Students
Tuition Fee
Start Date
Medium of studying
Fully Online
Duration
Details
Program Details
Degree
Courses
Major
Construction | Construction Management | Construction Technology | Civil Engineering
Area of study
Architecture and Construction | Engineering
Education type
Fully Online
Course Language
English
About Program

Program Overview


The Robert H. Buckman College of Engineering Online Education Program

The Robert H. Buckman College of Engineering Online Education Program offers various online degrees and certificates in engineering fields.


Online Programs & Courses

The program provides a range of online courses and programs, including:


  • Doctoral programs:
    • Doctor of Engineering
  • Master programs:
    • Aeronautics & Astronautics
    • Autonomy
    • Biomedical Engineering
    • Civil Engineering
    • Dual Degree MSE+MBA
    • Electrical & Computer Engineering
    • Engineering Education
    • Industrial Engineering
    • Interdisciplinary Engineering
    • Internet of Things
    • Mechanical Engineering
    • Microelectronics and Semiconductors
    • Nuclear Engineering
    • Robotics
    • Software Engineering
    • Systems Engineering
  • Graduate certifications:
    • Applied Heat Transfer
    • Digital Signal Processing
    • Hypersonics
    • International Development
    • Microelectronics and Semiconductors
    • Noise Control
    • Regulatory Affairs and Regulatory Science for Medical Devices
    • Systems
    • Teaching and Learning in Engineering

Professional Certifications

The program also offers professional certifications, including:


  • Lean Six Sigma Green Belt
  • Lean Principles
  • Lean Six Sigma Black Belt
  • Lean Six Sigma Green Belt Refresher
  • Agile Project Management
  • Project Management Essentials
  • PMP Exam Preparation

Professional Courses

Additionally, the program provides professional courses, such as:


  • Semiconductor Fabrication 101
  • Additive Manufacturing
  • Design for Security
  • Data Science
  • Building Water Essentials
  • Model-Based Systems Engineering
  • Product Safety: An Introduction for Effective Engineering Design
  • Small Modular and Advanced Reactor (SMR/AR) Technology
  • Systems Engineering Processes and Professional Competencies

Construction Productivity Course

Course Description

The Construction Productivity course is designed to teach students the importance of increasing productivity in the construction industry. The course covers methods of improving job site productivity via quantitative methods, risk or variation analysis, benchmarking, productivity defect reduction, resource utilization methods, and proven personnel management non-quantitative methods.


Course Details

  • Credit Hours: 3
  • Learning Objective: Students will learn the reasons for low construction industry productivity and explore various means to increase construction productivity.
  • Description: The course is conducted via asynchronous distant learning and live Zoom review sessions.
  • Topics Covered:
    • Introduction to Productivity
    • Reasons for Low Productivity
    • The Need for a Procedures Manual for Consistent Practices
    • Pre-Planning and Productivity
    • Personnel Practices for Improving Construction Productivity
    • Defect Analysis, LEAN, Six Sigma and TQM
    • Productivity Production Models
    • Using New Technology to Increase Construction Productivity
    • Human Factor Engineering
    • Control and Productivity
  • Prerequisites: B.S. in Civil Engineering, Construction Management and Technology, B.S. in Engineering
  • Applied / Theory: 90/10
  • Homework: Students will watch 44 streaming video lectures and complete 10 multiple-choice questions for each lecture.
  • Projects: Seven exercises to complete a job-related Construction Productivity Manual
  • Exams: None
  • Textbooks: Optional - Adrian, James J.; Measuring and Improving Construction Productivity, 4th edition Stipes Publishing, 2004 ISPB -8
  • Computer requirements: Exercises will be prepared using Microsoft programs, including Word, Excel, and PowerPoint. A statistical program for regression analysis will be used for one exercise.

Instructor

  • James Adrian
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