About

Dulcy Abraham is a Professor of Civil Engineering at Purdue University, with campus office located in CIVL 1241 and Civil and Construction Engineering office in HAMP 1241. Her contact information includes a phone number (765) 494-2239 and email dulcy@purdue.edu. She is affiliated with the Civil and Construction Engineering department and participates in the Construction Engineering and Management Committees, as well as serving on the Engineering Faculty on the University Senate. Her research focus, background, and key contributions are not detailed in the provided page text.

Research topics

• Computer Science
• Transport engineering
• Mathematics
• Artificial Intelligence
• Engineering
• Mathematical optimization
• Machine Learning
• Data Mining
• Systems engineering
• Econometrics
• Statistics
• Operations research
• Economics
• Business
• Reliability engineering

Selected publications

• Pocket LiDAR for Dimensional Measurement of Precast Concrete Panels

  Open MIND · 2026-04-14

  otherOpen access

  This study evaluates the feasibility of pocket LiDAR as a low-cost solution for dimensional quality control (QC) of precast concrete elements. Traditional methods rely on manual tape measurements, which are labor-intensive and prone to human error. High-precision technologies such as terrestrial laser scanning (TLS) are expensive and difficult to operate. To bridge this gap, this research assesses pocket LiDAR based on comparisons with TLS and tape measurements through controlled experiments. Point cloud data of a concrete slab specimen were collected in both pre-casting and post-casting stages using TLS and an iPhone LiDAR system. The datasets were evaluated in terms of point cloud fidelity, dimensional accuracy, and compliance with industry tolerances. Results show that pocket LiDAR achieved a root mean square error (RMSE) of 5.5 mm in rebar position measurements compared to TLS, significantly outperforming tape measurements (45.8 mm RMSE). For concrete dimensional measurements, pocket LiDAR demonstrated comparable accuracy to tape measurements. Compliance analysis further indicated that pocket LiDAR can reliably identify most tolerance violations in rebar position and concrete dimension, although minor discrepancies were observed in borderline cases. Overall, pocket LiDAR demonstrates strong potential as a practical alternative for rapid and automated QC in precast construction.

  PublisherDOI

• Pocket LiDAR for Dimensional Measurement of Precast Concrete Panels

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-14

  otherOpen access

  This study evaluates the feasibility of pocket LiDAR as a low-cost solution for dimensional quality control (QC) of precast concrete elements. Traditional methods rely on manual tape measurements, which are labor-intensive and prone to human error. High-precision technologies such as terrestrial laser scanning (TLS) are expensive and difficult to operate. To bridge this gap, this research assesses pocket LiDAR based on comparisons with TLS and tape measurements through controlled experiments. Point cloud data of a concrete slab specimen were collected in both pre-casting and post-casting stages using TLS and an iPhone LiDAR system. The datasets were evaluated in terms of point cloud fidelity, dimensional accuracy, and compliance with industry tolerances. Results show that pocket LiDAR achieved a root mean square error (RMSE) of 5.5 mm in rebar position measurements compared to TLS, significantly outperforming tape measurements (45.8 mm RMSE). For concrete dimensional measurements, pocket LiDAR demonstrated comparable accuracy to tape measurements. Compliance analysis further indicated that pocket LiDAR can reliably identify most tolerance violations in rebar position and concrete dimension, although minor discrepancies were observed in borderline cases. Overall, pocket LiDAR demonstrates strong potential as a practical alternative for rapid and automated QC in precast construction.

  PublisherDOI

• Knowledge-Based Digital Inspection System for Training Construction Inspectors

  Transportation Research Record Journal of the Transportation Research Board · 2026-05-07

  article

  State transportation agencies face growing challenges in training construction inspectors owing to a shrinking experienced workforce and the increasing complexity in inspection tasks. Traditional document-based training methods are often fragmented and lack contextual depth, limiting their effectiveness in preparing novice inspectors. This study presents a knowledge-based digital inspection training system that consolidates inspection information from multiple Indiana Department of Transportation (INDOT) documents—including Standard Specifications, General Instructions to Field Employees, Indiana Testing Methods, standard drawings, and Manual for Frequency of Sampling and Testing and Basis for Use of Materials—into a semantically structured knowledge graph. The inspection training system enhances learning through the integration of rationale, instructions, construction pitfalls, and failure scenarios associated with inspection tasks. A user-centered web application was developed to deliver this content in an intuitive format aligned with how inspectors naturally perform their duties—by pay item, construction process, or risk scenario. The system was evaluated through a mock exercise involving INDOT inspectors. Results showed that the system is effective in improving the construction inspectors’ confidence and understanding of inspection rationale, instructions, and risk consequences of missed inspection. This research contributes a scalable, risk-informed framework that improves accessibility and comprehension of inspection knowledge, with the potential to foster proactive inspection behaviors and support more consistent construction quality control across transportation projects.

  PublisherDOI

• Advancing the Science and Practice of Pipeline Condition Assessment in Wastewater Pipes

  2025-08-07

  articleCorresponding

  Pipeline Condition Assessment (PCA) is essential for the effective management of wastewater infrastructure, providing critical data that guides decisions on the maintenance, rehabilitation, and replacement of aging pipelines. Despite notable advancements, PCA practices in wastewater systems continue to face challenges due to the complexities of subsurface environments, the limitations of current technologies, and the increasing demands of urban infrastructure. This paper presents a synthesis of past studies of PCA practices in wastewater systems and identifies limitations in current PCA practices. The study examines the deterioration mechanisms and factors contributing to the failure of commonly used wastewater pipe materials, including Vitrified Clay Pipe (VCP), Cast Iron Pipe (CIP), Ductile Iron Pipe (DIP), Pre-stressed Concrete Cylinder Pipe (PCCP), Polyvinyl Chloride (PVC) pipe, High-density Polyethylene (HDPE) pipe, and Polymer Concrete Pipe. An overview of anomaly-detection capabilities, operational principles, and application areas of state-of-practice PCA technologies for wastewater systems is provided. Key methodologies, such as visual, electromagnetic, and acoustic inspection techniques, are analyzed in terms of their performance across different pipe materials, diameters, operational rates, data output formats, and accuracy in measuring anomaly dimensions, including depth, length, and width. By synthesizing research findings from the past, the review highlights both the progress made and the limitations that persist in the field. The findings emphasize the need for continuous innovation in PCA technologies and the advancement of practices to enhance the reliability and efficiency of wastewater pipeline condition assessments.

  PublisherDOI

• Probabilistic optimization of pavement preventive maintenance using multi-objective genetic algorithm

  Innovative Infrastructure Solutions · 2025-04-24 · 2 citations

  article
  PublisherDOI

• User Manual for Digital Inspection Training System

  2025-01-01

  reportSenior author

  Construction inspection plays a pivotal role in ensuring the quality and long-term performance of infrastructure products. INDOT is currently facing the challenge to effectively train novice inspectors and update experienced inspectors with the latest versions of quality requirements to perform construction inspection effectively and more efficiently. This project developed a digital inspection training system to train INDOT inspectors to inspect asphalt pavement construction. Inspectors can access the inspection instructions through a construction process-based approach or a pay item-based approach. The inspection guidance is developed using data and information from INDOT’s Standard Specification (2024), Manual for Frequency of Sampling and Testing and Basis for Use of Materials, General Instructions to Field Employees, Standard Drawings, risk-based check items developed in previous JTRP projects (SPR 4002 and SPR 4422), INDOT training materials, and relevant external references from Federal Highway Administration, state transportation agencies, and reports of National Cooperative Highway Research Program (NCHRP) projects. The system is implemented through a Web platform. It serves as a training tool for novice inspectors and a quick reference for experienced inspectors seeking update-to-date specifications and guidelines.

  PublisherDOI

• 497 Use of Virtual Reality in Adjunct to Anesthesia in Surgery and Anesthesia Requiring Procedures: A Systematic Review and Meta-Analysis

  Annals of Emergency Medicine · 2024-09-25

  review
  PublisherDOI

• Probabilistic Optimization of Pavement Preventive Maintenance Using Multi-Objective Genetic Algorithm

  Research Square · 2024-07-23

  preprintOpen access
  PublisherOA PDFDOI

• Field Handbook for Maintenance and Preservation Treatments of Concrete Pavements

  2024-01-01

  reportOpen access

  Information related to maintenance and preservation (M&P) treatments of Portland cement concrete pavement (PCCP) has not been uniformly presented across various Indiana Department of Transportation (INDOT) maintenance-related documents, including INDOT Standard Specifications and the INDOT Design Manual. Since this data is scattered across different documents and frequently incomplete, it is often challenging for field personnel to obtain consistent information which can assist in making decisions related to selection of treatments that can benefit the service life of concrete pavements. To address this gap, the SPR-4601 guidebook was developed to provide succinct descriptions of common distresses and failures observed in concrete pavements, and guidance related to routine maintenance and preservation (M&P) practices. Having consistent and comprehensive information should aid in implementing more uniform M&P practices and help to ensure the quality of the concrete pavement assets over their service life.

  PublisherOA PDFDOI

• Review of Probabilistic Modeling of Pavement Performance Using Markov Chains

  Preprints.org · 2024-07-24 · 1 citations

  preprintOpen access

  Reliable models of pavement performance play a crucial role in effective decision-making for maintaining and rehabilitating this class of infrastructure assets. Probabilistic modeling approaches have gained popularity in pavement performance modeling because they account not only for the stochastic nature of pavement behavior and deterioration factor variations but also for the imperfections and inadequacy of pavement condition data in certain situations. One of these approaches, Markov chains, has been used extensively to model the probabilistic performance of pavements through an interesting variety of methodological tweaks in the Markov model structure. Unfortunately, the current literature lacks a synthesis of Markov chain models and their associated methodologies, as used in this manner. It is anticipated that a comprehensive synthesis of these models and their various forms can provide some insight into the variations of Markov model forms and methodologies, and the appropriate Markov model type to use for pavement deterioration and performance modeling under given conditions of data types and availability. To address this issue, this paper reviews Markov chain models used in the literature to model pavement deterioration and the methodologies used to estimate the transition probabilities matrix which is the key feature of Markov chain models. The paper presents a critical analysis of various aspects of Markov chain models as they were applied in the literature, reveals gaps in knowledge, and offers suggestions to address these gaps. The paper also develops a decision tree to select the appropriate Markov model type and TPM estimation methodology to model pavement deterioration under given conditions of data availability. This paper therefore provide guidance and decision support for researchers and highway agencies in selecting an appropriate probabilistic technique for modeling their pavement infrastructure performance in a robust manner.

  PublisherOA PDFDOI

Frequent coauthors

• W.J. Croisant

  Construction Engineering Research Laboratory

  36 shared

• Victor M. Nakano

  U.S. Army Engineer Research and Development Center

  34 shared

• Fred Mannering

  34 shared

• Hyung Seok Jeong

  32 shared

• Carlos A. Arboleda

  22 shared

• Tom Iseley

  Purdue University West Lafayette

  20 shared

• Robert M. Lubitz

  Indiana University – Purdue University Indianapolis

  19 shared

• Ali Mostafavi

  Mitchell Institute

  15 shared

Awards & honors

• Purdue Engineering Distinguished Lecture Series
• Neil Armstrong Distinguished Visiting Professors
• Lillian Gilbreth Postdoctoral Fellowships at Purdue Engineer…