Viva presentation for PhD Civil Engineering (Construction Management) Areas: Tall Buildings Risk Management Machine Learning/Artificial Intelligence Digital Construction

1. A MACHINE LEARNING-BASED FRAMEWORK
FOR DELAY MITIGATION IN TALL BUILDING
PROJECTS
MUIZZ OLADAPO SANNI-ANIBIRE
PKA 173088
Supervised by:
PROF. IR. DR. ROSLI MOHAMAD ZIN
ASSOC. PROF. DR. SUNDAY OLUSANYA OLATUNJI

2. OUTLINE
 Introduction
 Problem Statement
 Research Objectives
 Conceptual Framework
 Literature Review
 Construction delays
 Identifying causes of construction delays
 Country related studies
 Project related studies
 Frameworks for Construction Delay Mitigation
 Overview and Application of Machine Learning in
Construction
 Research Method
 Phases of Research Method
 Model Development Process
 Architecture of Multi Classifier System
 Results and Discussions
 Systematic Literature Review
 Causes of Delay in Tall Buildings
 ML Models for Cost, Duration and Delay Risk
 Delay Mitigation Framework
 Conclusions of the Study
 Summary of Conclusions
 Contributions of the Study
 Current Achievements
 Expected Research Outputs

3. INTRODUCTION

4. PROBLEM STATEMENT
 Urbanization will add 2.5 billion people to urban populations
by 2050, and consequently 66% of the world’s population
will inhabit urban centres (United Nations).
 Building tall is a viable solution to creating urban space in
areas where there exists concentrated population, scarcity
of land, and high land costs.

5. SOURCE: Council of Tall Buildings and Urban Habitat (CTBUH)
PROBLEM STATEMENT
COST
QUALITY
PROJECT
SUCCESS
TIME

6.  The research domain is saturated with an abundance of literature on the causes of delay in
the construction industry, though few studies have dealt with tall building projects.
 AlSehaimi et al. (2013) suggests that most of the delay studies are descriptive and
exploratory in nature, hence, are potentially inadequate in mitigating delays.
 There are few studies that have proposed construction delay mitigation frameworks.
Despite, their contribution to the body of knowledge, they simply provide recommendations
or managerial steps for delay mitigation, and cannot be considered prescriptive in their
approach, thus the problem of delay in construction is still prevalent
 Nowadays, the mantra of the construction industry is the fourth industrial revolution (IR
4.0) and its accompanying digital technologies such as machine learning for smarter and
more intelligent solutions (Sawhney et al., 2020).
 Machine learning is slowly being adopted by the construction industry, and there are
currently no solutions for delay mitigation based on machine learning.
PROBLEM STATEMENT

7. The aim of this research is to develop a framework for delay mitigation in tall
building projects.
Specifically:
1. To carry out a systematic literature review of studies on the causes of delay
reported from various construction industries globally.
2. To identify and rank the causes of delay in tall building projects according to the
various stakeholders across the project life cycle.
3. To develop models for estimating the cost, duration and delay-risk of tall building
projects based on machine-learning techniques.
4. To develop a framework for delay mitigation based on the results of (2) and (3).
RESEARCH OBJECTIVES

8. CONCEPTUAL FRAMEWORK
DELAY
MITIGATION
FRAMEWORK
Understanding
the causes of
delay could
minimize and
control delays
Risk assessment
approaches based
on identified
causes of delay
Improving cost
and time
performance
could mitigate
delays
Assaf and Al Hejji, 2006; Yang and
Ou, 2008; Kolb et al., 2017
Gunduz et al., 2015; Budayan et al.,
2018
Chan and Kumaraswamy, 1999; Aibinu
and Jagboro, 2002; Lessing et al., 2017

9. LITERATURE REVIEW

10.  A delay can be defined as a situation were a project’s completion time is postponed due to
causes that may be related to the client, consultant, contractor etc. (Aibinu and Jagboro
2002).
 Delays can also be defined as situations were an event occurs at a time later than
expected, or to be performed later than planned; or not to take timely actions; or
occurring beyond the agreed date specified in the contract (Pickavance, 2005; Lo et al.,
2006; Trauner, 2009).
 Types of Construction Delays
 Avoidable or Unavoidable Delays
 Critical or Noncritical
 Concurrent or Non-concurrent
CONSTRUCTION DELAYS

11.  There are numerous causes of construction delays. Aziz and Abdel-Hakam (2016)
identified two hundred ninety-three (293) causes of construction delays categorized
into fifteen (15) major groups.
 Early studies in the causes of construction delay was presented in the United States by
Baldwin and Manthei (1971), in Turkey (Arditi et al., 1985); in the UK (Sullivan and
Harris, 1986), in Nigeria (Okpala and Aniekwu, 1988; Dlakwa and Culpin, 1990; Mansfield
et al., 1994), in Thailand (Ogunlana et al., 1996), in Indonesia (Kaming et al., 1997), in
Hong Kong (Chan and Kumaraswamy, 1997; Kumaraswamy and Chan, 1998), in Lebanon
(Mezher and Tawil, 1998), in Saudi Arabia (Assaf et al., 1995; Al-Khalil and AlGhafly,
1999).
 Classification of delay studies
 Country-related delay studies
 Project type-related delay studies
IDENTIFYING THE CAUSES OF
CONSTRUCTION DELAYS

12. COUNTRY RELATED DELAY STUDIES
S/N Author(s)
Category (regional,
project etc.)
No of causes
identified
Subject focus (objective,
summary)
1
Aibinu and Odeyinka,
(2006)
Nigeria 44
Pareto analysis shows that 88%
of the factors were responsible
for 90% of the overall delays.
2
El-Razek et al.,
(2008)
Egypt 32
Financing by contractor during
construction is the most
important cause of delay.
3
Ezeldin and Abdel-
Ghany, (2013)
Egypt -
The main parties responsible for
delays were the contractor and
employer.
A non-exhaustive review of construction delay studies in Africa (9 studies)

13. COUNTRY RELATED DELAY STUDIES
A non-exhaustive review of construction delay studies in Western Asia (8 studies)
S/N Author(s)
Category (regional,
project etc.)
No of causes
identified
Subject focus (objective, summary)
1 Assaf et al., (1995) Saudi Arabia 56 -
2
Enshassi et al.,
(2009)
Gaza 110
Strikes and border closures most
common cause of delays.
3
Faridi and El-
Sayegh, (2006)
UAE 44
50% of project in UAE encounter
delay, common cause is approval of
drawings.

14. COUNTRY RELATED DELAY STUDIES
A non-exhaustive review of construction delay studies in Eastern Asia (7 studies)
A non-exhaustive review of construction delay studies in Southern Asia (9 studies)
A non-exhaustive review of construction delay studies in Europe, Oceania, South
and North America (5 studies)

15. PROJECT TYPE RELATED DELAY STUDIES
A non-exhaustive review of construction delay studies in Oil and Gas Projects
(3 studies)
S/N Author(s)
Category (regional,
project etc.)
No of causes
identified
Subject focus (objective, summary)
1
Seddeeq et al.,
(2019)
Oil and Gas/Saudi
Arabia
29
Change in design and scope by clients
during construction is top cause of
delays.
2 Fallahnejad (2013) Oil & Gas/Iran 43
Imported materials most significant
cause of delay.
3
Ruqaishi and Bashir
(2015)
Oil & Gas/Oman 44
Poor interaction with vendors is a
unique cause of delay in Oil and Gas
projects.

16. PROJECT TYPE RELATED DELAY STUDIES
A non-exhaustive review of construction delay studies in Road Construction
Projects (5 studies)
S/N Author(s)
Category (regional,
project etc.)
No of causes
identified
Subject focus (objective, summary)
1
Aziz and Abdel-
Hakam, (2016)
Road
projects/Egypt
293 -
2 Kaliba et al., (2009)
Road
projects/Zambia
NA
Delayed payments is the main cause
of delays.
3
Mahamid et al.,
(2012)
Road
projects/Palestine
52
The political situation was the most
significant cause of construction
delays.

17. PROJECT TYPE RELATED DELAY STUDIES
A non-exhaustive review of construction delay studies in High-Rise Building
Projects (7 studies)
S/N Author(s)
Category (regional,
project etc.)
No of causes
identified
Subject focus (objective, summary)
1
Ogunlana et al.,
(1996)
Thailand 40
Developing economies are prone to
delays related to: “problems of
shortages or inadequacies in
resources”.
2 Kaming et al., (1997) Indonesia 11
Main cause related to design
changes..
3 Suksai et al., (2015) Thailand 17
Delays due to poor collaboration
between main contractors and
nominated sub-contractors

18. PROJECT TYPE RELATED DELAY STUDIES
A non-exhaustive review of construction delay studies in Residential Projects (5
studies)
A non-exhaustive review of construction delay studies related to Contract Type (5
studies)
A non-exhaustive review of construction delay studies related to other Project
Types (3 studies)

19. INFLUENTIAL DELAY STUDIES
Most influential delay studies selected based on:
1. Publication in referred journals in the last 15 years
2. High number of citation of the study (Source: Google Scholar)
3. Study specific to the building industry
4. Only one study selected from one country
5. Availability of relevant data for meta-data analysis
S/N Author(s) Region
No of causes
identified
No of citations
(Jan. 2021)
1 El-Razek, (2008) Egypt 32 529
2 Sambasivan and Soon, (2007) Malaysia 28 1607
3 Aibinu and Odeyinka, (2006) Nigeria 44 424
4 Doloi et al., (2012) India 45 588
5 Faridi and El‐Sayegh, (2006) UAE 44 621
6 Fugar and Agyakwah-Baah, (2010) Ghana 32 409
7 Gunduz et al., (2013) Turkey 83 295
8 Lo et al., (2006) Hong Kong 30 358
9 Sweis et al., (2008) Jordan 40 568
10 Toor and Ogunlana, (2008) Thailand 75 304
11 Enshassi et al., (2009) Gaza 110 320

20. ISHIKAWA DIAGRAM
36 CAUSES OF DELAY SELECTED AND GROUPED IN 9 CATEGORIES

21. Existing Frameworks for Construction Delay
Mitigation
S/N Author(s) Subject focus
1 Love et al. (2000)
Proposed a systems dynamics model for delay mitigation due to prolonged
overtime work on project costs and quality.
2 Ng et al., (2000)
Investigated the application of Case Based Reasoning to develop a
conceptual framework for delay mitigation.
3 Abdul‐Rahman, (2008)
Proposed a delay mitigation model based on the adoption of knowledge
management.
4 AlSehaimi (2011) Proposed the Last Planner System as a viable solution for delay mitigation.
5 Motaleb (2014) Developed a risk response model and described preventative measures and
mitigation measures in developing the model.
6 Chai et al., (2015) Presented a structural equation model based on preventive measures,
predictive measures, organisational measures and corrective measures for
delay mitigation.
7 Khair et al., (2018) Proposed a project management framework for delay mitigation based on
the ‘stage–gate’ approach.

22.  Artificial Intelligence (AI) is the problem of “making a machine behave in ways that would
be called intelligent if a human were so behaving” (Kaplan and Haenlein, 2019).
 In achieving AI, ML techniques are deployed to train algorithms for decision-making;
hence, ML is a subset of AI.
 Some common ML techniques
MACHINE LEARNING
MLRA
Multi Linear Regression Analysis
K-NN
K-Nearest Neighbors
ANN
Artificial Neural Networks
SVM
Support Vector Machines
MCS (Hybrid/Ensemble)
Multi Classifier Systems

23. ML APPLICATION IN COSTRUCTION
COSTS/DURATION ESTIMATION
Knowledge Based Expert System (KBES)
S/N Author(s) Subject focus
1 Hendrickson et al. (1987a) MASON: duration of masonry construction projects
2 Hendrickson et al. (1987b)
CONSTRUCTION PLANEX: construction planning and scheduling including
estimation of durations and costs
3 Moselhi and Nicholas, (1990) ESCHEDULER: precedence setting and duration estimation
4 (Shaked and Warszawski, 1995) HISCHED: enumerating tasks, dependencies, resources and timing.

24. ML APPLICATION IN COSTRUCTION
COSTS/DURATION ESTIMATION
Multi Linear Regression Analysis (10 studies)
S/N Author(s) Subject focus
1 Khosrowshahi and Kaka (1996) Total cost and duration of housing projects in the UK
2 Chan and Kumaraswamy (1999) Duration models for various work packages in housing projects in Hong Kong
3 Bustani and Izam (1999) Duration of various types of building projects in Nigeria
4 Skitmore and Ng (2003) Model for cost and time of Australian construction projects
5 Blyth et al. (2004) Project and activity duration model for buildings in the UK

25. ML APPLICATION IN COSTRUCTION
COSTS/DURATION ESTIMATION
Neural Networks
S/N Author(s) Subject focus
1 Mensah et al. (2016) Duration of bridge construction projects in Ghana based on BOQ items
2 Attal (2010) Cost and duration prediction of highway construction projects in Virginia
3 Lekan, (2011) Cost prediction model for building projects in Nigeria

26. ML APPLICATION IN COSTRUCTION
COSTS/DURATION ESTIMATION
Case Based Reasoning (CBR)
S/N Author(s) Subject focus
1 Li et al. (2017) Project duration for skyscrapers in China
2 Jin et al. (2016) Duration of multifamily housing projects in Korea

27. ML APPLICATION IN COSTRUCTION
COSTS/DURATION ESTIMATION
Hybrid methods/Other techniques
S/N Author(s) Subject focus
1 Koo et al. (2010)
CBR hybrid model for cost and construction duration applied to multifamily housing
projects
2 (Peško et al., 2017) Cost and duration estimation using ANN and SVM for urban roads
3 (Kim, An and Kang, 2004) Construction costs based on MRA, ANN and CBR

28. RESEARCH METHOD

29. METHODOLOGY
Identify
areas for
potential ML
application

30. METHODOLOGY
Objective
1
Objective
2
Objective
3

31. METHODOLOGY
Objective
4

32. MODEL DEVELOPMENT PROCESS/ 1
Objective
3

33. MODEL DEVELOPMENT PROCESS/ 2
Objective
3

34. Architecture of Multi Classifier System
Objective
3

35. RESULTS AND DISCUSSIONS

36. RESULTS AND DISCUSSIONS
Systematic
Literature
Review
Causes of Delay
in Tall Buildings
ML Models for
Cost, Duration and
Delay Risk
Delay
Framework
Objective
1
Objective
2
Objective
3
Objective
4

37. SYSTEMATIC LITERATURE REVIEW

38. IDENTIFIED CAUSES OF DELAY
Objective
1

39. Objective
1
IDENTIFIED CAUSES OF DELAY

40. Objective
1
RII VALUES EXTRACTED FROM
SELECTED STUDIES

41. RANKING FROM META DATA ANALYSIS
Objective
1

42. CAUSES OF DELAY IN TALL BUILDINGS

43. SURVEY RESPONDENTS
Objective
2

44. SURVEY RESULTS
Objective
2

45. SURVEY RESULTS
Objective
2

46. CAUSES OF DELAY IN TALL BUILDINGS
FROM VARIOUS COUNTRIES
Objective
2

47. ML Models for Cost, Duration and Delay Risk

48. DESCRIPTIVE STATISTICS OF DELAY
RISK FACTORS
Objective
3

49. DESCRIPTIVE STATISTICS OF DELAY
RISK FACTORS
Objective
3

50. DESCRIPTIVE STATISTICS OF COST AND
DURATION DATASET
Objective
3

51. DESCRIPTION OF PROPOSED ML
MODELS
Objective
3

52. DESCRIPTION OF PROPOSED ML
MODELS
Objective
3

53. DESCRIPTION OF PROPOSED ML
MODELS
Objective
3

54. PERFORMANCE OF PROPOSED ML
MODELS - COST
R2: 81%
RMSE: 6.09
MAPE: 80.95%
Best
Model
Objective
3

55. PERFORMANCE OF PROPOSED ML
MODELS - DURATION
R2: 69%
RMSE: 301.72
MAPE: 18%
Best
Model
Objective
3

56. PERFORMANCE OF PROPOSED ML
MODELS – DELAY RISK
Best
Model
Confusion matrix of
best performing model
Misclassified
instance
Objective
3

57. Proposed Delay Mitigation Framework

58. Proposed Delay Mitigation Framework based on CRISP-DM Objective
4

59. CONSULTED PROFESSIONALS FOR
FRAMEWORK VALIDATION
Objective
4

60. VALIDATION RESULTS
Objective
4

61. CONCLUSIONS OF THE STUDY

62.  The following specific conclusions could be made from the study:
 36 common delay causes of delay have been investigated globally.
 The top causes of construction delay, globally, include: “contractor’s financial
difficulties”, “delay in approval of completed work”, “slow delivery of
materials”, “poor site organization and coordination between various parties”,
and “poor planning of resources and duration estimation/scheduling”.
 Tall buildings are risky projects subject to increasing delay occurrences.
 The top three causes of delay in tall buildings include “client’s cash flow
problems/delays in contractor’s payment”, “contractor’s financial difficulties”,
and “poor site organization and coordination between various parties”.
 Delay causes such as “civil disturbances/hostile political conditions” and “labor
disputes and strikes” were perceived as relatively insignificant in the GCC
countries.
 “Unexpected foundation conditions encountered in the field” was considered as
an unlikely cause of delay in tall building projects, as soil and foundation
investigations are considered critical aspects of tall building projects.
SUMMARY OF CONCLUSIONS

63.  The following specific conclusions could be made from the study:
 Machine Learning is now receiving attention in construction due to Construction 4.0
 Out of twelve models developed to predict the duration of tall building projects,
the best model was based on an ensemble method using ANN as the combiner, with
a Correlation Coefficient (R2) of 0.69, Root Mean Squared Error (RMSE) of 301.72
and Mean Absolute Percentage Error (MAPE) of 18%.
 Out of twelve models developed to predict the cost of tall building projects, the
best performing model was based on a multi classifier system using KNN as the
combining classifier, with (R2) of 0.81, (RMSE) of 6.09 and (MAPE) of 80.95%.
 The best model for predicting the risk of delay was based on ANN with a
classification accuracy of 93.75%.
 Delay Mitigation Framework based on CRISP-DM proposed for tall building
projects.
 The proposed framework was viewed by industry experts as a welcome development
in the construction community. It was noted that the value of the framework is
dependent on the reliability of the data used for machine learning applications.
SUMMARY OF CONCLUSIONS

64.  Researched tall building projects, which have become a dominant building typology
of the modern urban habitat; and is rapidly becoming, an important area of
construction engineering and management research.
 First study to present a quantitative and systematic identification of the causes of
construction delay in the global construction industry through meta-analysis.
 Presented a thorough analysis of the causes of delay in tall building projects in the
GCC countries (Saudi Arabia, UAE, Kuwait, Oman, Bahrain and Qatar).
 Demonstrated the adoption of machine learning in predicting the preliminary cost,
duration and delay risk assessment of tall building projects. The idea can be
extended to other construction domains.
 First study to propose a prescriptive tool for construction delay mitigation based on
machine learning.
 Demonstrated the value of adopting emerging computing technologies in solving age-
long construction problems. Thus promoting the Construction 4.0 agenda.
CONTRIBUTIONS OF THE STUDY

65. CURRENT ACHIEVEMENTS

66. PROF. IR. DR. ROSLI MOHAMAD ZIN
ASSOC. PROF. IR. DR. SUNDAY
OLUSANYA OLATUNJI
WIFE: GHANIYYAH FATOYINBO;
CHILDREN: JUWAYRIYAH, RAMLAH &
SAWDAH; MOTHER: ENGR. MUSLIMAT
SANNI-ANIBIRE.
+ OTHER FAMILY AND FRIENDS
APPRECIATION