Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Fall Semester
Year 2025 352123 Application of Computational Intelligence in Construction Management 1 3.0 3 ★ Chi-Yun Liu GCDPE 14 0 土木208

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2025	352123	Application of Computational Intelligence in Construction Management	1	3.0	3	★	Chi-Yun Liu	GCDPE	14	0	土木208

Course Plan：

Instructor Chi-Yun Liu　Office Hours

Email cyliu@mail.ntut.edu.tw

Last Updated 2025-08-09 12:11:17

Course Objective 在當代營建產業中，項目日益複雜且數據量龐大，傳統管理方法已難以應對高效率、低成本和永續發展的需求。計算智慧作為人工智慧的重要分支，能有效處理不確定性、非線性和高維度問題，為營建管理帶來突破性變革。本課程系統性介紹計算智慧技術（涵蓋機器學習、深度學習、模糊系統、進化計算等）如何應用於營建管理各環節。本課程旨在培養同學理解各類演算法的基本原理與適用場景，並透過專案練習及軟體操作讓同學具備計算智慧技術的應用能力。 In today's construction industry, projects are becoming increasingly complex with massive volumes of data, making traditional management approaches inadequate to meet the demands for high efficiency, cost-effectiveness, and sustainable development. Computational intelligence, as a crucial branch of artificial intelligence, can effectively address uncertainties, nonlinearities, and high-dimensional problems, bringing transformative changes to construction management. This course systematically introduces how computational intelligence technologies, such as machine learning, deep learning, fuzzy systems, evolutionary computation, and more, can be applied across various aspects of construction management. The course aims to equip students with an understanding of the fundamental principles and practical applications of multiple algorithms while also developing their skills in applying computational intelligence techniques through hands-on projects and software implementation.

Course Schedule Week 1: Course Introduction and Optimization Needs in Construction Week 2: Fundamentals of Optimization Problems Week 3: Heuristics vs. Metaheuristics Week 4: Genetic Algorithms (GA) Week 5: Evolution Strategies (ES) and Differential Evolution (DE) Week 6: Particle Swarm Optimization (PSO) Week 7: Ant Colony Optimization (ACO) Week 8: Artificial Bee Colony (ABC) and Bat Algorithm (BA) Week 9: Simulated Annealing (SA) and Tabu Search (TS) Week 10: Multi-Objective Optimization Week 11: Harmony Search (HS) and Firefly Algorithm (FA) Week 12: Cuckoo Search (CS) and Grey Wolf Optimizer (GWO) Week 13: Hybrid and Memetic Algorithms Week 14: Parameter Tuning and Control in Metaheuristics Week 15: Discretization and Encoding Strategies Week 16: Swarm Intelligence Extensions Week 17: Mathematically-Inspired Metaheuristics Week 18: Recent Trends and Advanced Applications

Evaluation and grading policy Attendance and participation: 20% Mid-term project (application & presentation): 35% Final project (application, report & presentation): 45%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
References (for supplementary reading, not the main textbook) Talbi, E. G. Metaheuristics: From Design to Implementation. Wiley. Yang, X. S. Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley.

The access to curricular consultation Office hours are held on Wednesdays from 11:00 to 15:00.

Expanding teaching and resources

The course corresponds to the SDGs SDG4：優質教育（Quality Education）
SDG8：尊嚴就業與經濟發展（Decent Work and Economic Growth）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
SDG11：永續城市與社區（Sustainable Cities and Communities）
SDG12：負責任的消費與生產（Responsible Consumption and Production）
SDG13：氣候行動（Climate Action）

Does the course incorporate AI ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI）

Note
　

Instructor	Chi-Yun Liu　Office Hours
Email	cyliu@mail.ntut.edu.tw
Last Updated	2025-08-09 12:11:17
Course Objective	在當代營建產業中，項目日益複雜且數據量龐大，傳統管理方法已難以應對高效率、低成本和永續發展的需求。計算智慧作為人工智慧的重要分支，能有效處理不確定性、非線性和高維度問題，為營建管理帶來突破性變革。本課程系統性介紹計算智慧技術（涵蓋機器學習、深度學習、模糊系統、進化計算等）如何應用於營建管理各環節。本課程旨在培養同學理解各類演算法的基本原理與適用場景，並透過專案練習及軟體操作讓同學具備計算智慧技術的應用能力。 In today's construction industry, projects are becoming increasingly complex with massive volumes of data, making traditional management approaches inadequate to meet the demands for high efficiency, cost-effectiveness, and sustainable development. Computational intelligence, as a crucial branch of artificial intelligence, can effectively address uncertainties, nonlinearities, and high-dimensional problems, bringing transformative changes to construction management. This course systematically introduces how computational intelligence technologies, such as machine learning, deep learning, fuzzy systems, evolutionary computation, and more, can be applied across various aspects of construction management. The course aims to equip students with an understanding of the fundamental principles and practical applications of multiple algorithms while also developing their skills in applying computational intelligence techniques through hands-on projects and software implementation.
Course Schedule	Week 1: Course Introduction and Optimization Needs in Construction Week 2: Fundamentals of Optimization Problems Week 3: Heuristics vs. Metaheuristics Week 4: Genetic Algorithms (GA) Week 5: Evolution Strategies (ES) and Differential Evolution (DE) Week 6: Particle Swarm Optimization (PSO) Week 7: Ant Colony Optimization (ACO) Week 8: Artificial Bee Colony (ABC) and Bat Algorithm (BA) Week 9: Simulated Annealing (SA) and Tabu Search (TS) Week 10: Multi-Objective Optimization Week 11: Harmony Search (HS) and Firefly Algorithm (FA) Week 12: Cuckoo Search (CS) and Grey Wolf Optimizer (GWO) Week 13: Hybrid and Memetic Algorithms Week 14: Parameter Tuning and Control in Metaheuristics Week 15: Discretization and Encoding Strategies Week 16: Swarm Intelligence Extensions Week 17: Mathematically-Inspired Metaheuristics Week 18: Recent Trends and Advanced Applications
Evaluation and grading policy	Attendance and participation: 20% Mid-term project (application & presentation): 35% Final project (application, report & presentation): 45%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes References (for supplementary reading, not the main textbook) Talbi, E. G. Metaheuristics: From Design to Implementation. Wiley. Yang, X. S. Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley.
The access to curricular consultation	Office hours are held on Wednesdays from 11:00 to 15:00.
Expanding teaching and resources
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） SDG8：尊嚴就業與經濟發展（Decent Work and Economic Growth） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure） SDG11：永續城市與社區（Sustainable Cities and Communities） SDG12：負責任的消費與生產（Responsible Consumption and Production） SDG13：氣候行動（Climate Action）
Does the course incorporate AI	● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） ● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI）
Note