Mamunul Karim

PhD student in civil engineering at Arizona State University. I work on construction worker safety and cost estimation, building data-driven frameworks that project teams can actually follow — informed by two years as a project engineer in the field.

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Portrait of Mamunul Karim
Based inTempe, Arizona
ProgramPhD Civil Engineering, ASU
FocusWorker safety · Cost estimation · Scheduling
StatusGRA at SAVE Lab · Actively seeking summer internship

About

I am a project engineer by background and now a PhD student in civil engineering at Arizona State University, working in the SAVE Lab on construction worker safety. My field experience spans highway construction, geotechnical work, and BIM coordination across Bangladesh and the United States.

I am especially interested in how data and AI can make construction work clearer, safer, and more predictable for the people who actually use it — from cost estimates and schedules to safety monitoring on site. I am looking for a summer internship in field engineering, project engineering, estimating, or scheduling.

Selected work

Research and applied projects across construction safety, cost estimation, and field data.

What sends underground pipeline crews to the hospital

2026 – ongoing
Problem
Underground utility work carries some of the highest injury and fatality rates in construction, but incident data sits scattered across reporting systems and is rarely examined for pattern rather than compliance.
Tools
Python and R for data handling and analysis, survey design, systematic literature review.
Status
Ongoing doctoral research at ASU. Findings in preparation.

A machine learning framework for item-level cost estimation

2024 – 2026
Problem
State DOTs routinely see large gaps between the engineer's estimate and the awarded bid. WYDOT had observed deviations up to 25%, but nobody had established whether the gap was random noise or concentrated in identifiable items — and uniform estimating models treat every bid item as if it behaved the same way.
Tools
Python (scikit-learn), R, IBM SPSS.
Outcome
Average deviations of 21.41% (WYDOT), 19.10% (MDT), 48.20% (CDOT) and 24.00% (UDOT), concentrated in a small set of items rather than spread evenly. Funded by a competitive UTC research grant through CTIPS with USDOT and WYDOT as partners; published in the International Journal of Construction Management.

Tracking material deliveries from site video

2021 – 2022
Problem
Material deliveries are logged by hand at the gate, so site inventory drifts away from the paperwork and discrepancies only surface at reconciliation.
Tools
YOLOv5 object detection, deep learning pipeline in Python.
Outcome
83% accuracy on vehicle detection and 88% on quantity calculation. Accuracy fell in poor light and with partially occluded loads, which is the honest limit on deploying this at a real gate. Runner-up in the Builtech Fest case study competition at KUET.

A framework for how construction teams and AI divide the work

2026
Problem
Construction management literature discusses AI adoption at length but offers little structure for deciding which decisions stay with people, which move to models, and which need both.
Tools
AI-assisted literature screening, with manual verification of every included source.
Outcome
Accepted to the International Conference on Industrialized Construction (IConIC 2026). The framework is conceptual and still needs testing against live project decisions.

Experience

Graduate Research Associate

SAVE Lab, School of Sustainable Engineering and the Built Environment, Arizona State University
2026 – Present
  • Research on construction worker safety spanning underground pipeline incidents, heat stress, mental health, and workforce development.
  • Analyzed underground pipeline construction accident data from 2000–2026 and compared incident trends before and after COVID-19.
  • Designed a survey instrument assessing the current state of construction workers' mental health.

Graduate Research Assistant

Civil & Architectural Engineering and Construction Management, University of Wyoming
2024 – 2026
  • Led a data-driven investigation into cost estimation accuracy across more than 1,500 highway infrastructure projects for CTIPS-015, partnered with USDOT and WYDOT.
  • Acquired and processed bid tabulation datasets from four state DOTs, computing average deviations of 21.41%, 19.10%, 48.20% and 24.00% for WYDOT, MDT, CDOT and UDOT.
  • Applied K-means clustering, modified Z-score screening, ANOVA, and Pareto analysis to isolate the bid items driving systemic over- and under-estimation.
  • Contributed to a successfully funded grant proposal on knowledge management in construction projects.

Project Engineer

Bang Jin Group, Bangladesh
Feb – Jul 2024
  • Pioneered prefabricated vertical drain ground improvement in Bangladeshi highway construction, covering geotechnical investigation, materials testing, QC inspection, and field monitoring of 15,000+ drain installations, cutting the consolidation period by 40%.
  • Managed scheduling, cost estimation, budget tracking, life-cycle cost analysis, and earned value analysis on a $6M roadway expansion using Primavera P6 and MS Project, achieving 92% schedule adherence.
  • Ran materials testing programs including field density (nuclear gauge and sand cone), aggregate gradation, asphalt mix design verification, and concrete cylinder testing against AASHTO and RHD specifications.
  • Produced RFIs, contractor submittals, daily construction reports, quantity take-offs, pay item documentation, and as-built markups, and gave on-site technical direction to crews.
  • Delivered field engineering, construction inspection, and safety oversight across a 108 km highway corridor, coordinating QA and regulatory compliance with BWDB, RHD, LGED, and PWD.

Site Engineer & BIM Modeler (Intern)

United Purpose & Amigos Construction, Bangladesh
Jan 2022 – Jan 2024
  • Managed construction documentation including 50+ RFIs, contractor submittals, working drawings, and change orders.
  • Supported closeout: punch list completion, warranty documentation, and final inspection coordination.
  • Built 3D BIM models in Revit, produced site grading plans and earthwork calculations in AutoCAD Civil 3D, and ran drainage design in HydroCAD.
  • Performed structural design calculations for beams, columns, and slabs in ETABS per ACI, ASCE, and BNBC, and produced photorealistic renderings in Lumion for stakeholder presentations.
  • Supervised pile casting and material quality control on site, and resolved field issues with contractors and project teams.

Teaching Assistant

Building Engineering and Construction Management, KUET
Mar – Aug 2023
  • Assisted courses in construction estimating, building materials, construction techniques and equipment, and BIM.
  • Ran hands-on instruction in AutoCAD, Revit, Navisworks Manage, and Primavera P6, demonstrating collaborative modeling and scheduling workflows.

Education

PhD, Civil Engineering

Arizona State University · Expected May 2029
2026 – Present

MS, Civil Engineering — Construction Engineering and Management

University of Wyoming · CGPA 3.67 / 4.00
2024 – 2026

BSc, Building Engineering and Construction Management

Khulna University of Engineering & Technology · CGPA 3.37 / 4.00
2017 – 2022

Let's connect

Happy to talk about anything related to AI in construction, field engineering, research collaboration, or internship and full-time opportunities. I am always open to new connections.

mkarim29@asu.edu

Also reachable at joymamunulkarim@gmail.com

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