Machine learning framework for analyzing last planner system (NEC) (Softcopy is also available) (Record no. 71792)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02942nam a2200181Ia 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240315s9999 xx 000 0 und d |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | CEM TH-0432 |
| Item number | AMB |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Ambasana, Hetvi (PCM21135) |
| 245 #0 - TITLE STATEMENT | |
| Title | Machine learning framework for analyzing last planner system (NEC) (Softcopy is also available) |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Date of publication, distribution, etc | 2023 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xviii,73p. |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | TABLE OF CONTENTS <br/>ABSTRACT i <br/>UNDERTAKING iii <br/>CERTIFICATE v <br/>ACKNOWLEDGEMENTS vii <br/>ABBREVIATIONS ix <br/>TABLE OF CONTENTS xi <br/>LIST OF FIGURES xv <br/>LIST OF TABLES xvii <br/>CHAPTER-1: INTRODUCTION 1 <br/>1.1 Construction Automation in Indian Construction Industry 1 <br/>1.2 Current Scenario of Construction Planning 2 <br/>1.3 Need of the Study 2 <br/>1.4 Research Objective 3 <br/>1.5 Scope of the Study 4 <br/>1.6 Research Methodology 4 <br/>1.7 Research Timeline 5 <br/>CHAPTER-2: LITERATURE REVIEW 7 <br/>2.1 Lean Management 7 <br/>2.2 Lean Construction 8 <br/>2.3 Machine Learning 8 <br/>2.4 Last Planner System 10 <br/>2.5 Application of Machine Learning in Construction Industry 13 <br/>2.5.1 Prior studies on application of Machine Learning in Construction 14 <br/>2.6 Machine Learning Models 15 <br/>2.6.1 Multiple Linear Regression 15 <br/>2.6.2 Support Vector Machines (SVM) 16 <br/>2.6.3 Artificial Neutral Network (ANN) 16 <br/>2.6.4 K-nearest neighbors (KNN) 17 <br/>2.6.5 Naïve Bayesian classification algorithms 17 <br/>2.6.6 Logistics Regression 17 <br/>2.6.7 Decisions Tree 18 <br/>2.6.8 Random Forest 18 <br/>2.7 Machine Learning and Last Planner System 18 <br/>2.8 Research Gap 19 <br/>CHAPTER-3: RESEARCH METHODOLODY 21 <br/>3.1 Literature Review 21 <br/>3.2 Current Industrial Practices 21 <br/>3.3 Raw Data Collection 22 <br/>3.4 Data Acquisition Model 22 <br/>3.5 Data pre-processing and cleaning 23 <br/>3.6 Label Encoding 23 <br/>3.7 Developing Machine Learning Model 23 <br/>3.8 Evaluate and refine the model 23 <br/>CHAPTER-4: DATA COLLECTION 25 <br/>4.1 Look Ahead Plans 25 <br/>4.1.1 Super-Specialty Healthcare Project 25 <br/>4.1.2 Medical Education and Research Institute 26 <br/>4.2 Constraints Log 27 <br/>4.3 Summary 27 <br/>CHAPTER-5: DATA ANALYSIS 29 <br/>5.1 Data Cleaning and pre-processing 29 <br/>5.1.1 Data Cleaning and Pre-processing manually 29 <br/>5.1.2 Data Cleaning and Pre-processing by using python code 30 <br/>5.2 Constraint Grouping Library 31 <br/>5.3 Model Training 33 <br/>5.3.1 Model Preparation 35 <br/>5.3.2 Machine Learning Model 36 <br/>5.3.3 Result 37 <br/>5.3.4 Hyperparameter Tuning 37 <br/>5.4 Testing the Model 38 <br/>5.4.1 Result 39 <br/>5.5 Model Evaluation 39 <br/>CHAPTER-6: CONCLUSION 43 <br/>6.1 Summary 44 <br/>6.2 Practical Implications 45 <br/>6.3 Theoretical Implications 45 <br/>6.4 Limitations of the study 46 <br/>6.5 Future scope 46 <br/>REFERENCES 49 <br/>APPENDIX 55<br/> |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Devkar, Ganesh (Guide) |
| 890 ## - Country | |
| Country | India |
| 891 ## - Topic | |
| Topic | 2021 Batch |
| 891 ## - Topic | |
| Topic | Construction engineering and Management |
| 891 ## - Topic | |
| Topic | FT-PG |
| Withdrawn status | Lost status | Damaged status | Not for loan | Collection code | Withdrawn status | Home library | Current library | Date acquired | Source of acquisition | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Faculty of Technology | CEPT Library | CEPT Library | 15/03/2024 | Faculty of Technology | CEM TH-0432 AMB | 025782 | 15/03/2024 | 15/03/2024 | Thesis |