Application of Artificial Intelligence in Construction Scheduling
ABSTRACT
In this study, Artificial Neural Network and Neuro – Fuzzy models were
developed using data extracted from a residential two – storey reinforced
concrete framed structure construction schedule and project execution
documents. The evaluation of project performance indicators in earned value
analysis from 0 – 100% progress at 5% increment with a total of seventeen tasks
were carried out using Microsoft Project software and data obtained from the
computation were utilized for model development. Pearson Correlation results
obtained for the model variables indicated stronger positive relationship
between the response factors Earned Value (EV) and Performance Indicators
namely; Planned Progress, Actual Time (AT), Earned Schedule (ES), Actual Cost
(AC) and Cost Variance (CV) while negative linear relationships were observed
to exist for the Schedule Performance Indicator (SPI) and Schedule Variance
(SV) factors. Using input – output and curve fitting (nftool) function in
MATLAB, a 6 – 10 – 1 two – layer feed – forwards network with Tansig Activation
Function (AF) for the hidden neurons and linear Activation Function (AF) output
neurons was generated with Levenberg – Marquardt (Trainlm) training algorithm.
Similarly, with the aid of ANFIS toolbox in MATLAB software, the training,
testing and validation of the ANFIS model were carried out using hybrid
optimization learning algorithm at 100 epochs and Gaussian Membership Function
(gaussmf). Loss function and statistical parameters; Mean Absolute Error (MAE),
Root Mean Square Error (RMSE) and R-values were taken as the performance
evaluation criteria of the developed smart intelligent models. The generated
statistical results indicate no significant difference between model results
and experimental values with MAE, RMSE, R2 of 1.9815, 2.256 and
99.9% respectively for ANFIS model and MAE, RMSE, R2 of 2.146,
2.4095 and 99.998% respectively for the ANN model. The model performance shows
adaptive and robust behavior to deal with complex relationships between the
model variables to produce accurate target response.
TABLE OF CONTENTS
Title Page i
Declaration ii
Certification iii
Dedication iv
Acknowledgement v
Table of Contents vi
List of Tables ix
List of Figures x
Abstract xi
CHAPTER 1:
INTRODUCTION
1.1 Statement of Problem 4
1.2 Aim and Objectives of
Study 5
1.3 Significance of Study 5
1.4 Scope of Study 6
CHAPTER 2:
LITERATURE REVIEW
2.1 Construction Schedule 8
2.1.1 Importance of scheduling
in construction projects 10
2.2 Project Scheduling
Process 11
2.2.1 Plan schedule management 12
2.2.2 Define the project –
activities 12
2.2.3 Determine dependencies 13
2.2.4 Sequence activities 13
2.2.5 Estimate resources 13
2.2.6 Estimate durations 14
2.2.7 Develop the project –
schedule 14
2.2.8 Monitor and control 15
2.3 Project Scheduling tools and Techniques 15
2.3.1 Tool lists 15
2.3.2 Calendar 15
2.3.3 Gantt charts 16
2.3.4
Guidelines for generating a better project – schedule 16
2.4 The
Critical – Path Method 16
2.4.1
Mathematical formulation of CPM scheduling
18
2.4.2
Critical path – scheduling algorithms 18
2.5
Other Scheduling Methods 21
2.5.1 Line – of – balance (LOB) scheduling
technique 21
2.5.2 Resource – oriented scheduling 22
2.5.3 Q Scheduling 22
2.6 Earned Value Management (EVM) 22
2.6.1 Calculating earned value 24
2.6.2 The EVM indicators 24
2.7 Fundamentals of Earned Value Management 26
2.7.1 Organization and scope of project 26
2.7.2 Planning, scheduling and budgeting 27
2.7.3 Accounting for actual costs 28
2.7.4 Analyzing and reporting on project performance 28
2.7.5 Revisions and data maintenance 28
2.8 Artificial Intelligence Application in
Construction Management 31
2.8.1 Artificial neutral network and fuzzy –
inference – systems 32
CHAPTER 3: METHODOLOGY
3.1 Experimental Design 36
3.2 Steps to Critical Path Calculation 37
3.2.1 Forward scroll algorithm 37
3.2.2 Backward scroll algorithm 37
3.3 Model
Performance Evaluation 41
CHAPTER 4: RESULTS AND DISCUSSION
4.1 Data sets for Model Development 46
4.1.1 Pearson correlation 49
4.2 Artificial Neural Network (ANN) Model
Development 51
4.2.1 Training state of the ANN 54
4.2.2 Validation performance of the ANN 56
4.2.3 Error histogram of the ANN 58
4.2.4 Regression plot of the ANN 60
2.4.5 Selection of optimized ANN model 62
4.3 Neuro – Fuzzy Model Development 64
4.3.1 Testing and Training ANFIS 67
4.3.2 Graphical plots of the membership function 69
4.3.3 Selection of optimized ANFIS model 72
4.3.4 ANFIS – model variables graphical expression 74
4.4 Model Validation 76
4.5 Sensitivity Analysis 81
CHAPTER 5: SUMMARY, CONCLUSION AND
RECOMMENDATION
5.1 Conclusion 83
5.2 Recommendation 84
REFERENCES 85
APPENDICES 91
LIST OF TABLES
4.1: Start
and End Times of the Activities 43
4.2: Performance
Indicators Computation Results 44
4.3: Pearson’s Correlations for Model
Parameters 50
4.4: Artificial
Neural Network Processing Parameter Settings 52
4.5: ANN Architectures’ comparison to derive an
optimized model during training
and testing 63
4.6: ANFIS Network Parameter 65
4.7: ANFIS
Architectures’ comparison to derive the optimized model during training
and
testing 73
4.8: Actual
and Model predicted results 77
4.9: Performance Evaluation of ANFIS Model 78
LIST
OF FIGURES
2.1: Project Scheduling Process 12
2.2: Earned Value Analysis 30
3.1: Precedence Relations and Durations for a Nine Activity
Project 39
3.2: Gantt
chart 40
4.1: Interpretation of Value
for Indicators of Project Performance 45
4.2 a and b: Distribution histogram chart for
input variables (AT and ES) 46
4.2 c and d: Distribution histogram chart for
input variables (SV and Planned Progress) 47
4.2 e and f: Distribution histogram chart for
input variables (SPI and Planned AC) 47
4.2 g and h: Distribution histogram chart for
input and variables (CPI and CV) 48
4.2 i: Distribution histogram chart for output
variables (EV) 48
4.3: ANN Architecture 53
4.4: ANN
Training State 55
4.5: Validation
performance of the ANN 57
4.6: ANN Error Histogram 59
4.7: ANN Training, Testing and Validation
Regression Plot 61
4.8: ANFIS Model Variables and Architecture 66
4.9: ANFIS
Model Training and Error Plot 68
4.10: Plot
of Testing Datasets 68
4.11 ANFIS
Membership Function Plots 70
4.12: 3D-Surface
Plots of ANFIS – Model Variables 75
4.13: Goodness
of Fit Plot for ANFIS model 79
4.14: Goodness
of Fit Plot for ANN model 80
4.15: ANN
model Sensitivity analysis results 82
4.16: ANFIS model Sensitivity analysis results 82
0.0
RICHARD, C (2023). Application of Artificial Intelligence in Construction Scheduling . Mouau.afribary.org: Retrieved Nov 23, 2024, from https://repository.mouau.edu.ng/work/view/application-of-artificial-intelligence-in-construction-scheduling-7-2
CHINENYE, RICHARD. "Application of Artificial Intelligence in Construction Scheduling " Mouau.afribary.org. Mouau.afribary.org, 26 Jul. 2023, https://repository.mouau.edu.ng/work/view/application-of-artificial-intelligence-in-construction-scheduling-7-2. Accessed 23 Nov. 2024.
CHINENYE, RICHARD. "Application of Artificial Intelligence in Construction Scheduling ". Mouau.afribary.org, Mouau.afribary.org, 26 Jul. 2023. Web. 23 Nov. 2024. < https://repository.mouau.edu.ng/work/view/application-of-artificial-intelligence-in-construction-scheduling-7-2 >.
CHINENYE, RICHARD. "Application of Artificial Intelligence in Construction Scheduling " Mouau.afribary.org (2023). Accessed 23 Nov. 2024. https://repository.mouau.edu.ng/work/view/application-of-artificial-intelligence-in-construction-scheduling-7-2