Quantitative similarity assessment of construction projects using WBS-based metrics

doi:10.1016/j.aei.2020.101179

Advanced Engineering Informatics

Volume 46, October 2020, 101179

https://doi.org/10.1016/j.aei.2020.101179 Get rights and content

Abstract

Lessons learned from completed projects are valuable resources for planning of new projects. A quantitative similarity measurement between construction projects can improve knowledge reuse practices. The information and documents of a similar past project can be retrieved to resolve the challenges in a new project. This paper introduces a novel method for measuring the similarity of construction projects based on semantic comparison of their work breakdown structure (WBS). WBS of a project should theoretically encompass a hierarchical decomposition of the total scope of project’s works, thus it could be used as an appropriate representative of the projects. The proposed method measures the semantic similarity between WBS of projects by means of natural language processing techniques. This method was implemented based on three metrics: node, structural, and total similarity. Each of these metrics calculate a quantitative similarity score between 0 and 1. The method was assessed using fifteen test samples with promising results in compliance with similarity properties. In addition, precision and recall of the method were evaluated in retrieving similar past projects. The results illustrate that the structural similarity slightly outperforms the other metrics.

Introduction

Project managers typically consider knowledge gained from previous projects in their decision makings [43], [11]. Effective reuse of the gained knowledge not only reduces the time and cost of solving problems, but also improves the quality of solutions [45]. In construction, various studies have investigated different methods to use past information and experiences in new projects. These studies have applied different techniques from other areas such as Knowledge Management (KM) and Artificial Intelligence (AI).

Knowledge management systems are information technology-based systems aiming at improving organizational knowledge processes, including creation, storage/retrieval, transfer, and application of knowledge [2]. One important step in knowledge management systems is to effectively search databases and find the relevant knowledge. The conventional retrieval of relevant knowledge can often be difficult and sometimes results in several irrelevant documents [14]. For instance, in construction, a method was proposed in which the intended information was retrieved through a simple Google™-like search or an advanced search function [44].

Case-Based Reasoning (CBR) is one of the popular techniques to solve a problem by reusing past information. A CBR system recalls a similar past situation to solve a new problem [21]. In construction, CBR has been used in various areas, such as cost estimation [3], [35], safety [12], structural design [26], and planning [38], [29]. The first step in CBR methods is to measure the similarity of a new case with previously stored cases to retrieve the most similar case(s) [6]. The retrieving process requires some predefined nominal and/or numerical attributes, such as type, size, structural system, and location of the project. In addition, a user-defined weight is considered for each attribute which will be used to calculate the similarity of cases and retrieve the most similar project(s). One of the existing challenges in this step is to find the most relevant attributes and their appropriate weights.

In addition to CBR, some AI methods have been used in construction to model different problems based on the past data and information to predict important project information, such as cost, schedules, and safety plans. Neural networks and linear regression models have been implemented to estimate project costs [20] and facilitate project planning [47].

A quantitative similarity measurement of construction projects can help project managers find similar past projects and extract related information and documents. This can happen at various stages of a project, such as planning and execution phases. Quantitative similarity assessment between projects can potentially improve current CBR and other AI methods by providing more comprehensive attributes that consider the entire project rather than focusing on certain attributes.

The research studies on quantitative measurement of similarity between construction projects, however, are still limited. For example, a recent attempt in the area of project bundling proposed a method to quantify construction projects similarity by vectorizing the projects’ pay items and measuring the distance between vectors [34].

Scope management of a construction project requires comprehensive assessment of the project and a main outcome of this assessment, i.e. Work Breakdown Structure (WBS), is used by other project management areas, namely project time and cost management [32]. But there is not any research attempt to use WBS, as a hierarchical breakdown of the scope of a project, for similarity assessment of the projects. The outcome of this assessment can identify similar projects for better development of WBS and project planning of a new project.

The aim of this research study is to develop a method to assess the similarity of construction projects using their WBSs. It has been hypothesised that the tasks and services required during the construction phase can be used to develop metrics to measure the similarity of construction projects. Since the WBS of a project contains hierarchical information about its scope, WBS was considered as a potential representative of construction projects. Natural language processing (NLP) techniques were employed in the proposed method to extract semantic attributes of the work-packages. This method calculates a score between 0 and 1 to determine the semantic similarity of two WBSs.

Section snippets

Work breakdown structure (WBS)

Project Management Institute (PMI) defines WBS as “a hierarchical decomposition of the total scope of work to be carried out by the project team to accomplish the project objectives and create the required deliverables. The WBS organizes and defines the total scope of the project and represents the work specified in the current approved project scope statement” [32]. In another word, the main goal of WBS is to present a complete and proper scope of the entire project work [17].

The highest level

Methodology

This paper proposes a method to quantify similarity of construction projects based on semantic and structural metrics derived from their WBSs. The WBS of a project includes some nodes, which are labelled with tasks required to complete that project. The focus of this research is to quantify the similarity of construction projects based on the tasks required to build a project during its construction phase. A method was developed in Python programming environment to compare documented

Experimental results

This section presents a set of experiments to evaluate the performance of the defined WBS similarity metrics in distinguishing construction projects and retrieving relevant samples. The experiments were carried out on fifteen different construction projects test samples.

These WBSs were developed for the construction phase of the projects, and other phases, such as feasibility study and design phases, were excluded. Three-dimensional models of five different construction projects were given to

Conclusion

Reuse of the knowledge and experiences gained from completed construction projects can improve planning of the new projects. In order to reuse knowledge, finding similar past projects is critical. This research was undertaken to develop quantitative similarity metrics, to measure the similarity of construction projects using the WBS as their representative. These metrics were implemented using NLP techniques written in Python programming language. The similarity metrics were evaluated based on

Future works

The proposed method could identify similar projects using their WBSs. But the future research can investigate inclusion of major quantitative attributes, such as work quantity of the tasks and their duration, to enhance the similarity assessment of the construction projects. The vocabulary source in this research (i.e. WordNet) is a general and comprehensive source, and might not be able to provide flawless similarity assessments for some technical terms. Developing a specialized resource for

Data availability

The source code of the developed program (in Python programming language) is publicly available and can be found at: https://osf.io/b8qvy/ Data generated or analysed during the experiments are available from the corresponding author upon reasonable request.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This research project was funded by Discovery grant RGPIN-2015-03812 from Natural Sciences and Engineering Research Council of Canada.

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View full text

Quantitative similarity assessment of construction projects using WBS-based metrics

Abstract

Introduction

Section snippets

Work breakdown structure (WBS)

Methodology

Experimental results

Conclusion

Future works

Data availability

Declaration of Competing Interest

Acknowledgement

Build. Environ.

Math. Comput. Simul

Inform. Syst.

Build. Environ.

Adv. Eng. Inf.

Adv. Eng. Inf.

Autom. Constr.

Adv. Eng. Inf.

Autom. Constr.

Adv. Eng. Softw.

Autom. Constr.

Concept relation extraction from construction documents using natural language processing

J. Construct. Eng. Manage.

Knowledge management and knowledge management systems: conceptual foundations and research issues

MIS Quarterly

The relationship between recall and precision

J. Am. Soc. Inform. Sci.

Natural language processing

Ann. Rev. Inform. Sci. Technol.

Facilitating knowledge sharing and reuse in building and construction domain: an ontology-based approach

J. Intell. Manuf.

Measuring similarity between semantic business process models

A model of project knowledge management

Project Manage. J.

Case-based reasoning approach to construction safety hazard identification: adaptation and utilization

J. Construct. Eng. Manage.

A survey of text similarity approaches

Int. J. Comput. Appl.

A framework of knowledge management systems: issues and challenges for theory and practice

ICIS 2000 Proc.

A formal basis for the heuristic determination of minimum cost paths

IEEE Trans. Syst. Sci. Cybernetics

Effective Work Breakdown Structures