Модел за детекцију и анализу узрока кашњења на пројектима базиран на подацима издвојеним из неструктурираних извора
ǂA ǂmodel for detection and analysis of causes of delay on projects based on unstructured textual sources
Докторанд
Ivanović, MarijaМентор
Stojadinović, ZoranЧланови комисије
Ivanišević, NenadTrivunić, Milan
Marinković, Dejan
Nedeljković, Đorđe
Метаподаци
Приказ свих података о дисертацијиСажетак
ашњење, базни узроци кашњења, изградња путне
инфраструктуре, машинско учење, рударење по текстуалним документима,
Transformer, неструктурирани подаци
Time overrun in construction projects is a global phenomenon that has been
researched for decades. The traditional approach to detection and analysis of
causes of delay usually involves gathering experts’ experiences acquired on similar
projects (grouped by their type or geographical location). The result of such an
approach is list of causes of delay, hierarchically arranged according to their
importance. Such empirical research is burdened with bias and subjectivism of
experts and does not lead to the detection of the root causes of delay at a single
project level.
A database, formed using data collected from 75 road infrastructure projects
implemented in Serbia between 2004 and 2021, is used to demonstrate the
traditional approach’s weaknesses and to create a basis for establishing a new
approach. The results of research and analysis of the database show that over 80%
of projects got delayed with an average time overrun greater than 90% of the
contract duration. Based on the survey ...involving key stakeholders on the database
projects, a causes of delay list was formed that does not deviate from lists in most
studies. Furthermore, low values of Spearman rank correlation were obtained
(0,204 - 0,565) between attitudes of different stakeholders, which confirms the
significant presence of subjectivism and bias in the conducted empirical research
(surveys).
The main goal of the doctoral dissertation is to create a new model for unbiased
discovery of the root causes of delays at the single project level and its entities,
using machine learning on unstructured text documentation from the project. The
chosen documentation for the development of the model is Minutes of Meetings
(MoM) because they contain comprehensive information about delays, which
occurred at the time of the issues, with a precise time frame. Machine learning
techniques using Transformer language models enable automatic detection of
causes of delays. Focused expert knowledge is used for additional unbiased
training of the model for the selected domain of road infrastructure, by connecting
parts of the text with causes of delay from a previously defined list. Recognized
entities of road infrastructure projects are tunnel, route, and bridge. By combining
the mentioned elements, the dissertation developed an analytical Model for the
detection and analysis of the causes of delays in road infrastructure construction
projects, called DREAM (Delay Root-causes Extraction and Analysis Model).
In the first phase, DREAM automatically generates a causes of delay list by project
entities, based on the frequency of their occurrence in Minute of Meetings. The
results show that the model can detect the causes of delay, returning acceptable
recall values (recall = 0.69, for the most frequent causes of delay).
In the second phase, enabled by MoM dates, DREAM adds a new and unique
feature – graphs of the temporal distribution of causes of delay during the project.
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By qualitatively analyzing these graphs that show the frequency and intensity of
individual causes of delay, experts can understand the nature of the causes of
delay, which enables them to detect root causes, the ultimate goal of all research
related to delays on construction projects.
The conducted research provides scientific and practical contribution. A new
approach to the causes of delays identification and analysis is proposed through a
developed analytical model based on unstructured data, machine learning, and the
focused use of expert knowledge. DREAM overcomes the disadvantages of the
traditional approach when creating a causes of delay list, and enables the
discovering of the root causes of delay by applying a unique feature - temporal
distribution of the causes of delay. In a practical sense, the proposed model
provides unbiased support in reconstructing the events related to delay at the
single project level and its entities, which contributes to the reduction of disputes
between contracting parties and aide intelligent decision-making on future
projects.