Nivedya Madankara Kottayi

Bio: Nivedya Madankara Kottayi is an academic researcher from Worcester Polytechnic Institute. The author has contributed to research in topics: Moisture & Aggregate (composite). The author has an hindex of 3, co-authored 6 publications receiving 23 citations.

Suitable Tests and Machine Learning Approach to Predict Moisture Susceptibility of Hot-Mix Asphalt

Abstract: The objectives of this study were to determine a suitable set of tests to use with a moisture-conditioning process and to develop a machine learning model to predict the moisture susceptibi...

Moisture susceptibility evaluation of Hot Mix Asphalt: combined effect of traffic and moisture

Abstract: To date, most of the studies to evaluate moisture susceptibility of hot mix asphalt have been carried out by quantifying the degradation of the mix properties due to conditioning that simulates the action of moisture in the field. There is a need for research on the identification of moisture susceptible mixes which show the material loss in the wheel-path under the combined action of traffic and moisture. The objective of this study was to simulate and analyze the moisture induced material loss, and also to identify a mix with the potential of moisture induced material loss that has shown damage in the field but not under regular testing in the laboratory. The Moisture Induced Stress Tester (MIST), Ultrasonic Pulse Velocity (UPV), Dynamic Modulus in Indirect tensile mode, Indirect Tensile Strength (ITS), and Model Mobile Load Simulator (MMLS3) tests were utilized in the study. The effluent from the MIST was checked for the gradation of dislodged aggregates and the Dissolved Organic Carbon content. The results from the effluent analysis showed the loss of material and aggregate breakage from a moisture susceptible mix. A similar type of losses from the mix was also evident from MMLS3 loading under wet-hot conditions. The results of the mix mechanical properties showed that the use of MIST in combination with UPV or ITS is ab le to identify moisture susceptible mixes, in particular for mixes with the potential of aggregate breakage.

Economics of Making Roadway Pavements Resilient to Climate Change: Use of Discounted Cash Flow and Real Options Analysis

Abstract: An increase in the number of extreme weather events and gradual shifts in climate parameters due to a changing climate pose a serious threat to the nation’s roadway infrastructure. A system...

Framework for Rigorous Analysis of Moisture-Related Structural Damage in Flexible Pavements:

Abstract: The infiltration of water from precipitation through the hot mix asphalt layers in flexible pavements can lead to significant decrease in the moduli of the underlying layers, especially the base la...

Hot mix asphalt (HMA) moisture susceptibility analysis: material loss to mechanical properties

Abstract: Numerous studies have been conducted to identify moisture sensitive mixes during mix design by simulating various mechanisms of moisture damage. These methods involve the determination of changes in strength or stiffness of asphalt mixes due to moisture conditioning. The objective of this study is to understand the coupled problem of moisture induced material loss and change in strength/stiffness of the mix. Moisture Induced Stress Tester was used for conditioning samples of a poor and a good performing mixes. This test applies cyclic pressures in the asphalt mix samples through repeated pulses of water. The effluent containing aggregates and binder that were dislodged from the samples during the moisture conditioning process were collected for testing. Both coated and uncoated/fractured aggregates were found in the effluent. The results indicated that the samples with a higher loss of asphalt binder compared to other samples in the investigation during conditioning may exhibit higher tensile strengths, and those with a loss of finer materials, which is indicative of aggregate breakdown, show a lower tensile strength. Both seismic modulus and indirect tensile strength tests were found to be able to differentiate the poor and good performing mixes. For the mixes used in this study, the rate of change in indirect tensile strength during moisture conditioning was found to be strongly correlated to the pre-conditioning modulus of the mix, and a method is suggested for using the threshold values of properties of pre-conditioning mixes for different durations of moisture conditioning during mix design to screen poor mixes in a fast and nondestructive manner.

Flexible Pavements and Climate Change: A Comprehensive Review and Implications

Abstract: Flexible pavements and climate are interactive. Pavements are climate sensitive infrastructure, where climate can impact their deterioration rate, subsequent maintenance, and life-cycle costs. Meanwhile, climate mitigation measures are urgently needed to reduce the environmental impacts of pavements and related transportation on the macroclimate and microclimate. Current pavement design and life cycle management practices may need to be modified to adapt to changing climates and to reduce environmental impacts. This paper reports an extensive literature search on qualitative and quantitative pavement research related to climate change in recent years. The topics cover climate stressors, sensitivity of pavement performance to climatic factors, impacts of climate change on pavement systems, and, most importantly, discussions of climate change adaptation, mitigation, and their interactions. This paper is useful for those who aim to understand or research the climate resilience of flexible pavements.

Reducing susceptibility to moisture damage in asphalt pavements using polyethylene terephthalate and sodium montmorillonite clay

Abstract: This study examines the feasibility of developing built-in resistance against moisture damage using either passivation or an arresting mechanism. A specific moisture-susceptible paving mixture with proven moisture-damage issues in the field was selected for this study. The damage was attributed to failure at the interface of bitumen and stone aggregate due to accumulation of acidic compounds at the interface with subsequent dissolution in the presence of water. Here, we examine two remedial methods. The first method introduces a polyethylene terephthalate based additive (PET) to bitumen to neutralize active sites of stones with concentrations of silica oxide greater than 50% (denoted simply as siliceous stones), suppressing nucleation and growth of acids at the interface. The second method introduces sodium montmorillonite clay (MMT) to adsorb acids and prevent their migration to the interface of stone and bitumen. Measurement of shear binding between siliceous substrates and bitumen using a shear rate sweep test showed increases of 21% and 43% due to the inclusion of MMT and PET, respectively. This improvement was also observed in the results of bitumen bond-strength tests performed on glass and on stone substrates. Results of the evaluation at the mixture level using a Hamburg wheel-tracking test showed that the addition of MMT and PET improved resistance to moisture damage, as evidenced by the increasing number of cycles before moisture stripping occurs. For the PET-modified mixture, no stripping inflection was observed until 20,000 cycles. It was also observed that MMT’s adsorption of acidic compounds from bitumen led to the appearance of surface dents after water conditioning. The PET-modified specimen showed some signs of color change after water exposure, without any signs of stripping. The study results help formulators design materials with built-in resistance mechanisms against moisture damage.

Cost-Effective Approaches Based on Machine Learning to Predict Dynamic Modulus of Warm Mix Asphalt with High Reclaimed Asphalt Pavement.

Abstract: Warm mix asphalt (WMA) technology, taking advantage of reclaimed asphalt pavements, has gained increasing attention from the scientific community. The determination of technical specifications of such a type of asphalt concrete is crucial for pavement design, in which the asphalt concrete dynamic modulus (E*) of elasticity is amongst the most critical parameters. However, the latter could only be determined by complicated, costly, and time-consuming experiments. This paper presents an alternative cost-effective approach to determine the dynamic elastic modulus (E*) of WMA based on various machine learning-based algorithms, namely the artificial neural network (ANN), support vector machine (SVM), Gaussian process regression (GPR), and ensemble boosted trees (Boosted). For this, a total of 300 samples were fabricated by warm mix asphalt technology. The mixtures were prepared with 0%, 20%, 30%, 40%, and 50% content of reclaimed asphalt pavement (RAP) and modified bitumen binder using Sasobit and Zycotherm additives. The dynamic elastic modulus tests were conducted by varying the temperature from 10 °C to 50 °C at different frequencies from 0.1 Hz to 25 Hz. Various common quantitative indications, such as root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) were used to validate and compare the prediction capability of different models. The results showed that machine learning models could accurately predict the dynamic elastic modulus of WMA using up to 50% RAP and fabricated by warm mix asphalt technology. Out of these models, the Boosted algorithm (R = 0.9956) was found as the best predictor compared with those obtained by ANN-LMN (R = 0.9954), SVM (R = 0.9654), and GPR (R= 0.9865). Thus, it could be concluded that Boosted is a promising cost-effective tool for the prediction of the dynamic elastic modulus (E*) of WMA. This study might help in reducing the cost of laboratory experiments for the determination of the dynamic modulus (E*).

Incorporating Climate Change in Pavement Maintenance Policies: Application to Temperature Rise in the Isfahan County, Iran

Abstract: Although temperature rise is imminent in Iran and could damage asphalt pavements, no national guide exists to adapt them. To ensure the sustainability of pavements against temperature rise, a county-level methodology based on machine-learning algorithms was developed. To show the applicability of the framework, the Isfahan County was studied. The county's climate was found to change from cold-semi-desert to relatively-warm-semi-desert in future decades. Then, optimal maintenance policies before and after climate change were identified. It was concluded that optimal policies of arterial roads before and after climate change were more intense than those of local roads. Furthermore, optimal policies after climate change were more intense than those before climate change at additional costs of 1379.57 MR/KM and 632.49 MR/KM respectively for arterial and local roads. The same methodology could be applied to sustainably adapt asphalt pavements of other counties. To validate the research, a questionnaire survey of pavement management and climate change experts was done. The experts confirmed that the methodology facilitates achieving sustainable development goals #9, #11, and #13 by improving maintenance budget allocation, enhancing policy-makers communication with authorities, maintaining adequate technical and end-user levels of service, and adapting pavements to climate change through cost-effective and performance-effective maintenance policies.

Prediction of the Compressive Strength for Cement-Based Materials with Metakaolin Based on the Hybrid Machine Learning Method

Abstract: Cement-based materials are widely used in construction engineering because of their excellent properties. With the continuous improvement of the functional requirements of building infrastructure, the performance requirements of cement-based materials are becoming higher and higher. As an important property of cement-based materials, compressive strength is of great significance to its research. In this study, a Random Forests (RF) and Firefly Algorithm (FA) hybrid machine learning model was proposed to predict the compressive strength of metakaolin cement-based materials. The database containing five input parameters (cement grade, water to binder ratio, cement-sand ratio, metakaolin to binder ratio, and superplasticizer) based on 361 samples was employed for the prediction. In this model, FA was used to optimize the hyperparameters, and RF was used to predict the compressive strength of metakaolin cement-based materials. The reliability of the hybrid model was verified by comparing the predicted and actual values of the dataset. The importance of five variables was also evaluated, and the results showed the cement grade has the greatest influence on the compressive strength of metakaolin cement-based materials, followed by the water-binder ratio.