000			02942nam a2200181Ia 4500
008			240315s9999 xx 000 0 und d
082			_aCEM TH-0432 _bAMB
100			_aAmbasana, Hetvi (PCM21135) _994244
245		0	_aMachine learning framework for analyzing last planner system (NEC) (Softcopy is also available)
260			_c2023
300			_axviii,73p.
505			_aTABLE 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
700			_aDevkar, Ganesh (Guide)
890			_aIndia
891			_a2021 Batch
891			_aConstruction engineering and Management
891			_aFT-PG
999			_c71792 _d71792