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2025 | Book

Proceedings of the 7th International Conference of Transportation Research Group of India (CTRG 2023), Volume 1

Editors: Prasanta K. Sahu, Nikhil Saboo, Bandhan Bandhu Majumdar, Agnivesh Pani

Publisher: Springer Nature Singapore

Book Series : Lecture Notes in Civil Engineering

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About this book

This book presents select proceedings of the 7th Conference of Transportation Research Group of India (7th CTRG, 2023) and provides an opportunity for discussion of state-of-the-art research and practice in the developing world for achieving equitable, efficient, and resilient infrastructure and opens pathways to sustainable transportation. This book covers the solutions related to transportation challenges such as road user safety, traffic operation efficiency, economic and social development, non-motorized transport planning, environmental impact mitigation, energy consumption reduction, land-use, equity, freight transport planning, multimodal coordination, access for the diverse range of mobility needs, sustainable pavement construction, and emerging vehicle technologies. The information and data-driven inferences compiled in this book are therefore expected to be useful for practitioners, policymakers, educators, researchers, and individual learners interested in sustainable transportation and allied fields.

Table of Contents

Frontmatter
A Reserve Price Model for Public–Private Partnership Auctions of Brownfield Airports and Impact of Economic Shocks
Abstract
Over the last two decades, attempts of airport privatization in emerging economies have seen mixed outcomes. Literature shows that though many privatized airports could achieve incremental operational efficiencies, challenges have remained in establishing public–private-partnership (PPP) arrangements that could become benchmarks. Our study attempts to address this challenge by developing a reserve price model for PPP auctions of brownfield airports. We define relationships between the variables affecting airport income and use them to forecast the net income for the concession period under different scenarios. Deviating from conventional forecasting techniques, such as time-series and regression, we adopt Gaussian fuzzy membership functions for forecasting air traffic. We also assess the impact of economic shocks on reserve prices. The findings are corroborated with the recent PPP auctions of four brownfield Indian airports. Emerging economies can contextualize and adopt the model for their brownfield airport auctions.
Soumya Jain, Partha Sarathi Banerjee
Machine Learning Approach for Deflection Bowl Parameter Prediction in Flexible Pavements: A Random Forest Algorithm-Based Study
Abstract
The Falling Weight Deflectometer (FWD), among many other non-destructive analysis techniques, is vital to evaluating the structural condition of roadways and optimizing pavement management systems. Various methods based on pavement surface deflection, as measured by FWD, are widely used for assessing the structural stability of pavements across the globe. However, performing these tests consistently at the network-level is challenging, and data interpretation requires time, technical expertise, finances, and other resources. As a result, the structural component of roadways is often neglected when making decisions about maintenance and repair. This study proposes a machine learning approach, specifically the random forest algorithm, to estimate four basic deflection basin parameters, including surface curvature index, Base Damage Index, Base Curvature Index, and Deflection Ratio, using various input parameters such as structural, functional, environmental, and subgrade soil properties. To develop an effective model, the random forest algorithm is trained and tested using MATLAB tool, using the data gathered through field trials. The prediction accuracy from the developed model is evaluated based on the root mean square error and coefficient of determination value. Based on the input variables, the random forest method constructs numerous decision trees, each of which offers a prediction for the deflection basin parameters. The algorithm then combines the predictions of all the trees to obtain a final prediction. This approach improves the accuracy of deflection basin parameter prediction compared to traditional methods, which rely on backcalculation of pavement layer moduli. Overall, the proposed machine learning approach using the random forest algorithm provides an efficient and accurate way to predict the deflection basin parameters of flexible pavements.
Aakash Gupta, Sachin Gowda, C. S. Nandan, G. Kavitha
Characterization of Cement Treated Aggregates Base Stabilized with Supplementary Cementitious Materials
Abstract
Characterization of stabilized aggregates through material properties is essential to satiate its design. These material properties comprise of unconfined compressive strength (UCS) and assessment of flexural behavior through modulus of rupture (MoR) and flexural modulus (FM). These properties are strongly affected by the type of stabilizers and materials to be stabilized. The present research highlights the utilization of supplementary cementitious materials (SCMs) like ground granulated blast furnace steel slag (GGBS) and fly ash as a prospective partial replacement to cement in stabilizing aggregates through mechanical tests and its effect on the design process. Changes in material properties with incorporating SCMs were identified and assessed for the subsequent curing periods of 3, 7, 28, and 90 days. Flexural modulus, an essential parameter in the design of stabilized aggregates, was statistically analyzed to identify the effect of using SCMs. Strong linear relationships were found between FM and UCS & MoR (R2 > 0.94). The correlating factors were found to be high for GGBS and fly ash incorporated mixes while less for cement-stabilized aggregates. Further, the cost–benefit analysis for the prospective application of SCMs revealed significant savings of 16 and 6% in cost on utilizing GGBS and fly ash in the stabilized aggregates layer, respectively. The findings from this research are expected to increase the usage of SCMs materials in stabilizing aggregates in high proportion through characterization and economic advantages.
Rohit Kumar Sharma, Dharamveer Singh, Satyanarayana Murty Dasaka
From Cash to Cashless: Exploring Factors Influencing Digital Payments Acceptance Among Public Transport Drivers
Abstract
Digital technologies are spreading across countries, enhancing every sector of society. Digital payment technology acceptance is well-researched, but the research from the perspective of public transportation drivers is minimal. This study tries to fill this gap by understanding the factors responsible for the choice of payment among public transport drivers. Data from a questionnaire survey designed to elicit information on the factors that influence the choice of payment modes of 261 Public transport drivers in Kota City, India, is used to understand the factors responsible for the choice of payment. Binary Logit, Decision Tree, and Random Forest models were used to analyse the data. The accuracies of the models are compared. Results indicate that income, working hours, and age are the most significant factors that influence the payment mode choice of public transport drivers. Drivers with higher working hours and older age are less likely to choose digital payment modes than cash. Also, bus and taxi drivers are more likely to accept digital payments than auto drivers. Drivers with higher and fixed incomes are more likely to choose digital payments over cash. This study highlights the importance of understanding the factors that influence payment mode choices of public transport drivers and provides insights valuable for policymakers to encourage the adoption of digital payment modes in the public transportation sector. Policymakers can focus on increasing awareness and trust in digital payment systems among older and higher working-hour drivers while incentivising auto drivers to adopt digital payment systems.
Shahiq Ahmad Wani, Ranju Mohan
Estimation of Target Modulus Using LWD Testing on CBR Mould for Compaction Quality Assurance
Abstract
Nowadays, the use of Light Weight Deflectometers (LWDs) for in-situ evaluation of pavement layer moduli has gained popularity. Modulus-based quality assurance (QA) approaches have supplanted density-based QA methods due to their portability and testing simplicity. Using LWD testing on CBR mould, this study aims to determine the target modulus of soil samples. Locally available samples of three distinct types of soil were gathered. In the laboratory, particle size distribution, index properties, and the IS light compaction test were performed. In addition to determining the elastic modulus with LWD testing on the CBR mould (ELWD;MOULD), the optimum moisture content and maximum dry density were also determined using the same mould. A correlation was established between moisture content (MC) and elastic modulus to calculate the target modulus (ETarget). The modulus values reached their maximum on the dry side of the optimum moisture content (OMC). The developed models provide target modulus values corresponding to field moisture content data, which should fall within the acceptable range as per agency requirements. LWD on mould target determination eases the shift from density-based to modulus-based QA. It minimises fieldwork and expenses.
Shubhm Dwivedi, Sanjeev Kumar Suman
Prediction of the Rutting Behaviour and Strain Response of Flexible Pavement Layers Using Roller Compactor Cum Rut-Analyser
Abstract
Flexible pavements are subjected to vehicular loads which can affect their performance and decrease the service life due to various reasons, including rutting. The performance of a flexible pavement over its design life is directly related to parameters, such as stresses, strains, and deflections, that are induced due to wheel loads. The experimental models used in earlier rutting-related pavement performance studies mainly relied on empirical correlations and did not fully simulate the actual field conditions. This paper addresses limitations by presenting a laboratory investigation using a comprehensive flexible pavement model, employing a roller compactor cum rut-analyser (RCRA). The RCRA rutting model correlates wheel load passes with rut depth, aligning measured and predicted values, reflecting model accuracy. Concurrently, strain responses during rutting were measured, validating flexible pavement conduct at different interfaces. Highlighting the RCRA rutting and strain response models’ capability to simulate in-service pavement behaviour is noteworthy. The method suitably monitors strain responses in flexible pavement layers under wheel loads, eliminating the necessity for in-situ measurements.
B. V. Kiran Kumar, N. Shiva Prasad
Unsupervised Clustering of Asphalt Pavement Conditions with Principal Component Analysis-aided Dimensionality Reduction
Abstract
The state of the pavement is a critical component of the transportation infrastructure that affects cost, efficiency, and safety. Machine learning (ML) techniques have attracted a lot of attention recently as a means of improving our understanding and capacity for predicting asphalt pavement behavior. This study provides unsupervised ML models for categorizing the states of asphalt pavement based on their measured features, such as roughness, cracking, rutting, texture depth, potholes, and raveling. Unsupervised clustering algorithms, namely K-means and K-medoids are implemented for clustering pavements based on the conditions relative to deterioration metrics and, allowing the detection of patterns and groupings within vast datasets and the categorization of road segments based on their attributes, even in the absence of labeled data. By choosing variables with significant levels, algorithms were able to cluster the stretches of pavements with almost similar deterioration levels under distinct groups. Optimal number of groups were chosen based on silhouette score, variance within clusters, total variance, and between clusters. The outcomes show that suggested unsupervised ML model effectively partitions different pavement conditions into groups based on their measured parameters, providing insightful information about how asphalt pavements behave. These data can help transportation infrastructure managers make better judgements about maintenance, repairs, and improvements, enhancing cost-effectiveness, safety, and efficiency.
Sachin Gowda, C. S. Nandan, M. A. Jayaram, Aakash Gupta, G. Kavitha
On the Transferability of Speed Prediction Models: A Case for Cluster-Modeling on Large Road Networks
Abstract
Operating speed is influenced by several factors related to road geometry, roadway environment, vehicle, and driver. Over years, several operating speed prediction models have been developed across the globe. Researchers generally regard operating speed prediction model as non-transferable, owing to variability in unobserved factors, such as driver behavior. This paper focuses on performing a transferability check on operating speed prediction models. The transferability is evaluated based on consensus of speed prediction models when applied on a carefully generated hypothetical network that confirms to site selection criteria of selected models. As expected, a statistical check confirms the lack of consensus across models, and, therefore, the non-transferability. The range of speed predicted for the hypothetical network implied the non-transferability of the speed model—not only in the case of models from distant geographies but even for models developed in the same region. Subsequently, we introduce an approach of cluster-modeling for application on large road networks. We propose to first identify clusters of road segments that are similar in terms of attributes that are significant predictors of speed.
R. N. Shilpa, B. K. Bhavathrathan
Estimation of Public Transport Accessibility in Rural Areas Using GIS
Abstract
The transportation field is one of the challenging fields. It plays a vital role in the development of any region. Policymakers and transport planners are trying to make the transportation field more sustainable. Sustainable transportation indicators help to evaluate transportation systems. Public transportation is the key component of sustainable transportation systems. As accessibility is a major transportation indicator, it is used in the study to evaluate the sustainability achievement of the study area. Public Transportation Accessibility Level (PTAL) methodology is an effective tool used to evaluate the accessibility of public transportation in an area. Accessibility index values identified using the methodology can be used to evaluate accessibility. Accessibility analysis using GIS techniques is adopted in the study. Mapping of accessibility indices in ArcGIS gives an appropriate visualization to analyze public transportation accessibility. The low accessible area to public transportation can be easily identified from the analysis. This study includes the application of PTAL methodology incorporating the GIS software to analyze the accessibility of public transportation.
P. Devipriya, Jomy Thomas
Evaluation of Jute Fibre as a Reinforcing Material in Stone Mastic Asphalt Used for Rural Roads
Abstract
The impetus of the current study arises from the scarcity of commercial pelletized cellulose fibre, motivating an investigation into economically practical alternative materials for the construction of stone mastic asphalt (SMA) utilized as the wearing course of flexible pavement. Consequently, the research tries to assess the viability of applying locally abundant wastes like Rice Husk Ash (RHA) and Pond Ash (PA) as alternative fillers. Additionally, jute fibres use as a reinforcing agent by employing a combination of Marshall mix design, Drain-Down tests, and Indirect Tensile Strength (ITS) tests. Findings revealed that introducing 0.3% jute fibre into the 4% RHA-enriched SMA resulted in a higher Tensile strength ratio (TSR) value and lower optimum bitumen content as well as lower drain-down of binder materials, could potentially serve as a promising wearing course for low-volume rural roads.
Asit Ghosh, Tapash Kumar Roy, Sandip Karmakar
The Transportation and Public Health Conundrum: How Vehicle Ownership Promotes Both Lifestyle Diseases and Access to Preventive Healthcare in India
Abstract
Transportation has been regarded as a significant facilitator and barrier to accessing jobs and a range of basic urban services and opportunities. Availability of car, for example, is known to increase access to healthcare services, thereby improving public health outcomes in cities. Scholars, however, are concerned about physical inactivity and associated diseases due to increasing motorized personal transportation use and declining use of active travel modes such as, bicycling and walking. Policymakers are getting worried about deteriorating physical activity and fitness, rising obesity, and increasing incidence of illnesses like diabetes that stem from physical inactivity and sedentary behavior not only in developed but also in developing countries like India. Although many studies have tried to study the association between motorized vehicle ownership and lifestyle diseases in India, they were either limited to a small section of population or focused on specific diseases. Thus, the present study aims to study the association between: (a) household motorized vehicle ownership and lifestyle diseases (obesity and diabetes), and (b) household motorized vehicle ownership and accessibility to preventive healthcare in a nationally representative sample of men in India. Using data from India’s National Family Health Survey, we find that owning motorized vehicle was significantly associated with obesity as well as preventive care visits to a healthcare facility among men, but not with diabetes. It indicates a public health conundrum. Motorized vehicle ownership has a negative role in promoting lifestyle diseases like obesity but can still facilitate accessibility to preventive healthcare, thereby promoting health. Transportation policies must consider this public health puzzle.
Sagar Verma, Sandip Chakrabarti
Partial Replacement of Cement with Waste Marble Dust and Coarse Aggregate with Reclaimed Asphalt Pavement in Concrete
Abstract
Concrete is the most supreme material of construction which consists of cement, coarse aggregate, fine aggregate and appropriate amount of water. Due to intolerable cost of aggregates used in the making of concrete, Civil Engineering Researchers across the world are trying to produce reclaimable and resource saving concrete for the purpose of construction. Also due to the deposition of waste material on landfill, several environmental problems arise. To overcome such problems alternate waste materials can be taken into consideration such as waste marble dust, waste glass, fly ash, ceramic powder, e-waste, reclaimed asphalt pavement etc. This paper is a brief study on the partial replacement of cement with waste marble dust (WMD) and coarse aggregate (CA) with reclaimed asphalt pavement (RAP).The results indicated that optimum percentage replacement of RAP and WMD is taken as 30 and 10% respectively because on 30% replacement of coarse aggregate with RAP the 28 days compressive strength decreases by 18.41% while accomplishing the minimum strength benchmark of M30 grade of concrete and on 10% replacement of cement with WMD it is increased by 20.33% as compared to the conventional mix. Thereafter, a mix of concrete containing 10% WMD and 30% RAP is prepared; the 28-day compressive strength of this mix is increased by 0.108% as compared to the conventional concrete and similar trend can be seen in flexural strength and split tensile strength. So, the study suggested that WMD can be used as a substitutional cementitious material which when mixed with RAP in proper proportion makes the strength of RAP containing concrete at least equal to the conventional concrete.
Akanksha Bilthare, Kamal Singh, Siddhartha Rokade
Effect of Fly Ash in Soil Subgrade: Study on Design Implications and Cost-Effectiveness
Abstract
The objective of this study was to evaluate the effect of fly ash in soil sub-grade modification on pavement design and quantify the cost-effectiveness of the construction. The research scope encompassed the modification of soil using fly ash, quantification of improvement through laboratory experimentation, design of flexible pavement at three design traffics, and estimation of initial cost of construction based on the volume of materials. With three levels of design traffics: low, medium, and high; three fly ash contents: 5, 10, and 15%, along with control soil, totaling 12 pavement sections were considered in the design. When fly ash content increased, the CBR of soil also increased consistently. Next, the improvement of soil properties quantified through CBR was used as an input for pavement design. It was found that there existed a scope for thickness reduction in both WMM and GSB layers. The highest reduction in thickness, around 14.5%, was noticed in the WMM layer at medium traffic conditions. Furthermore, the cost-effectiveness analysis during the initial construction phase revealed that the addition of fly ash did not have any negative impact on the cost-effectiveness of pavement. A marginal saving was found when 10% fly ash was used in soil modification under medium traffic conditions. Overall, it was envisaged that the research methodology adopted as part of this study would help road agencies, practitioners, engineers, and other stakeholders evaluate various alternative strategies for improving the quality of subgrade layers.
Ramchandra Naik, T. Akhil, Gourab Saha
Impact of Transportation on the Quality of Life of Physically Challenged Working Women and Students—A Case of Bangalore
Abstract
A unique marginalized section of society, women with a disability, although limited by their challenges, have the right to equally access the service of the city that gives them a sense of autonomy and improves their overall Quality of Life. However, from the literature review, it was found that there is a scarcity of studies done on understanding the factors that affect the mode choices of women with disability. This study fills in that gap and aims to understand the experiences and challenges that affect the mode choice of women with disabilities while carrying out trips for various purposes and how they differ from their male counterparts. Through a Focus Group Discussion (FGD) methodology, questions were asked to women with disability to understand their experience and expectations from the transportation system in Bangalore, India. Sentiment analysis was carried out to understand the emotions and sentiments of two women groups while commuting in Bangalore. Working women with walking disabilities expressed that poor road infrastructure, unsafe travel experiences, lack of disabled-friendly public transport, and lack of disabled-friendly parking facilities are hurdles in commuting while women with visual impairments expressed challenges in navigation and challenges in using public transport as major barriers in travelling. The key finding of the study was that due to these challenges, women with disabilities are forced to curtail their commute or shift to non-sustainable modes of transportation, which have major implications on their education, economic condition as well as social well-being, affecting their overall Quality of Life. The study concludes with policy recommendations that can encourage and empower this section of women and provide easy access to transportation.
Meghna Verma, Silky Jain
Understanding the Willingness of Mode Shift to Feeder Services Amid Important Events—A Case Study
Abstract
Special events in cities lead to the surrounding road network being overloaded with traffic. The operations of traffic, particularly in cities and its transportation, are significantly impacted by these kinds of events. The usage of personal modes by the visitors aggravates the problem. The present paper tries to study and analyze the impact of congestion and parking issues prevalent in a major event location named Kanakakkunnu at Thiruvananthapuram city, Capital of the Indian State of Kerala. The data collection through Revealed Preference and Stated Preference survey was conducted during the flower show event held at Kanakakkunnu, which is one of the major events that attract visitors in large scale from across the State. To tackle this problem, a traffic management scheme was proposed that included the provision of bus as feeder services in the locality with park and ride options aimed to reduce the use of personal modes. Further logit models are developed to understand the willingness of mode shift to feeder services. Results showed that the suggested scheme is acceptable, and the traffic management plan can be implemented in the event premise to reduce the congestion. Besides, it was observed that the shift to feeder services reduces with increase in average monthly household income in case of two wheelers. However, the shift reduces with increase in age as well as monthly income in case of car users. The likelihood of shift decreases as travel time and distance increase. The results are useful for policy makers and stakeholders to introduce new such schemes aiming to reduce congestion during such events.
V. S. Sanjay Kumar, N. Vasudevan, Neelu Mammen, Shabana Yoonus, Samson Mathew
Reclaiming Streets of an Inner Urban Core Area with the Place-Making Approach: Case of Surat City
Abstract
Amidst the era of urbanization, streets hold immense significance as centers of civic engagement, social interactions, and commercial endeavors. They epitomize paramount locations for the public. The discourse surrounding superior road planning, design, and management has gained prominence on a global scale. Both the new urban agenda and the Sustainable Development Goals underscore the pivotal role of establishing high-quality, accessible, and secure public spaces that cater to everyone. This article delves into the context of Surat, a rapidly urbanizing city in Gujarat, India. In consonance with numerous urban and suburban locales, a considerable portion of street space is dominated by vehicular traffic, with streets predominantly tailored to accommodate automobiles. However, the absence of easily accessible sidewalks, inadequate provision of bicycle lanes, recurring traffic congestion, prevalent on-street parking, insufficient infrastructure for pedestrian crossings, disorderly placement of bus stops, utilities, shop extensions, and vendors, has transformed the streets into a source of distress for the local population. The true essence of the street can only be grasped by immersing oneself in its public character. To rectify this, the concept of Place-making is embraced, aiming to orchestrate a holistic redesign that reclaims the street as a communal area and forges a connection between individuals and their surroundings. The trajectory of this endeavor commences with a comprehensive review of existing literature to ascertain the scope of Place-making research. Subsequently, it progresses to encompass a field survey, a zone-based analysis, and a space syntactic analysis, collectively serving to gauge the vitality of the area. The technique of space syntax analysis is employed to gauge users’ perceptions and pinpoint areas necessitating intervention. This research advances a methodological framework intended to enhance the current street environment in Surat, accommodating the diverse needs of its users. Strategies and recommendations are proffered to elevate public spaces and enhance pedestrian mobility on multiple tiers, encompassing spatial transformation.
Jinal Boricha, Satyaki Sarkar, Prashant Prasad
Prediction of Aggregate Gradation from Two-Dimensional (2-D) Image of Aggregates—A Simple Approach
Abstract
Prediction of aggregate gradation from 2-D images of aggregates typically involves two stages—estimation of aggregate gradation by number and then by weight. For the first part, one needs to determine whether a given aggregate would pass through a given sieve size or not, and repeat this for all the aggregates under consideration. For the second part, one needs to estimate the volume of each of the aggregates to obtain their weights—since aggregate gradation is generally expressed in terms of weight proportion. Both these stages accumulate errors during computation because of the attempt to predict 3-D features from the acquired 2-D data. To reduce these errors, the present paper suggests incorporation of two shape-dependent correction factors for these two stages. Tests are conducted on a set of aggregates to obtain these correction factors. Thereafter, the proposed scheme is implemented on a fresh set of aggregates for validation.
Sneha Mandal, Ranja Bandyopadhyaya, Animesh Das
Comparative Analysis of Trip Generation Model of Tier II Cities in India
Abstract
This study focuses on analysis of trip generation (TG) of Tier II Cities in Madhya Pradesh, India, namely Bhopal and Jabalpur. The collection of household data is conducted via a questionnaire survey. Multilinear Regression (MLR) and Artificial Neural Network (ANN) are utilized to found that six dominant variables affect the household TG. The MLR dataset was used to create, test, and compare ANN models for each municipality. The ANN model with r square value of 0.604 and 0.686 and mean square error (MSE) of 458.62 and 254.63 performed better than the MLR model (R2 = 0.48 and 0.59 and MSE = 1573.33 and 1569.98) for Bhopal and Jabalpur respectively. The findings suggest that ANN is a practical tool for TG modeling, with predictions that are even more solid than those of traditional MLR. The TG model provides precise TG calculations and facilitates in providing transportation infrastructure facilities by policy makers.
Saumya Anand, Pritikana Das, G. R. Bivina
Assessment of Robustness of Backcalculation Tool in Capturing Pavement Moduli Using FWD Device
Abstract
The estimation of in-situ pavement responses, such as deflections, through Non-Destructive Testing (NDT), is a widely accepted technique for assessing the strength characterization of in-service pavements. The most commonly used NDT tool is the falling weight deflectometer (FWD). The deflection profiles obtained from this NDT device are used to determine the layer moduli. Presently, various backcalculation approaches are used, and the resulting moduli vary based on the chosen methodology, algorithm, modulus range. In India, determining the optimal approach for estimating realistic pavement layer moduli is a challenging task. Hence, a comparative analysis of the different techniques employed for estimating backcalculated moduli of each pavement layer becomes imperative. This study aims to undertake such a comparison analysis. The study focuses on evaluating the variation in backcalculated moduli pavement layers using several methodologies, including the Radius of Curvature Method and the Deflection Basin Method by ELMOD (RCME, DBME), KGPBACK, approximation methods, and Artificial Neural Networks (ANN). 10 pavement stretches comprising both poor-condition and good-condition pavements were selected for field investigation and analysis. Comparing the outcomes of layer moduli among static and adaptive methods reveals that for both good and poor condition pavement stretches, the approximation method exhibits the highest percentage of variation: 29%, 20%, and 25% for surface, granular, and subgrade layers, respectively. Conversely, the ANN approach demonstrates the least percentage of variation: 9, 10, and 9% for the same layers. This study validates the suitability of the ANN method due to its robustness and simplicity.
Gangisetti Satyanandam, Sunny Deol Guzzarlapudi, Laxmikant Yadu
Study on Strength and Microstructural Properties of Bio-enzyme and Cementitious Treated Reclaimed Pavement Materials
Abstract
This study provides an innovative approach to enhance the compaction and strength characteristics of Reclaimed Pavement Material (RPM) mixes by utilizing bio-enzyme, an organic stabilizer, without affecting nature. Various laboratory tests were performed by varying curing periods, cement dosages, and bio-enzyme (optimum dose) to analyze the effect of cement stabilization and enzymatic cement stabilization in RPM. The test results show that with an increase in cement percentage, there was a decrease in OMC and a consistent rise in MDD. The change in MDD and OMC in enzymatic cement stabilization is due to bio-clogging and bio-cementation. The hydrated cementitious products clog the voids between the soil and exhibit higher compressive strength. Pozzolanic activity is increased when bio-enzyme is added to the soil–cement mixture because it changes the clay matrix by microbial action. Therefore, C–S–H gel is formed, resulting in increased cohesion, bond strength, UCS, reduced porosity, and affinity to water.
Ashish Mishra, Sunny Deol Guzzarlapudi
Metadata
Title
Proceedings of the 7th International Conference of Transportation Research Group of India (CTRG 2023), Volume 1
Editors
Prasanta K. Sahu
Nikhil Saboo
Bandhan Bandhu Majumdar
Agnivesh Pani
Copyright Year
2025
Publisher
Springer Nature Singapore
Electronic ISBN
978-981-9796-54-0
Print ISBN
978-981-9796-53-3
DOI
https://doi.org/10.1007/978-981-97-9654-0