Construction IT

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Week 13

Construction IT

457.657 Civil and Environmental Project Management Department of Civil and Environmental Engineering

Seoul National University

Prof. Seokho Chi shchi@snu.ac.kr

건설환경공학부 35동 304호

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1.1 Construction IT

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4차 산업혁명 시대

4차 산업혁명의 정의

4차 산업혁명에 대한 기대

0 10 20 30 40 50 60 70 80 90 100

2013‐04‐01 2014‐02‐01 2014‐12‐01 2015‐10‐01 2016‐08‐01

4차 산업혁명 키워드 관심도(출처: Google Trend)

Industry 4.0 4차 산업혁명

수치는 기간의 차트에서 가장 높은 지점에 대한 검색 관심도를 나타냄. 100은 검색어의 최고 인기도.

0은 검색어의 인기도가 최고 인기도 대비 1% 미만.

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4차 산업혁명과 건설산업

자료: 스위스 최대은행 UBS ‘4차 산업혁명 영향’ 다보스 포럼 보고서 (평가기준: 노동시장 유연성, 기술수준, 교육시스템, SOC 수준, 법제도 등)

4차 산업혁명 준비에 대한 국가 순위

Country Rank

Switzerland 1

Singapore 2

Netherlands 3

Finland 4

United States 5 United Kingdom 6

Hong Kong 7

Norway 8

Denmark 9

New Zealand 10

Sweden 11

Japan 12

Germany 13

Ireland 14

Canada 15

Taiwan 16

Australia 17

Austria 18

Belgium 19

France 20

Israel 21

Malaysia 22

Portugal 23

Czech Republic 24 South Korea 25

Chile 26

Spain 27

China 28

McKinsey Global Institute (2011)

가용 데이터, 데이터 중심의 의사결정습관, IT 인프라 수준 모두 최하위!

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 Information on Construction Projects

$10M and More General Construction Project

420 Stakeholders, 850 People, 50 Kinds of Documents, 56,000 Pages

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 Information through Project Lifecycle

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Difficulties in Information Management and Communication

 Difficulties in PMIS Building and Management

– Unique objectives: Unique construction processes, information needed, management skills – Difficult to achieve uniform, standard information format

– Diverse contractors and suppliers

– Locational diversion  Possible loss of information – Individual tacit knowledge

 Difficulties in Communication

– 1:1 or 1:Small knowledge sharing

– No effort for information analysis and management – Lack of lifecycle information management

*

자원관리

,

구매조달

,

전자문서관리 국한

*출처: 한대근(2013)정보기술 지원도 평가를 통한 건설업무 프로세스 개선에 관한 연구

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Project Management Information Systems

PMIS

 Support Decision Making for Construction Projects

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PMIS Structure

PMIS

 Information Technology (IT)

o Technology itself

o e.g. server, hardware, software, etc.

o Under the information systems umbrella

 Information Systems (IS)

o Large umbrella o Combination of ITs

o Systems designed to create, store, manipulate or disseminate information

 Organizational Structure

o Project IS

o Department IS o Enterprise-Wide IS o Inter-organizational IS

Appliance Server Store 3 Thin Client PC

Appliance

On-line Multi-station Store Appliance

Server Store 3 Thin Client PC

Appliance

On-line Multi-station Store

Store Location 1

Thin Client

PC PC

Thin Client

PC

In-house operations Serial Term inals

Mux Hub Unix

Enterprise Server

Unix Enterprise

Server

DIALUP/T1/T3/ISDN/FRAME RELAY

Store Location 2

DIALUP/T1/T3/ISDN/FRAME RELAY Mux

Mux

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IS and IT

PMIS

 How can IT support strategic IS management?

o Change in processes  Competitive intelligence

o Link with business partners (supply chain management)

o Relationships management between suppliers and customers

*CRM: Customer Relationship Management

*ERP: Enterprise Resource Planning

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PMIS Example

PMIS

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Purpose of PMIS: Value Chain

PMIS

Analyze the internal operations of a corporation to increase its efficiency, effectiveness and competitiveness.

A company analysis by systematically evaluating a company’s key processes and core competencies.

Activity support using IT to adding value to the company and finally maximize the company’s profit!

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PMIS Issues

PMIS

HARD INFRASTRUCTURE  SOFT INFRASTRUCTURE TO PRACTICALITY!

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PM Data Mining

 Big Data  Data Mining + 3V(Volume, Velocity, Variety)

– Knowledge discovery from data

– Extraction of interesting patterns or knowledge from huge amount of data

ALTERNATIVE NAMES

Knowledge discovery (mining) in databases (KDD)

Knowledge extraction Data/pattern analysis Information harvesting

Data Mining in Your Life?

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Purpose of Data Mining: Data  Info.  Knowledge

 Data

o Raw description of things, events, activities and transactions that are recorded, but alone do not convey any specific meaning (e.g. 400,000)

 Information

o Data that have been organized so that they have meaning and value to the recipient (e.g. Current $400,000 house price)

 Knowledge

o Information that has been organized and processed to convey

understanding experience and expertise as they apply to a current

problem or activity (e.g. The current $400,000 house price is cheaper

than the last year’s price. The property market may be deflated.)

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Soccer Player Analysis Using Image Processing

 Raw Data: Recorded Video

 Information: Position Identification & Tracking

o Travel distance, movement, position, time, etc.

 Knowledge: Performance Analysis

o Efficient/Inefficient movement, pass accuracy, reasons for poor performance, etc.

o Planned vs. actual, team formation analysis, set play analysis, etc.

o Use knowledge for team training

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Big Data Technique

(1) Classification (Example: Classifying Mammals)

Model

Learn Classifier

Name Body Temper

ature

Gives Birth

Four-le gged

Hibernates Class Label

Human Warm-blooded Y N N Y

Elephant Warm-blooded Y Y N Y

Leopard shark Cold-blooded Y N N N

Turtle Cold-blooded N Y N N

Penguin Cold-blooded N N N N

Eel Warm-blooded N N N N

Dolphin Warm-blooded Y N N Y

Spiny anteater Cold-blooded N Y Y Y

TRAINING SET

TESTING SET

Name Body Temper

ature

Gives Birth

Four-le gged

Hibernates Class Label

Pigeon Warm-blooded N N N ?

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Big Data Technique

(2) Clustering

o Given a set of data points, each having a set of attributes and a similarity measure among them, find clusters such that

• Data points in one cluster are more similar to one another

• Data points in separate clusters are less similar to one another

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Big Data Technique

(3) Association Rule Discovery

o Given a set of records each of which contain some number of items from a given collection

o Produce dependency rules which will predict occurrence of an item based on occurrences of other items

(4) Sequential Pattern Discovery

o Given is a set of objects, with each object associated with its own timeline of events, find rules that predict strong sequential dependencies among different events.

o Association rule: Concurrent events

o Examples: Computer bookstore: Intro to C++  MFC using C++

Shoes  Racket, Racketball  Sports Jacket

• Marketing and sales promotion

• Supermarket shelf management

• Inventory management

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Big Data Technique

(5) Text Mining

o Plant Inspection Documents

• 28,800 documents during 2 years

• When, where, what issues?

• Relationships among issues?

출처: 조성준 교수(서울대학교 산업공학과, 2013)

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Big Data Technique

When, where, what issues?

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Big Data Technique

Relationships among issues?

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Big Data Technique

Relationships among issues?

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Big Data Technique

Relationships among issues?

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Big Data Technique

Relationships among issues?

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Big Data Application in Construction

 Caterpillar Real-time Equipment Monitoring

 Shimizu Construction: Building Condition Monitoring

 Komatsu Real-time Equipment Monitoring  What’s New?

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SYSTEM DATA

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SYSTEM DATA

교량별 손상상태 추정

언제 어느 교량의 어느 경간의 어느 부재에서 어떤 문제가 발생?

결과물

BMS 교량 안전성능 점검데이터 + 외부환경정보 데이터

 교량별 손상상태 추정 및 원인분석 기술

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SYSTEM DATA

•상부구조 : PSCB

•손상종류 : 주형균열

•상부구조 : PSCI

•손상종류 : 바닥판 철근노출/부식

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SYSTEM DATA

•하부구조 : Wall Pier 벽식 교각

•손상종류 : 교량받침 부식

•하부구조 : 역T형 교대

•손상종류 : 교각균열

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Development of Cycle Estimation Model of Construction Cost Index(CCI) Using Fractal Analysis

Fractal Analysis

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Quantifying Urban Resilience to Natural Disaster Using Spatial Analysis

Urban Disaster Resilience

 Ability to minimize the total socio‐economic impact of disastrous events

 Ultimate outcome differ depending on the urban disaster resilience

Spatial distribution of the recoverability measure

Spatial distributions of the vulnerability measure

Imply relatively weak recoverability compare to other neighborhoods.

however, the results of the case study may not represent actual disaster resilience because of the lack of data and

assumptions.

mean damage state of the district degree of recoverability

Support Decision‐Making

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IMAGE DATA

“

안전관리계획 수립 시 계측장비 및

CCTV

설치

/

운용 계획을 포함토록 하며

,

발주청은 그 비용을 안전관리비에 계상

” (건진법 시행령 제99조, 2016)

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IMAGE DATA

현장에서 수집된 영상 데이터를 자동 분석하여

작업 생산성 및 안전성 분석에 필요한 정보를 실시간으로 제공하는 기술

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IMAGE DATA

무인항공기(UAV) 영상 수집 및 분석을 통해

재해 폐기물 등 3차원 영상의 체적 자동산출 기술

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IMAGE DATA

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TEXT DATA

안전보건공단(KOSHA)

건설안전정보시스템 정밀점검/정밀안전진단보고서

Web Crawling

건설관련 뉴스기사 칼럼

기술자료 정책자료 학술지 논문

전자민원 외

건설현장

회의록 작업일보 공문 시방서

설계변경서류 입찰서류

사업수행계획서 외

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TEXT DATA

교량시설물 손상발생 패턴 분석

•연관규칙 분석 등의 방법론을 활용하여 단어와 단어 간 연관성 파악

Keywords (sorted by PPMI*)

균열 누수 백태 박락 탈락

수지 하부 고형 충돌 개축

주입 교체 염성 원형 완화

피로 빗물 증거 부유 즉시

표준 이음 화의 기술자 무관

폭 신축 수분 우물 배수관

에폭시 부속 캔틸레버 통 타일

허용 배수 보통 분류 배

측정 흔적 백색 책임 햄머

방법 유도 데크 크기 수구

때문 오염 바닥 수중 그레이

균열 누수 백태 박락 탈락

… … 균열 균열 …

콘크리트 균열 … … 균열

… … 누수 콘크리트 …

수축 신축 … 철근 배수관

… … 바닥 … 보수

시공 이음 콘크리트 노출 …

건조 … 판 파손 콘크리트

… 백태 … … 부식

… 파손 철근 분리 …

… 부식 노출 단면 도장

Keywords (sorted by raw frequency)

* PPMI: Positive Pointwise Mutual Information

 단어 간 가중치 (PPMI)

‐특정 손상에 특히 연관이 있는 요인(키워드)

‐ 누수 ~ {하부, 빗물, 이음, 신축, 배수, 흔적, 오염}  신축이음 및 배수관 손상으로 인해 빗물이 하부구조의 오염 유발

‐ 백태 ~ {캔틸레버, 데크, 바닥}  백태의 주 발생 위치는 바닥판, 데크, 캔틸레버부

‐ 탈락 ~ {개축, 즉시, 배수관}  배수관 탈락이 자주 발생하며, 어떤 부재의 탈락은 즉시 개축을 요구하는 중대한 손상

 단어 빈도 (term frequency)

‐일반적이고 잘 알려진 요인(키워드)

‐ 균열 ~ {콘크리트, 수축, 시공, 건조}  시공 시 콘크리트 건조수축으로 인한 균열 발생

‐ 누수 ~ {균열, 신축, 이음, 백태, 파손, 부식} 

신축이음의 균열 및 파손으로 인한 누수가 (하부의) 백태 및 부식 유발

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TEXT DATA

도로안전시설물 교통사고 연관 분석

Association rules for the crash result

‐ 사고 유형별로 교통사고와 연관된 단어를 도출

‐ 도출된 단어를 시각화하여 사고 유형별 특성 도출

Network analysis for freeway safety facility

‐ 도로안전시설물별 교통사고와 연관된 단어를 네트워크 분석을 통해 시각화

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텍스트마이닝 기반 건설 경험정보 웹 인텔리젼스 시스템 개발

41 Developing Web Intelligence System of Construction Tacit Knowledge Based on Text Mining

• UNI(User Needed Information)‐Tacit

Automated keyword extraction and document tagging

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텍스트마이닝 기반 건설 경험정보 웹 인텔리젼스 시스템 개발

42 Developing Web Intelligence System of Construction Tacit Knowledge Based on Text Mining

• UNI(User Needed Information)‐Tacit

Text data visualization

tag (keyword)

wordcloud

list of dataset

keywords

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Step-by-Step Approach to DM

(1) Education

o Understand data mining principles

o Formulate data-oriented thinking habit

(2) Brainstorming

o Discover topics from various disciplines (e.g., 20 data mining projects)

(3) Feasibility Analysis

o Data? Business impact? Analysis level?

(4) Data Collection and Analysis (5) Result Review and Update

o Additional data required

o Review and correct data collection approaches o Improve data quality

o UI/UX development for better implementation

Source: Professor Sungjoon Cho (SNU Industrial Engineering, 2013)

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Issues on Data Mining

 Very New?

 100% Accurate?

 Possible only with Data, HW/SW Infra?

 What is the good model?

 Challenges of Data Mining

o Scalability, Dimensionality

o Complex and heterogeneous data o Data quality

o Data ownership and distribution o Privacy preservation

Source: Professor Sungjoon Cho (SNU Industrial Engineering, 2013)

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SNS Analysis

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 Six must-have construction tech tools

o Drone surveying o 3D Printing

o Smart Roads

o Transparent Solar Panels o Smart Helmet

o Anti-collision Software

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