TY  - BOOK
AU  - Ambasana, Hetvi (PCM21135)
AU  - Devkar, Ganesh (Guide)
TI  - Machine learning framework for analyzing last planner system (NEC) (Softcopy is also available)
U1  - CEM TH-0432 
PY  - 2023///
N1  - TABLE OF CONTENTS 
ABSTRACT   i 
UNDERTAKING   iii 
CERTIFICATE   v 
ACKNOWLEDGEMENTS   vii 
ABBREVIATIONS   ix 
TABLE OF CONTENTS   xi 
LIST OF FIGURES   xv 
LIST OF TABLES   xvii 
CHAPTER-1: INTRODUCTION   1 
1.1 Construction Automation in Indian Construction Industry   1 
1.2 Current Scenario of Construction Planning   2 
1.3 Need of the Study   2 
1.4 Research Objective   3 
1.5 Scope of the Study  4 
1.6 Research Methodology   4 
1.7 Research Timeline   5 
CHAPTER-2: LITERATURE REVIEW   7 
2.1 Lean Management   7 
2.2 Lean Construction   8 
2.3 Machine Learning   8 
2.4 Last Planner System   10 
2.5 Application of Machine Learning in Construction Industry   13 
2.5.1 Prior studies on application of Machine Learning in Construction   14 
2.6 Machine Learning Models   15 
2.6.1 Multiple Linear Regression  15 
2.6.2 Support Vector Machines (SVM)   16 
2.6.3 Artificial Neutral Network (ANN)   16 
2.6.4 K-nearest neighbors (KNN)     17 
2.6.5 Naïve Bayesian classification algorithms   17 
2.6.6 Logistics Regression   17 
2.6.7 Decisions Tree   18 
2.6.8 Random Forest   18 
2.7 Machine Learning and Last Planner System   18 
2.8 Research Gap  19 
CHAPTER-3: RESEARCH METHODOLODY   21 
3.1 Literature Review   21 
3.2 Current Industrial Practices   21 
3.3 Raw Data Collection   22 
3.4 Data Acquisition Model   22 
3.5 Data pre-processing and cleaning   23 
3.6 Label Encoding   23 
3.7 Developing Machine Learning Model   23 
3.8 Evaluate and refine the model   23 
CHAPTER-4: DATA COLLECTION   25 
4.1 Look Ahead Plans   25 
4.1.1 Super-Specialty Healthcare Project   25 
4.1.2 Medical Education and Research Institute   26 
4.2 Constraints Log   27 
4.3 Summary   27 
CHAPTER-5: DATA ANALYSIS   29 
5.1 Data Cleaning and pre-processing  29 
5.1.1 Data Cleaning and Pre-processing manually   29 
5.1.2 Data Cleaning and Pre-processing by using python code   30 
5.2 Constraint Grouping Library   31 
5.3 Model Training   33 
5.3.1 Model Preparation   35 
5.3.2 Machine Learning Model   36 
5.3.3 Result   37 
5.3.4 Hyperparameter Tuning   37 
5.4 Testing the Model  38 
5.4.1 Result   39 
5.5 Model Evaluation   39 
CHAPTER-6: CONCLUSION   43 
6.1 Summary   44 
6.2 Practical Implications   45 
6.3 Theoretical Implications   45 
6.4 Limitations of the study  46 
6.5 Future scope   46 
REFERENCES   49 
APPENDIX   55

ER  -