uncracked and cracked asphalt pavement-subgrade models. The research objectives are to (1) evaluate and enhance asphalt pavement condition deterioration prediction models, (2) evaluate modulus backcalculation approaches for characterizing asphalt pavement layers of selected test sections, (3) develop three dimensional-finite elements (3D-FE) asphalt pavement models and study impacts of cracking on pavement structural responses, and (4) implement pavement condition deterioration models for improved structural design and asset management of asphalt highway pavements

Decision making in engineering prediction systems

Doctor of PhilosophyDepartment of Civil EngineeringYacoub M. NajjarAccess to databases after the digital revolutions has become easier because large databases are progressively available. Knowledge discovery in these databases via intelligent data analysis technology is a relatively young and interdisciplinary field. In engineering applications, there is a demand for turning low-level data-based knowledge into a high-level type knowledge via the use of various data analysis methods. The main reason for this demand is that collecting and analyzing databases can be expensive and time consuming. In cases where experimental or empirical data are already available, prediction models can be used to characterize the desired engineering phenomena and/or eliminate unnecessary future experiments and their associated costs. Phenomena characterization, based on available databases, has been utilized via Artificial Neural Networks (ANNs) for more than two decades. However, there is a need to introduce new paradigms to improve the reliability of the available ANN models and optimize their predictions through a hybrid decision system. In this study, a new set of ANN modeling approaches/paradigms along with a new method to tackle partially missing data (Query method) are introduced for this purpose. The potential use of these methods via a hybrid decision making system is examined by utilizing seven available databases which are obtained from civil engineering applications. Overall, the new proposed approaches have shown notable prediction accuracy improvements on the seven databases in terms of quantified statistical accuracy measures. The proposed new methods are capable in effectively characterizing the general behavior of a specific engineering/scientific phenomenon and can be collectively used to optimize predictions with a reasonable degree of accuracy. The utilization of the proposed hybrid decision making system (HDMS) via an Excel-based environment can easily be utilized by the end user, to any available data-rich database, without the need for any excessive type of training

Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks

Geotechnical investigation plays an indispensable role in site characterization and provides necessary data for various construction projects. However, geotechnical measurements are time-consuming, point-based, and invasive. Non-destructive geophysical measurements (seismic wave velocity) can complement geotechnical measurements to save project money and time. However, correlations between geotechnical and seismic wave velocity are crucial in order to maximize the benefit of geophysical information. In this work, artificial neural networks (ANNs) models are developed to forecast geotechnical parameters from seismic wave velocity. Specifically, published seismic wave velocity, liquid limit, plastic limit, water content, and dry density from field and laboratory measurements are used to develop ANN models. Due to the small number of data, models are developed with and without the validation step in order to use more data for training. The results indicate that the performance of the models is improved by using more data for training. For example, predicting seismic wave velocity using more data for training improves the R2 value from 0.50 to 0.78 and reduces the ASE from 0.0174 to 0.0075, and MARE from 30.75 to 18.53. The benefit of adding velocity as an input parameter for predicting water content and dry density is assessed by comparing models with and without velocity. Models incorporating the velocity information show better predictability in most cases. For example, predicting water content using field data including the velocity improves the R2 from 0.75 to 0.85 and reduces the ASE from 0.0087 to 0.0051, and MARE from 10.68 to 7.78. A comparison indicates that ANN models outperformed multilinear regression models. For example, predicting seismic wave velocity using field plus lab data has an ANN derived R2 value that is 81.39% higher than regression model

Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks

Geotechnical investigation plays an indispensable role in site characterization and provides necessary data for various construction projects. However, geotechnical measurements are time-consuming, point-based, and invasive. Non-destructive geophysical measurements (seismic wave velocity) can complement geotechnical measurements to save project money and time. However, correlations between geotechnical and seismic wave velocity are crucial in order to maximize the benefit of geophysical information. In this work, artificial neural networks (ANNs) models are developed to forecast geotechnical parameters from seismic wave velocity. Specifically, published seismic wave velocity, liquid limit, plastic limit, water content, and dry density from field and laboratory measurements are used to develop ANN models. Due to the small number of data, models are developed with and without the validation step in order to use more data for training. The results indicate that the performance of the models is improved by using more data for training. For example, predicting seismic wave velocity using more data for training improves the R2 value from 0.50 to 0.78 and reduces the ASE from 0.0174 to 0.0075, and MARE from 30.75 to 18.53. The benefit of adding velocity as an input parameter for predicting water content and dry density is assessed by comparing models with and without velocity. Models incorporating the velocity information show better predictability in most cases. For example, predicting water content using field data including the velocity improves the R2 from 0.75 to 0.85 and reduces the ASE from 0.0087 to 0.0051, and MARE from 10.68 to 7.78. A comparison indicates that ANN models outperformed multilinear regression models. For example, predicting seismic wave velocity using field plus lab data has an ANN derived R2 value that is 81.39% higher than regression model

GRACE Downscaler: A Framework to Develop and Evaluate Downscaling Models for GRACE

Monitoring and managing groundwater resources is critical for sustaining livelihoods and supporting various human activities, including irrigation and drinking water supply. The most common method of monitoring groundwater is well water level measurements. These records can be difficult to collect and maintain, especially in countries with limited infrastructure and resources. However, long-term data collection is required to characterize and evaluate trends. To address these challenges, we propose a framework that uses data from the Gravity Recovery and Climate Experiment (GRACE) mission and downscaling models to generate higher-resolution (1 km) groundwater predictions. The framework is designed to be flexible, allowing users to implement any machine learning model of interest. We selected four models: deep learning model, gradient tree boosting, multi-layer perceptron, and k-nearest neighbors regressor. To evaluate the effectiveness of the framework, we offer a case study of Sunflower County, Mississippi, using well data to validate the predictions. Overall, this paper provides a valuable contribution to the field of groundwater resource management by demonstrating a framework using remote sensing data and machine learning techniques to improve monitoring and management of this critical resource, especially to those who seek a faster way to begin to use these datasets and applications

Development of European End-Treatment TWINY Using Simulation and Crash Testing

In this study, a simple guardrail end treatment, called TWINY, designed particularly for use with a thrie-beam guardrail system is developed. In the first phase, the system is designed and analyzed using a versatile, highly nonlinear finite-element analysis program LS-DYNA. Two different crashes involving a head on impact and a 15 S angle impact are simulated using LS-DYNA. In both simulations, a nominal 900 kg car traveling at 80 km/h is used to impact the end treatment as outlined in European Crash Testing Guidelines EN1317 section 4. Based on the successful simulation results, both tests are repeated in a crash test facility in Germany to substantiate simulation predictions. Full-scale crash testing results compared favorably with those obtained from LS-DYNA simulation. Based on the results, a final full-scale crash testing was carried out on the system to fully verify its compliance with the EN1317 section 4. A 1,300 kg compact car traveling at 80 km/ h impacted the end terminal at its midlength at an angle of 15 S. The vehicle is successfully redirected with minimal damage to both vehicle and terminal. Based on the simulation and full-scale crash test results, it can be concluded that TWINY is a promising end treatment for steel thrie-beam guardrail terminals and can be implemented at the European Highway System with confidence