Difference between revisions of "ANLY482 AY2017-18T2 Group02 Project Overview"

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<p>Arup Singapore Pte Ltd is an engineering consultancy firm. Starting from 2008, Arup was involved in the alignment planning and design of the Downtown Line phase 3. Prior to construction, Arup has put in extensive efforts to design the train stations and tunnelling, accounting for geological and surrounding building data.
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==<div style="background: #800000; line-height: 0.5em; font-family:'Helvetica';  border-left: #FFB6C1 solid 15px;"><div style="border-left: #F2F1EF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF">PROJECT BACKGROUND</font></div></div>==
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<p>DonorsChoose.org is a non-profit organisation seeking to improve the education system in America. The DonorsChoose.org platform is a civic crowdfunding platform that allows public school teachers across America to reach out to potential donors. Donors can choose the type of projects to fund and can donate any amount to the cause of their liking. Since its founding in 2000, the platform has funded over a million projects and benefited over 27 million students.
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Arup's design philosophy involves design across multiple phases. At the end of each phase, Arup will consult with contractors to determine differences in recorded data and readjust their design accordingly. However, many such projects are on a case-by-case basis and learning from each experience is not documented.
 
 
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With the recent opening of the Downtown line to public, it is timely to review the as-built plans and explain the differences from the prediction stage. This will help Arup close the loop from the planning phase to the end result. Findings from the project will be valuable for Arup when engaging on similar engineering projects in the future, including the upcoming Thomson-East Coast Line.
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The process starts with teachers submitting their project proposals, detailing the resources and materials they require and how the resources will benefit their students. Upon submission to DonorsChoose.org, volunteers will review the project submission and determine whether it can be approved.As the number of project submissions is expected to increase beyond 500,000 in 2018, DonorsChoose.org has to scale their efforts in the project approval process as well. A prediction model will help facilitate the process, but close attention has to be paid to the model such that it can selectively discern deserving projects.
 
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==<div style="background: #800000; line-height: 0.5em; font-family:'Helvetica';  border-left: #FFB6C1 solid 15px;"><div style="border-left: #F2F1EF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF">OBJECTIVES</font></div></div>==
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<b><span style="color:#800000">Key Objectives</span></b>
 
<b><span style="color:#800000">Key Objectives</span></b>
  
 
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There are 2 key objectives:
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The objective our project would be to develop a model for DonorsChoose.org to predict the likely approval status of projects submitted by teachers. Based on past data on the project, the teacher and the school, we would seek to build a model that could determine the projects’ approval rate. Ideally, the model would have high precision and recall rate.
 
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1. Identifying design features that have high probability for re-design in rail design
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The expected outcome would be for DonorsChoose.org to automate part of its project screening process, and redirect efforts into examining proposals that need more assistance. The project will also shed insight into how organisations can utilize prediction models to scale manual processes in an efficient manner while preserving accuracy.
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2. Building a predictive model to assist engineers in rail design planning and to serve as an error catching tool
 
 
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<b><span style="color:#800000">Predictive Analytics</span></b>
 
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In order for data modelling to attempt to reach the heights of acting as a true supplement or even replacement for engineering design, it’s necessary to achieve predictive accuracy sufficient to meet the stringent standards of safety. The model must be nuanced enough to predict design parameters for a wide range of components meant to function under diverse geological conditions, while having adequate levels of buffer to observe safety standards.
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==<div style="background: #800000; line-height: 0.5em; font-family:'Helvetica';  border-left: #FFB6C1 solid 15px;"><div style="border-left: #F2F1EF solid 5px; padding:15px;font-size:15px;"><font color= "#F2F1EF">STAKEHOLDERS</font></div></div>==
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<b><span style="color:#800000">Expected Outcomes</span></b>
 
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We aim to survey several possible predictive approaches, and identify models with high probability of success at modelling for rail engineering design. In order to achieve this while keeping expectations reasonable, we target a level of prediction that is statistically significant, i.e. distinguishable from random guesses.
 
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<b><span style="color:#800000">Planned Deliverables</span></b>
 
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Given the limited data size, our expected outcomes are unlikely to be operationalized in actual engineering design. However, we plan to commit to laying a good foundation for further work in predictive analysis for rail tunnel engineering.
 
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We will submit a technical paper detailing our methodology, and the results, accompanied by any code written in the process.
 
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<p><b><span style="color:#800000">Students:</span></b> [https://wiki.smu.edu.sg/ANLY482/Ang_Zhuang_Kai_Friedemann Friedemann] & [https://wiki.smu.edu.sg/ANLY482/Josh_Ho_Xian_Zheng Josh]</p>
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[[File:KD_Stakeholders.png|700px|center]]
 
  
 
<p><b><span style="color:#800000">Supervisor:</span></b> [https://sis.smu.edu.sg/faculty/profile/9618 Prof. Kam Tin Seong]</p>
 
<p><b><span style="color:#800000">Supervisor:</span></b> [https://sis.smu.edu.sg/faculty/profile/9618 Prof. Kam Tin Seong]</p>
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<p><b><span style="color:#800000">Sponsor:</span></b> Arup Singapore Pte. Ltd - [https://www.linkedin.com/in/chris-deakin-50370613/ Chris Deakin]: Rail Leader, Digital Engineering Leader</p>
 
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<p><b><span style="color:#800000">Students:</span></b> [https://wiki.smu.edu.sg/ANLY482/Ang_Zhuang_Kai_Friedemann Friedemann] & [https://wiki.smu.edu.sg/ANLY482/Josh_Ho_Xian_Zheng Josh]
 
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Latest revision as of 09:00, 15 April 2018

Home   Project Overview   Findings & Insights   Documentation   Project Management   Back to project list



PROJECT BACKGROUND

DonorsChoose.org is a non-profit organisation seeking to improve the education system in America. The DonorsChoose.org platform is a civic crowdfunding platform that allows public school teachers across America to reach out to potential donors. Donors can choose the type of projects to fund and can donate any amount to the cause of their liking. Since its founding in 2000, the platform has funded over a million projects and benefited over 27 million students.

The process starts with teachers submitting their project proposals, detailing the resources and materials they require and how the resources will benefit their students. Upon submission to DonorsChoose.org, volunteers will review the project submission and determine whether it can be approved.As the number of project submissions is expected to increase beyond 500,000 in 2018, DonorsChoose.org has to scale their efforts in the project approval process as well. A prediction model will help facilitate the process, but close attention has to be paid to the model such that it can selectively discern deserving projects.

OBJECTIVES

Key Objectives

The objective our project would be to develop a model for DonorsChoose.org to predict the likely approval status of projects submitted by teachers. Based on past data on the project, the teacher and the school, we would seek to build a model that could determine the projects’ approval rate. Ideally, the model would have high precision and recall rate.

The expected outcome would be for DonorsChoose.org to automate part of its project screening process, and redirect efforts into examining proposals that need more assistance. The project will also shed insight into how organisations can utilize prediction models to scale manual processes in an efficient manner while preserving accuracy.


STAKEHOLDERS

Students: Friedemann & Josh

Supervisor: Prof. Kam Tin Seong

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