Eshta Ranyal Postdoctoral Researcher

Research Eshta Ranyal

Research team

Expertise

Macro-scale endeavors, primarily focused on AI-assisted monitoring and maintenance, providing solutions that enhance the reliability, efficiency, and sustainability of systems and infrastructure. Vision-Based Object Detection to develop algorithms for detecting and recognizing objects within visual data, often employing deep learning models for improved accuracy. Deep Learning Models to analyze complex datasets, extract meaningful patterns, and make predictions, contributing to enhanced decision-making processes. Time Series Analysis investigating temporal patterns within data to understand trends, fluctuations, and dependencies over time, particularly relevant for predictive maintenance and forecasting. Predictive Modeling that can forecast future outcomes or conditions based on historical data, aiding in proactive decision-making and resource optimization. Image Processing Techniques to enhance, analyze, and extract valuable information from visual data. This includes filtering, segmentation, and feature extraction for improved understanding. AI-Assisted Monitoring to continuously monitor and analyze real-time data streams, identifying anomalies or patterns that may require attention. Automation and Efficiency by Integrating AI and automation to improve efficiency in monitoring and maintenance processes, reducing manual intervention and enhancing overall system performance.

SuPAR-C: SuPAR Technology and Innovation Consult Service Platform 01/05/2026 - 30/04/2027

Abstract

SuPAR (Sustainable Pavement and Asphalt Research) is a multidisciplinary research group within the Faculty of Applied Engineering at the University of Antwerp, with internationally recognised expertise in sustainable and resilient pavement structures, particularly asphalt technology and materials. The group has a strong track record in national and international research projects and maintains extensive academic and industrial networks. While SuPAR's research output has reached high technology readiness levels (TRLs), the valorisation of these results into market-oriented services requires a more structured organisational approach. A key strength of this proposal is that SuPAR already delivers services; the proposed platform enables scaling, professionalisation, and internationalisation, particularly by addressing the gap between published research results and their implementation in industry. This is especially relevant for multidisciplinary challenges combining performance, sustainability, and life-cycle assessment. This Proof-of-Concept (PoC) project aims to launch SuPAR-C, a dedicated Service Platform that facilitates consultancy projects, industrial engagement, and valorisation activities. The PoC project will focus on the design, implementation, and validation of the platform through real-life pilot cases with Benelux stakeholders, building on existing expertise and tools. The platform will provide low-threshold access to SuPAR's expertise, testing facilities, and tools; enable structured knowledge transfer and consultancy for the pavement and construction sectors; and serve as a scalable vehicle for the valorisation of high-TRL research outcomes. The Service Platform will be supported by SuPAR experts and enhanced through a secure website and digital portal to increase visibility and accessibility. Through this portal, external service requests and digital tool data exchange will be managed in a structured and transparent manner. Expertise is organised into four clusters, each supervised by academic staff: Innovative and Sustainable Asphalt Mixtures and Structures – consultancy, testing, and validation of innovative materials and designs, including Green Public Procurement tools (planned launch in 2026). Digitalisation and AI Technologies – support for digital, AI-driven, and data-centric solutions in pavement and infrastructure engineering. Construction Process Quality and Road Asset Management – data-driven tools and consultancy for pavement monitoring and network management (e.g. ROAD_IT tools and sensor-based systems). Bitumen and Asphalt Laboratory – one of the most advanced academic pavement laboratories in Europe, providing specialised testing beyond current standards. The Service Platform builds on expertise and tools developed in past and ongoing projects. It formalises existing service activities through a dedicated legal and organisational structure, enabling faster response times, clearer market access, and professional service delivery. The development of fundamentally new tools is not within the scope of this project and would be addressed through separate PoC initiatives, external funding, or internal investments if required. By the end of the PoC phase, SuPAR-C will demonstrate technical feasibility, organisational readiness, and market relevance. The platform will establish a sustainable pathway for long-term valorisation, enable broader involvement of early-career researchers in industry-facing activities, and form the basis for international upscaling in alignment with initiatives such as the SURPAVE Erasmus Mundus programme and the CRIPI COST Action.

Researcher(s)

Research team(s)

Project type(s)

  • Research Project

Exploring AI-guided Transportation Infrastructure Asset Management (AI-TIM). 01/12/2025 - 30/11/2027

Abstract

This project explores innovative approaches for AI-guided transportation infrastructure asset management through two complementary research paths: (i) health monitoring of road structural condition, and (ii) detection of erosion on cut-and-fill slopes. Both areas address pressing challenges in maintaining resilient transport systems and provide the foundation for future large-scale studies. For the road monitoring component, a field test section equipped with embedded sensors will be used to collect structural response data under real traffic and environmental conditions. The project will support the acquisition of additional hardware required for reliable and continuous data acquisition and remote monitoring. In the first phase, data collected for 3-4 months will be analyzed to refine AI-based processing techniques and assess the robustness of the data management process. In the second phase, the project will investigate alternative global sensing strategies that move beyond traditional point-based measurements. The goal is to conceptualize sensing approaches that can provide broader insights into structural performance and early signs of deterioration over the entire length of the road. The erosion component of the project focuses on the use of AI-enabled image analysis to identify and characterize erosion features on soil and rock slopes. Building on previous work on erosion processes, the study will explore the development of shape and texture metrics from image data. A central question is the suitability of different image sources for this task: whether satellite imagery provides sufficient resolution for erosion detection at scale, or whether higher-resolution UAV-based field imagery is required. The project will include the acquisition of relevant imagery and three months of focused methodological development. The outcomes of the project will include: • A functioning data acquisition and monitoring setup for a sensor-instrumented road section. • Preliminary analysis of local sensing data and conceptual exploration of global sensing approaches. • A set of candidate image-based metrics for erosion detection, tested on satellite and/or UAV imagery.

Researcher(s)

Research team(s)

Project type(s)

  • Research Project

Enhancing Pavement Quality Through AI-Guided Compaction (EPAIC). 01/09/2025 - 31/08/2026

Abstract

Early damage on asphalt pavements is a high and avoidable social and economic cost. Research has shown that only 20% of early pavement failures are due to material defects, , while the remaining 80% can be attributed to the poor construction process itself. Currently these defects are only detected afterwards through core drilling and analysis. Too late, because the road surface has been realized. The innovative EPAIC project aims to the road construction industry in delivering more reliable and sustainable execution of the asphalt compaction process, which better meet established quality standards through the use of spatial and elevation monitoring technologies during road construction. EPAIC is introducing a digitised, AI-driven compaction system capable of continuously monitoring critical construction parameters, along with climatic conditions, during asphalt road construction. By analysing this data in real time using advanced AI algorithms, the system activates a smart alert mechanism that helps operators adjust key process variables during the process, such as the number, speed and frequency of rolling passes. As a result, optimal compaction quality is pursued. This proactive, real-time guidance helps prevent inferior quality on site, which currently can only be determined after the fact. EPAIC thus ensures an extended service life and no accelerated and unforeseen maintenance. EPAIC technology aims to become a market leader and addresses the persistent problem of an asphalt road that does not meet pre established quality standards, contributes to reduced infrastructure maintenance costs, a sustainable environment, reduced emissions and improved public health; factors that today's road construction industry is looking for. With a strong foundation of broad, technical expertise within the SuPAR Group, EPAIC is well positioned to move from concept to commercialisation and implement this technology in modern road construction.

Researcher(s)

Research team(s)

Project type(s)

  • Research Project