M. K. Nivedya
Bio: M. K. Nivedya is an academic researcher from Worcester Polytechnic Institute. The author has contributed to research in topics: Moisture & Discounted cash flow. The author has an hindex of 3, co-authored 13 publications receiving 35 citations.
TL;DR: In this article, the field permeability of Hot Mix Asphalt (HMA) is controlled to prevent excessive ingress of water into asphalt pavements, which leads to premature failure.
Abstract: Field permeability of Hot Mix Asphalt (HMA) needs to be controlled to prevent excessive ingress of water into asphalt pavements, which leads to premature failure. Existing literature shows scatter ...
••01 Jun 2019
TL;DR: Moisture-induced damage in asphalt mixes can lead to various distresses in the pavement as mentioned in this paper, the primary cause of such distresses is the loss in adhesion and cohesion properties of the asphalt.
Abstract: Moisture-induced damage in asphalt mixes can lead to various distresses in the pavement. The primary cause of such distresses is the loss in adhesion and cohesion properties of the asphalt ...
TL;DR: In this article, the authors confirm the significant structural damage that is caused by flooding on flexible pavements caused by rainwater, and propose a method to repair the damage caused by the flooding.
Abstract: Flood-induced moisture damage of flexible pavements is a serious concern for many road authorities. Reports from several studies confirm the significant structural damage that is caused by flooding...
01 Mar 2020
TL;DR: A method of multiple structure multiple run and ranging to optimize ANN to produce models with small data sets with high accuracy to solve the classification problem with different ranges in pavement engineering.
Abstract: In pavement engineering, the data sets that are typically obtained from experiments are small and cannot be classified as big data. The effective use of machine learning techniques such as artificial neural networks (ANN) for small data is a challenge because of poor accuracy of models. This paper presents a method of multiple structure multiple run and ranging to optimize ANN to produce models with small data sets with high accuracy. In this method, a large number of data fitting ANNs, with different number of neurons, layers, training and validation ratios, and randomized layer weights and biases are run in parallel, and the most accurate ANN is filtered out on the basis of the lowest MSE or highest R. The process is demonstrated with weather and pavement temperature data for a hot mix asphalt (HMA) and an open graded friction course (OGFC) pavement. Models are generated to predict the temperature at a depth of 12.5 mm below the surface. For the HMA pavement, an accuracy of 99.73% was obtained and an optimum structure was found to be with 4 layers, 11 neurons, 70% training ratio, 15% validation ratio. In the case of the OGFC pavement, an accuracy of 99.75% was obtained for an optimum structure with 3 layers, 11 neurons, 75% training ratio, 15% validation ratio. Furthermore, the fitting/regression problem was converted to a classification problem with different ranges, and then ANNs were utilized to develop very accurate classification models with small datasets.
16 Jul 2018
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
01 Jan 2014
TL;DR: In this paper, 100% recycled hot mix asphalt lab samples were modified with five generic and one proprietary rejuvenators at 12% dose and tested for binder and mixture properties, which ensured excellent rutting resistance while providing longer fatigue life compared to virgin mixtures and most lowered critical cracking temperature.
Abstract: 100% recycled hot mix asphalt lab samples were modified with five generic and one proprietary rejuvenators at 12% dose and tested for binder and mixture properties. Waste Vegetable Oil, Waste Vegetable Grease, Organic Oil, Distilled Tall Oil, and Aromatic Extract reduced the Superpave performance grade (PG) from 94–12 of extracted binder to PG 64-22 while waste engine oil required higher dose. All products ensured excellent rutting resistance while providing longer fatigue life when compared to virgin mixtures and most lowered critical cracking temperature. Rejuvenated samples required more compaction energy compared to virgin and some oils reduced moisture resistance slightly.
TL;DR: In this article, two optimized mixing orders were designed based on the microscopic observation of synthetic composite interfaces to simulate the worst situation during the traditional mixing process and observed in micro scale by scanning electron microscopy.
Abstract: Cold recycling technology is getting more and more attention due to economic and environmental benefits by reduced energy consumption and resource conservation. However, its application has been limited to the base and subbase layer because of complicated components and poor crack resistance for the last decades. The design method has shortcomings especially the traditional mixing order, which may be one of the reasons for poor crack resistance. To support the viewpoint above, synthetic composite interfaces were designed to simulate the worst situation during the traditional mixing process and observed in micro scale by scanning electron microscopy. In the microstructure of the traditional mixing order, it was apparent that cement paste had a number of microdefects, a signal of lower interfacial adhesive strength. Moreover, based on the microscopic observation, the adding sequence of cement ought to be changed and two optimized mixing orders were designed of which the difference was verified by the SEM observation of synthetic composite interfaces and the mechanical experiments for different curing time. It can be summarized that mechanical performance was consistent with the microscopic observation. The traditional mixing order was the worst one both in the strength and moisture sensitivity. Finally, the optimal mixing order is put forward to decrease the possibility of the adverse interface, that is, the graded aggregates are mixed with additional water first to reach the workability, while cement, asphalt emulsion and mineral powder are mixed to form cement-asphalt emulsion mortar, finally mixing them all up.
TL;DR: An investigation of the use of recursive partitioning and artificial neural networks (ANN; deep learning frameworks) in predicting the crack rating of pavements shows compelling machine learning models for the prediction of the crack ratings.
Abstract: Departments of Transportation regularly evaluate the condition of pavements through visual inspections, nondestructive evaluations, image recognition models and learning algorithms. The above metho...
01 Jan 2017
TL;DR: In this paper, the effects of long-term aging on both SBS-modified asphalt and base asphalt using AFM were investigated, and two new indices were introduced to quantify changes in the microstructures of asphalt, namely, the percentage of elliptical bee structures and surface roughness.
Abstract: Abstract Atomic force microscopy (AFM) has the capacity to distinguish among different phases at high resolution, and it is widely used in obtaining topography and mechanical property maps for asphalt binders. This study investigated the effects of long-term aging on both SBS-modified asphalt and base asphalt using AFM. In describing the study findings, this paper introduces two new indices used to quantify changes in the microstructures of asphalt, namely, the percentage of elliptical bee structures and surface roughness. In addition, the Derjaguin-Muller-Toporov modulus and adhesive force were measured to quantify the mechanical properties of the micro-phases. Finally, to evaluate the influence of aging on composite phase properties, a parallel model from the field of composite materials was introduced and applied. The results indicate that aging significantly affected the microstructures and mechanical behavior of micro-phases of asphalt binders. Aging also had a significant influence on the microstructures of the asphalt, especially in the bee structures. The percentage of bee structures increased or decreased after aging depending on the asphalt binder type, while surface roughness always decreased. Aging also increased the composite modulus of the micro-phases and decreased the composite adhesion of the micro-phases. These findings are in agreement with the macroscale aging behavior of asphalt binders.