This document discusses project risk management and describes several challenges with current computer programs and methods for project risk management. It notes that most current methods favor one-time identification, evaluation and analysis of risks rather than ongoing risk management throughout the project. The document also provides details about different types of computer programs for project risk management, including those focused on risk analysis and those that support risk management processes.

1. PRMis a veryimportanttaskduring project
planning and project control. The most
popular computer programs used for
project risk management are still
somewhat primitive. They have not yet
beendevelopedintocommonplace toolsof
project management in the same way as
time management programs have, for
example.Essentially,almostall methods of
risk management, presented in the
literature on project management and
commercial risk management computer
programs appear to favour one-time
identification, evaluation and analysis of
risks
Project Risk
Management
lezasmith

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Introduction
Risk analysis and risk management have been popular subjects for research, articles,
specialised literature and conferences during the 1990s [8], [9], [10] and [11]. Today’s
project risk management aims at proactive project management, which is also an
objective of this study. PRM is a very important task during project planning and project
control. The most popular computer programs used for project risk management are still
somewhat primitive. They have not yet been developed into commonplace tools of
project management in the same way as time management programs have, for example
[12]. Essentially, almost all methods of risk management, presented in the literature on
project management and commercial risk management computer programs appear to
favour one-time identification, evaluation and analysis of risks. These methods do not
seem to favour simple, quick risk analyses and demand a great deal of work.
Commercially distributed programs mostly deal with the probable project outcomes.
Commercial PRM computer programs fall into two primary categories, which are (1) risk
analysis oriented programs such as @RISK, OPERA and CRYSTAL BALL; and (2)
programs supporting PRM processes such as FUTURA and TEMPER SYSLA. Risk
analysis does not necessarily reveal whether a risk is really becoming a reality but
states the risk’s level of seriousness, its effect on the project and the probability of its
realisation. Also, risk analysis often remains a one-time procedure at the beginning of
the project with very little true risk management being carried out during the course of
the project[ ]
Early Warning Signs
Early Warning Signs An early warning sign is an observation, signal, message, or some
other form of communication that is or can be seen as an expression, indication, proof,
or sign of the existence of some future or incipient positive or negative issue. It is a
signal, an omen, or an indication of future developments (Nikander, 2002)[2]. Ansoff’s
(1975, p. 22)[3] ideas of responding to “weak signals” stated: “A firm that wishes to
prepare for strategic surprises has two options. The first is to develop a capability for
effective crisis management—fast and efficient afterthe- fact responsiveness to sudden
discontinuities. The second approach is to treat the problem before the fact and thereby
minimize the probability of strategic surprises. . . . Both approaches deserve
management attention.” This article has a focus on early warning signs—the proactive
approach. Loosemore (1999, 2000) identified three types of crisis in a construction
project management context. Perceptions of an impending creeping crisis present EWS
that are understood but unaddressed until the crisis occurs. Sudden crises occur
seemingly without warning, whereas periodic crises occur in cycles that may or may not
be understood. Many crises appear without accompanied contingency plans—often

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being perceived as low probability but high potential impact events perhaps best tackled
using an emerging strategy and having sufficiently skilled project management teams to
recognize EWS and react appropriately (Mintzberg, 1987; Mintzberg, Ahlstrand, &
Lampel, 1998). Kappelman, McKeeman, and Zhang (2006) indicated that peoplerelated
and process-related risks scored higher than product-related risks as dominant EWS of
IT project failure Syamil, Doll, and Apigian (2004) argued that behavior-related
performance measures evaluating project processes are mediating variables affecting
theextent to which the given process contributes to the overall project result.
Why construction project?
In analyzing needs and requirements for document classification in construction
information systems, it is pertinent first to review the changing environment within which
the construction industry operates. Some of the key issues include the following:
• Construction projects are usually unique. The characteristics, specifications, and
execution plans of construction products are described in a large number of
construction documents, such as drawings, specifications, schedules, and cost
estimates. These documents necessarily consist of both graphic and nongraphic
information that must be communicated between members of the project team.
• Projects are designed, built, and maintained using dynamic processes, which are
subject to changes throughout the construction life cycle. A number of different factors
substantially affect the implementation of construction projects. Different technologies,
site conditions, constraints, and internal or external impositions can lead to changes.
These deviations have to be communicated to all project partners affected by them in
order to enact the necessary adaptations and changes in accordance with the new
situation. Tracking and enacting all changes in construction information is a great
challenge.
• Unique project teams develop construction processes. According to Cleland ~1995!,
project teams are ‘‘organizational entities devoted to the integration of specialized
knowledge for a common purpose.’’ Team members include representatives of owners,
designers, contractors, and other stakeholder organizations. Each project team member
has its own organizational structure. The organizational configuration of the overall
project team will have to match all individual participants’ organizational structures. It is
important to notice that, according to the contingency theory ~Galbraith 1973!,each
project’s organizational arrangement can lead to different performances.
• Although each individual project’s organization will have a specific function,
interdependencies also occur among them. In order to ensure that the entire system
works as a unit, the different organizations must work in a collaborative and coordinated
way, sharing data, information, and knowledge.

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• Project team members vary in size, capital, and level of information[6] technology ~IT!
capability and are typically small to medium enterprises. A particular problem in the
implementation of IT is the varying capabilities of the team members.State-of-the-art
technologies could be available to some team members, but probably not to all of them.
All these characteristics were considered in the design and development of the
proposed automated document classification system for construction management.
Text mining for managing project documents
In construction projects, a high percentage of project information is exchanged using
text documents, including contracts, change orders, field reports, requests for
information, and meeting minutes, among many others [6]. Management of these
documents in model-based information systems, such as Industry Foundation Classes
(IFC) based building information models, is a challenging task due to difficulties in
establishing relations between such documents and project model objects. Manually
building the desired connections is impractical since these information systems typically
store thousands of text documents and hundreds of project model objects. Current
technologies used for project document management, such as project websites,
document management systems, and project contract management systems do not
provide direct support for this integration. There are some critical issues involved, most
of them due to the large number of documents and project model objects and
differences in vocabulary. Search engines based on term match are available [15] in
many information systems used in construction. However, the use of these tools also
has some limitations in cases where multiple words share the same meaning, where
words have multiple meanings, and where relevant documents do not contain the user-
defined search terms [6] heterogeneous data representations including text documents
[16]; various data analysis tools were also applied on text data to create thesauri,
extract hierarchical concepts,and group similar files for reusing past design information
and construction knowledge [17]. In one of the previous studies conducted by our
research group, a framework was devised to explore the linguistic features of text
documents in order to automatically classify, rank, and associate them with objects in
project models [13]. This framework involved several methods as discussed in our
vision for data analysis on non-traditional construction data sources: – Special Data
Preparation operations for text documents were identified, such as transferring text-
based information into flat text files from their original formats, including word
processors, spreadsheets, emails, and PDF files; removing irrelevant tags and
punctuations in original documents; removing stop words that are too frequently used to
carry useful information for text analysis like articles, conjunctions, pronouns, and
prepositions; and finally, performing word stemming to remove of prefixes and/or
suffixes and group words that have the same conceptual meanings. – The

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preprocessed text data were transformed into a specific Data Representation using a
weighted frequencymatrix A = {aij}, where aij was defined as the weightof a word i in
document j. Various weighting functionswere investigated based on two empirical
observations regarding text documents: (1) the more frequent a word is in a document,
the more relevant it is to topic of the document; and (2) the more frequent a word is
throughout all documents in the collection, the more poorly it differentiates between
documents. By selecting and applying appropriate weighting functions, project
documents were represented as vectors in a multi-dimensional space. Query vectors
could then be constructed to identify similar documents based on similarity measures
such as Euclidian distance and the cosine between vectors. – Data Analysis tools were
applied to develop a framework for integrating text documents in model-based
information systems. The main goal of this integration framework is to improve the
identification and analysis of relevant project documents. The large number of objects in
a project model and of text documents in construction projects makes the proposed
automated integration framework desirable. It generates significant savings in the time
and effort required to link all objects contained in a project model to all relevant
documents generated during the project’s life cycle. [15]
Methodologies
Text Mining and Automated Document Classification.Text mining is increasingly being
used to denote all the tasks that, by analyzing large quantities of text documents, try to
extract possibly useful information. Results of the text mining process in a collection of
documents stored in interorganizational systems can be used to improve information
management in such systems and also to generate knowledge about the subjects
contained in these documents. According to this view, text classification~or
categorization! is an instance of text mining ~Sebastiani 1999!. In this project, the
classes are represented by construction project components. Hence, construction
document classification was defined as the task of assigning a Boolean value to each
pair$dj ,oi%PD3O, where D is a domain of documents and O5$ol ,…,on% is a set of
project components ~classes!. A value of T ~true! assigned to C(dj ,oi) indicates a
decision that document dj is related to the component oi , while a value of F ~false!
indicates that dj is not related to the component oi . The document classification task
can is binary classification, in which a classification decision for each document is made
independently on a class-by-class basis. In binary classification, each document is
classified as relevant or not to each of the existing classes. From a theoretical point of
view, the binary case is more general than the multilabel case, in the sense that an
algorithm for binary classification can also be used for multilabel classification. In order
to do this, the problem of multilabel classification under objects can be transformed into
n independent problems of binary classification where n is the number of classes.

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Through the use of machine learning algorithms, a general inductive process
automatically builds a classifier ~classification model! for each class by observing the
characteristics of a set of documents that have previously been classified manually by a
domain expert. The classification problem is an activity of supervised learning, since the
learning process is driven by the previous knowledge of the categories in some of the
documents that will be used to build the model. Hence, this approach relies on the
existence of an initial corpus of documents previously classified according to their
relevance to a set of project components. A document dj is called a positive example of
oi if C(dj ,oi)5T,and a negative example of oi if C(dj ,oi)5F. After generating the
classification model it is important to evaluate its effectiveness. One alternative for this
evaluation is to split the initial collection of documents into two sets:
• Training set: set of documents that will be used to create theclassification model; and
• Test set: set of documents that will be used for testing the effectiveness of the
classifier.
The documents in the test set cannot participate in the inductive construction of the
classifier; if this condition is not satisfied, then the experimental results obtained would
probably be unrealistically good. The definition of the size of the training set is also
crucial to avoid overfitting. This happens when the classifier performs with few errors on
the training set and does not generalize to the new test cases.
SYSTEM ARCHITECTURE
This section briefly describes the system architecture for the EWAS for monitoring news
articles. News covers everything about anything in world. News update readers can
read about the direction in which his/her interested entity in the world is moving. The
interested entity can be the whole country, a bureaucratic session, a terrorist
organization or anything. Services which analyze and read thousands of news items
from heterogeneous sources and provide consolidated channels to access the news
have become available. The biggest advantage of using such a service is that the
duplication issues, topic identification issues, and other linguistic problems are being
solved during the consolidation process, thus providing filtered information for analysis.
Europe Media Monitor (EMM) is one of these services and is being used in this project
[1]. EMM provides an RSS feed of news items which contain all of the consolidated
news items. RSS adds some structure to the news by specifying its publication info but
still the facts and nuggets lie buried in the text which actually forms the news.
Text Classification Process and Analysis of Algorithms
The project described in this paper used data from construction interorganizational
information systems to evaluate text classification algorithms and to guide the
development of the prototype of a construction document classification system. Bricsnet

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Inc., a company that has developed commercial Internet-enabled project
management and collaboration services specifically for construction, has provided
access to data from current and past projects
that used its services. Bricsnet’s servers are used to host construction project extranets,
creating a communication environment for
project participants and providing a common repository for project documents. One
construction project database was selected as a case study. The database was used by
16 project team organizations and contained around 4,000 document files ~1.5 GB!.
Several types of construction documents were available in this database, including
specifications, meeting minutes, requests for information, architect’s supplemental
instructions, change orders, and field reports, among others. Meeting minutes were
selected for this evaluation, which store information about weekly progress meetings
among project participants. Each topic discussed during a meeting is recorded
in separate items, which are grouped by topics according to specific project divisions.
The divisions for this particular project were
•A–GENERAL;
•B–SCHEDULE;
•C–DEMOLITION-CIVIL;
•D–LANDSCAPE-SITE;
•E–STRUCTURES;
•F–BUILDING–SKIN;
•G–ROOFING-WATERPROOF;
•I–INTERIORFINISHES;
•M–CONVEYANCE;
•N–PLUMBING;
•O–FIREPROTECTION;
•P–HVAC;and
•Q–ELECTRICAL
Originally there were 92 meeting minutes. Each item for all of these meeting minutes
was automatically extracted from the original document and stored in separate
document files. A total of 845 documents were then used in the document classification
process, which followed the steps described and detailed next, which
range from data selection to creation of classification models, followed by discussion of
the results. Data Selection In the first step, classes were defined as the project divisions
presented previously. This step also involved selection of the documents used to create
the classification model. In this step, the training positive, training negative, testing
positive, and testing negative documents for each class were selected. These
documents could be stored in central databases, such as the project
extranets, or in distributed databases. Currently, only the case in which the data are

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stored in a central location is being explored. Data Preparation Text-based information
is usually stored using formats such as word processor, spreadsheet, e-mail, HTML,
XML, Postscript ~PS!, or Adobe Acrobat Portable Document Format ~PDF! files. In
order to apply the preprocessing and classification algorithms, these files must be
converted to text file format. This procedure used file converter systems to create a text
version copy of each document, while keeping the original documents in their native
formats and locations. The text versions were then used in the remaining steps of the
classification process.