The S-curve for forecasting construction waste generation using big data

Speaker: Crystal, Xi CHEN Dept. of Real Estate and Construction, Faculty of Architecture, The University of Hong Kong Email: chenx90@hku.hk

Authors: Xi Chen, Weisheng Lu, Yi Peng

Year 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 C&D waste at landfills (tpd) 2 10202 6728 6595 6556 4125 3158 3092 3121 3584 3331 3440 Proportion of C&D waste (%) 48 38 38 37 27 23 25 25 26 23 25

Domestic waste at landfills (tpd) 2 7519 7402 7014 6828 6634 6372 6081 6015 6135 5973 6286

Commercial waste at landfills (tpd) 2 1342 1428 1673 1895 2062 2190 2280 2319 2352 2360 2260

Industrial waste at landfills (tpd) 2 561 612 601 654 583 622 660 629 627 663 732

Special waste at landfills (tpd) 2 1534 1588 1620 1746 1635 1559 443 340 1119 1131 1127

Solid waste disposed of at landfills from 1991 to 2010 (Data source: HKEPD, Monitoring of Solid Waste in Hong Kong in various years)

Comparing C&D waste disposed in landfill with other sectors (year 2002-2012)

Construction waste reduction is a grave concern. Forecasting construction waste generation as project progresses is the core to preparing the waste management plan (WMP). Therefore, it becomes a key research question in construction waste management (CWM).

Introduction

•A model, by feeding in project characteristics, which can forecast the waste generation as the project progresses, is highly desired.

•Project Management Institute (PMI) defined an S-curve as a graphic display of cumulative costs, labour hours, percentage of work, or other quantities plotted against time.

•Intuitively, accumulative waste generated from the project should also feature an S-curve.

Material and Methods

•Step 1: Data Collection: Collecting historical data of time,

relevant amount of construction waste, and project

characteristics

•Step 2: Model selection: Identifying the best form of S-

curve formulas to model construction waste generation

•Step 3: Link Establishment: Establishing the link between

project characteristics and parameter values of CWG S-

curve through ANN

There were more than 4,000,000 waste transaction records in 2011, 2012, 2013 and 2014

The CWDCS Starting from 1 December 2005, main contractor who undertakes construction work under a contract with value of HK$1 million or above is required to open a billing account for the contract.

Step 1: Data collection

To probe into construction waste management on the ground!

standardized time & standardized cumulative waste amount

Step 2: Model selection Summary of estimating S-curve models

Programs can be designed in Matlab [7] to conduct curve fitting so as to select the best-fit S-curve formulas from the options as listed in the left table. A method called the least-squares curve fitting analysis (LSCFA) was used to evaluate the fitness of an S-curve formula that describes the data trend of a specific project [8]. Model 5 (M5) is selected according to the results of LSCFA.

where x is the standardized time, and y is the standardized construction waste amount.

• ANN was used to link the project characteristics and the parameter values, i.e. a and b in M5, the above equation.

• Matlab© was used to conduct the ANN model development [7]. Each network’s performance was then evaluated by average MSE.

Step 3: Link Establishment

Initial GA coding Determine the BPstructure

Initialize the weighting andthreshold of BP

Data input

Datastandardization

Using the trained errorof BP as fitness index

Selection operation

Cross operation

Mutation operation

Mutation operation

Calculating the fitnessindex

Reach maxnumber ofevolution?

Obtaining best weightingand threshold of BP

Calculating the trainederror of BP

Updating the weighting andthreshold of BP

Reach thepreset

objectives?

Forecasting withdetermined BP

All combinatonsof input

variables?

Calculating MSE offorecasted CW S-curve

Selecting the combinationof input variables

generating lowest MSE

No

Yes

No

Yes

Yes

No

Statistical summary of MSE of the ANN model (%)

contract sum, location and public-private nature

Total amount of CWG

Where TCWG is the total amount of CWG, contractsum is the contract sum of the project, pripub is whether the project is public (denoted as 1) or private (denoted as 0).

• With the historical data, regression can be used to identify the relationship between project characteristics and total amount of CWG.

• With historical data of projects in training sample, stepwise regression was used to find the best model explain the relationship, the results of which is shown in the following Equation.

With the abovementioned models, the daily accumulative flow of CWG of a new project can therefore be forecasted.

The uses of the CWG s-curve model

• For project contractors/managers, the model can predict a baseline S-curve to be used for estimating the waste management requirements during construction progress;

• At project planning stage, the CWG S-curve developed based on the model can not only provide consultants with the predicted total waste generation amount, but also predict construction progress through estimating waste generation progress;

• The model may provide decision makers with a baseline to benchmark waste generation amount of future construction works; and

• Stakeholders in CWM as a whole may utilize this model as a reference to develop a standard and handy tool to be accepted among construction industry for predicting CWG progress.

Conclusions

This study presents a methodology including a standard S-curve represented by a cubic polynomial function and ANN model.

By inputting contract sum, location and public-private nature into the model, a CWG of a project can be forecasted and presented as an S-curve.

The methodology is a potential useful tool allowing waste management stakeholders (e.g. consultants, contractors and government) to forecast waste generation using project characteristics.

Acknowledgement

The research was jointly supported by the National Nature Science

Foundation of China (NSFC) (project no.: 71273219), and the Innovation

and Technology Fund (Project No.: ITP/045/13LP) of the Hong Kong

Innovation and Technology Commission (ITC).