The inconsistency of data terminology has imposed big challenges on integrating transportation project data from distinct sources. Differences in meaning of data elements may lead to miscommunication between data senders and receivers. Semantic relations between terms in digital dictionaries, such as ontologies, can enable the semantics of a data element to be transparent and unambiguous to computer systems. However, because of the lack of effective automated methods, identifying these relations is labor intensive and time consuming. This paper presents a novel integrated methodology that leverages multiple computational techniques to extract heterogeneous American-English data terms used in different highway agencies and their semantic relations from design manuals and other technical specifications. The proposed method implements natural language processing (NLP) to detect data elements from text documents and uses machine learning to determine the semantic relatedness among terms using their occurrence statistics in a corpus. The study also consists of developing an algorithm that classifies semantically related terms into three different lexical groups including synonymy, hyponymy, and meronymy. The key merit in this technique is that the detection of semantic relations uses only linguistic information in texts and does not depend on other existing hand-coded semantic resources. A case study was undertaken that implemented the proposed method on a 16-million-word corpus of roadway design manuals to extract and classify roadway data items. The developed classifier was evaluated using a human-encoded test set, and the results show an overall performance of 92.76% in precision and 81.02% recall.

A better understanding of top-down estimating practices and their contribution to budgeting accuracy allows public transportation agencies to allocate limited construction funds more efficiently. This paper builds on a recent study that evaluated the accuracy of early highway construction cost estimates for the Montana Department of Transportation (MDT). The study included 996 MDT projects awarded between 2006 and 2015, with more than $2.2 billion in construction costs, accounting for more than 82% of the agency’s construction spending. The results suggest that top-down models provide a means to improve the prediction accuracy of agency cost estimates (when measured as the mean absolute percentage error of project costs), particularly for projects with higher levels of complexity and lower sample sizes. These conclusions are drawn from a comparison of agency in-house estimates to predictions obtained through artificial neural network (ANN) and multiple regression models. In interpreting these findings, the paper demonstrates that the bias-variance trade-off, a common model building concern in the machine learning and artificial neural network literature, is likely a key factor in explaining the prediction performance of simplified models.

Large-scale industrial projects are challenging due to high degrees of complexity, multiple stakeholders with differing business objectives, evolving technology, building in adverse environments, and other difficulties. As a result, they can be seen as high-risk business ventures. Collaborative project delivery strategies such as integrated project delivery (IPD) have been used to manage and mitigate risks associated with delivery of projects in other sectors such as commercial buildings in the United States. This project delivery method focuses on enhanced collaboration and integration to reduce uncertainty and increase the chances of project success. This paper evaluates the change in industrial project performance as collaboration and integration are increased. The research is based on the results of a survey of 85 experienced industrial project management professionals. The survey output was used to develop a quantitative index that measures the level of collaboration and integration during project delivery. The paper finds a statistically significant correlation between the degree of collaboration and integration increases and project performance, meaning projects experiencing a higher level of collaboration and integration have a stronger and more predictable correlation with better project performance. The research findings provide evidence to suggest industrial project performance certainty is improved by increased collaboration and integration, demonstrating industrial projects are good candidates for a more integrated project delivery method.

With the advancement of various technologies, transportation agencies are transitioning from two-dimensional (2D) paper-based to three-dimensional (3D) digital data-based project delivery. This transition results in an increasing amount of digital data exchange throughout the project delivery process, and a holistic framework is required to better manage data for improved decision making. A recently developed concept, civil integrated management (CIM) could potentially assist transportation agencies to transition to digital data-based project and program delivery by providing appropriate stakeholder access to an agency-wide data pool. The purpose of this paper is to further define this vision and concept and establish an initial framework for CIM implementation. An extensive amount of information was collected through two weeks of on-site visits to seven transportation agencies in the United States on their current CIM-related practices. The results obtained from using qualitative research methods...

Data-driven models using historical project attributes to estimate future construction costs, such as multiple-regression analysis and artificial neural networks are both proven techniques that highway agencies could adopt for conceptual cost estimating. This research found literature using those techniques has been solely focused on estimating model performance with little to no attention to the level of effort required to conduct the conceptual estimate. It is commonly believed using more input data enhances estimate accuracy. However, this paper finds for the highway agency studied that using more input variables than necessary in the conceptual estimate does not improve estimate accuracy. Conceptual estimates using the minimum amount of input data to produce an estimate with a reasonable level of confidence is more cost effective. This paper quantifies the effort expended to undertake conceptual estimates using data from a highway agency and concludes that input variables that have a large inf...

Change orders are documents that describe a specific contract amendment to the original scope of work. Historical change orders are invaluable information sources that can provide practical and proven solutions for developing new change orders from similar cases. However, current change order management systems are not efficient in searching for and finding the most related and similar change orders due to inherent weaknesses in current archiving and search processes, such as keyword-based or reason code–based search. This study proposes and develops a natural language processing (NLP)–driven model that can significantly improve the accuracy and reliability of searching cases by restructuring how each change order’s information is stored and retrieved in change order management systems. The NLP-driven model proposed in this study can automatically detect change reasons and altered work items through text representation pattern analysis and training. The proposed model applies semantic frames to define essential semantic components and determines syntactic features for text representation pattern analysis. The model also utilizes a conditional random field (CRF) classifier, which can consider contexts in sequential texts at the model training stage. The proposed model can significantly improve the accuracy and relevancy of the search process to find the most similar cases by allowing context-driven classification, archiving, and retrieval of change orders.