Course title
H00140002
Mathematics for Civil Engineering 2

KONNO Katsuaki

HIRABAYASHI Yukiko
Course description
Students will learn the fundamentals of probability statistics and linear algebra in this course.
During the first half of the lecture, students will focus on the basics of probability statistics. In the field of civil engineering, various aspects of planning, design, construction, and maintenance rely on experimental and statistical data. To analyze this data objectively and rationally, a solid understanding of probability statistics is essential.
In the second half of the course, students will delve into basic linear algebra. Many fields, such as surveying and structural design, involve computerized mass calculations, which rely on the principles of vectors and matrices in linear algebra.
Purpose of class
Understand how to analyze statistical data and how statistics is utilized and used in the field of civil engineering. Learn and understand computational examples utilizing linear algebra vectors and matrices in the field of civil engineering.
Goals and objectives
  1. Students will be able to understand various probability distributions (normal, lognormal, Weibull, and Poisson) and can draw distribution shapes using Excel.
  2. Using statistical test methods (t-test and chi-square test), students can calculate tests of difference of means and tests of distribution shapes by Excel.
  3. Students can understand the fundamentals of matrices and eigenvalue problems, and be able to compute solutions to simultaneous equations in Excel.
  4. Students will be able to understand and explain the mathematical fundamentals of matrices used in civil engineering.
Relationship between 'Goals and Objectives' and 'Course Outcomes'

Assignment 1st examination 2nd examination Total.
1. 5% 20% 0% 25%
2. 5% 20% 0% 25%
3. 5% 0% 20% 25%
4. 5% 0% 20% 25%
Total. 20% 40% 40% -
Evaluation method and criteria
The evaluation method is based on homework (20%), mid-term exam (40%), and final exam (40%), with a total score of 100 points. A score of 60 or higher is considered to be a passing grade (achievement of learning and educational objectives).
Language
Japanese
Class schedule

Class schedule HW assignments (Including preparation and review of the class.) Amount of Time Required
1. The Role of probability statistics in engineering
- Design and decision making under uncertainty
Read the syllabus to get an overview of the lecture objectives and lecture content. Purchase a reference book 120minutes
2. Analytical Model for Uncertain Phenomena 1
- Normal distribution
- Log-normal distribution
Study normal distribution and lognormal distribution. 120minutes
3. Analytical Model for Uncertain Phenomena 2
- Poisson distribution
Study Poisson distribution. 120minutes
4. Statistical inference based on observed data 1
- Role of statistical inference in engineering
- Sample mean
- Sample variance
Study statistical inference using observed data. 120minutes
5. Statistical inference based on observed data 2
- Reliability test
- Reliability Interval
Study statistical tests and confidence intervals. 120minutes
6. Statistical inference based on observed data 3
- chi-square test
Study the chi-square test. 120minutes
7. Mid-term examination Understand the first half of the lecture contents and prepare for the exam 240minutes
8. Basics of matrices
- Matrix and vector arithmetic
- Four arithmetic operations on matrices, transposed matrices, and inverse matrices
- Matrix representation and solution of simultaneous equations (both in Excel (using inverse matrix) and by hand (sweep method))
Students who have difficulty with matrices should review the basics. 120minutes
9. Regression and correlation analysis
- Estimation of regression equations by the least squares method
- Correlation coefficient
Understand regression equations using the least-squares method. 120minutes
10. Eigenvalue problems 1
- Matrix formulas
- Eigenvalue problem
Review determinants. 120minutes
11. Eigenvalue problems 2
- Application of eigenvalue problems
Review and deepen understanding of eigenvalue problems. 120minutes
12. Applications of matrices 1
- Image processing for photogrammetry and remote sensing
- Rotation and translation matrices, affine transforms
Review matrices used in image processing. 120minutes
13. Applications of matrices 2
- Projective transformation, Adamar product
Review the concept of projective transformations and Adamar products 120minutes
14. Final examination Understand the second half of the lecture contents and prepare for the exam 240minutes
Total. - - 1920minutes
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
Textbooks and reference materials
参考書:菅 民郎著:Excelで学ぶ統計解析入門 Excel2016/2013対応版 オーム社
Prerequisites
Basic Excel usage should be understood.
Office hours and How to contact professors for questions
  • Questions will be taken after the lecture.
Regionally-oriented
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates a basic problem-solving skills
Active-learning course
N/A
Course by professor with work experience
Work experience Work experience and relevance to the course content if applicable
N/A N/A
Education related SDGs:the Sustainable Development Goals
  • 9.INDUSTRY, INNOVATION AND INFRASTRUCTURE
Last modified : Thu Mar 06 10:02:16 JST 2025