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