6 papers
Bayesian-Monte Carlo Schedule Updating for Construction Digital Twins: A Probabilistic Framework for Dynamic Project Forecasting
Atena Khoshkonesh, Mohsen Mohammadagha, Vinayak Kaushal +1
Construction projects frequently experience schedule delays and forecasting uncertainty due to variability in labor productivity, material availability, weather conditions, and pro…
Lean 5.0: A Predictive, Human-AI, and Ethically Grounded Paradigm for Construction Management
Atena Khoshkonesh, Mohsen Mohammadagha, Navid Ebrahimi +1
This paper introduces Lean 5.0, a human-centric evolution of Lean-Digital integration that connects predictive analytics, AI collaboration, and continuous learning within Industry…
Simulation-Based Validation of an Integrated 4D/5D Digital-Twin Framework for Predictive Construction Control
Atena Khoshkonesh, Mohsen Mohammadagha, Navid Ebrahimi
Persistent cost and schedule deviations remain a major challenge in the U.S. construction industry, revealing the limitations of deterministic CPM and static document-based estimat…
Integrated 4D/5D Digital-Twin Framework for Cost Estimation and Probabilistic Schedule Control: A Texas Mid-Rise Case Study
Atena Khoshkonesh, Mohsen Mohammadagha, Navid Ebrahimi
Persistent cost and schedule overruns in U.S. building projects expose limitations of conventional, document-based estimating and deterministic Critical Path Method (CPM) schedulin…
Region-of-Interest Augmentation for Mammography Classification under Patient-Level Cross-Validation
Farbod Bigdeli, Mohsen Mohammadagha, Ali Bigdeli
Breast cancer screening with mammography remains central to early detection and mortality reduction. Deep learning has shown strong potential for automating mammogram interpretatio…
Comprehensive Review of Analytical and Numerical Approaches in Earth-to-Air Heat Exchangers and Exergoeconomic Evaluations
Saeed Asadi, Mohsen Mohammadagha, Hajar Kazemi Naeini
In recent decades, Earth-to-Air Heat Exchangers (EAHEs), also known as underground air ducts, have garnered significant attention for their ability to provide energy-efficient cool…