Ahmed Moussa

ORCID: 0000-0003-0635-9834
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About
Contact & Profiles
Research Areas
  • BIM and Construction Integration
  • Construction Project Management and Performance
  • Occupational Health and Safety Research
  • Advanced Data Processing Techniques
  • Innovations in Concrete and Construction Materials
  • Complex Systems and Decision Making
  • Risk and Safety Analysis
  • Advanced Sensor and Control Systems
  • Advanced Algorithms and Applications
  • Engineering Diagnostics and Reliability
  • Oil and Gas Production Techniques
  • Infrastructure Maintenance and Monitoring
  • Wireless Sensor Networks and IoT
  • Big Data and Business Intelligence
  • Infrastructure Resilience and Vulnerability Analysis
  • Advanced Manufacturing and Logistics Optimization
  • Cloud Computing and Resource Management
  • Blockchain Technology Applications and Security
  • IoT and Edge/Fog Computing

McMaster University
2022-2025

Cairo University
2021

10.1016/j.techfore.2023.122347 article EN Technological Forecasting and Social Change 2023-02-13

10.1007/s41315-023-00275-1 article EN International Journal of Intelligent Robotics and Applications 2023-02-23

10.1061/jcemd4.coeng-15381 article EN Journal of Construction Engineering and Management 2025-04-23

Complex by nature, infrastructure megaprojects rarely meet stakeholders' expectations. A key characteristic of such complexity is the interdependence among different project stakeholders (e.g., contractors) where disruption one contractor's work may instigate (system-level) systemic risks, resulting in poor performance indicators (KPIs) whole project. Attributed to lack appropriate analysis and quantification tools, managing risks from remains challenging. The current study fills this...

10.1061/(asce)me.1943-5479.0001071 article EN cc-by Journal of Management in Engineering 2022-07-04

Abstract Early and accurate detection of operational anomalies in sucker-rod pumping wells is crucial. Maximizing production while minimizing downtime essential. Traditionally, dynamometer (dyno) card classification has been labor-intensive inefficient, especially with increasing high-frequency sensor data. To address this, a Semi-supervised Generative Adversarial Network (SSGAN) proposed. It classifies pump conditions. The model uses both labeled unlabeled dyno learns the underlying data...

10.2118/223190-ms article EN 2024-10-20
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