This paper presents a method and prototype implementation for tracking and assessing the structural condition of bridges. The method uses building information modeling (BIM) software to link and analyze data related to the inspection, evaluation, and management of bridges. In the proposed method, an inspector uses a custom software add-in, developed by the authors, to capture information on damage type, amount, severity, and location gathered during bridge inspection. Next, a custom tool in Microsoft Excel evaluates the structural performance and provides load ratings of the inspected bridge. The proposed method was validated through field testing on a case study bridge. Findings from this study suggest that BIM can effectively facilitate the inspection and evaluation of bridges, which may, in the future, enable transportation agencies to more efficiently manage bridge inventories and lead to a more automated practice.

Building maintenance is a fundamental practice in facility management, which supports the longevity of a building. Increasing costs of maintenance practices is a challenge for facility management professionals. Given that, building maintenance decisions often comprise complex and conflicting criteria. The primary purpose of this study is to develop and rank a set of criteria needed for constructing a multi-criteria decision-making model for use in building maintenance processes. This study also has an exploratory aspect and tries to establish the decision-making and condition assessment practices currently used in facility management. To do so, a literature review was conducted to reveal the significant criteria for building maintenance decision-making processes. Moreover, the results of a nationwide survey conducted with the members of two globally recognized facility management organizations were utilized. Identified criteria address a gap in facilities management research, i.e., the lack of comprehensive criteria in building maintenance decision-making, and can be used for the development of a multi-criteria decision-making model for use in building maintenance processes. Furthermore, the results of this study can help establish the current status of decision-making and condition assessment practices in facility management.

Building Information Modeling (BIM) is an emerging concept within the construction industry. With the increase in the utilization of BIM, it has become clear that there is some legal uncertainty in dealing with this technology. While the technology is rapidly growing in sophistication, these legal uncertainties may be acting as obstacles on BIM's wider adoption. It is important that the potential users of BIM become aware of the potential legal issues and how these issues are evolving within the companies that have been using BIM to make better informed decisions. Within this context, this article's purpose is to: (i) provide an overview of the potential legal issues associated with BIM and (ii) present the perspectives of companies (that have been involved in projects using BIM) on those potential legal issues and their experiences in dealing with those issues as gathered through semi-structured interviews. The interviews included ten professionals representing different entities in the construction industry that are primarily affected by the BIM-related potential legal issues, architects, owners, and contractors. The potential legal issues that were investigated are model ownership, right to rely, shifting of risk, standard of care, and BIM compensation.

Data envelopment analysis (DEA) is a mathematical method based on production theory and the principles of linear programming. It enables one to assess how efficiently a firm, organization, agency, or such other unit uses the resources available (inputs) to generate a set of outputs relative to other units in the data set. Recent papers by different writers present different applications of DEA in the transportation engineering domain. All of these papers are published in transportation journals. These papers are mainly aimed at addressing the transportation-related issues and thus do not focus too much on the DEA concept itself. It can be asserted that DEA is very likely to be used more and more in the transportation engineering domain. Given this, there is a need for the transportation professionals to fully understand the DEA concept. It is essential for such a community to identify cases where the application of this innovative and powerful method can be useful to help the decision-making process, to accurately apply DEA in a particular setting, to derive meaningful conclusions from the obtained results, and to acknowledge the limitations of DEA in certain cases so as to approach the results with caution. The purpose of this paper is to illustrate to the civil engineering, more specifically to the transportation engineering community the use of this powerful approach in performing comparative performance measurement. Within this context, this paper will address a transportation-related problem by using the DEA approach. Different from the other papers containing transportation-related DEA applications (as mentioned above), this paper will discuss, in detail, the steps that need to be taken to generate the DEA model and solve it.

Within the past two decades, the road maintenance concept has been gaining tremendous attention. This has brought about new institutional changes, predominant of which is the challenge for maintenance managers to achieve maximum performance from the existing road system. Such challenge makes it imperative to implement comprehensive systems that measure road maintenance performance. However, as pointed out by the Transportation Research Board, even though the road maintenance performance measurement systems developed and implemented by the state departments of transportation elaborate on the maintenance level of service (i.e., effectiveness of the road maintenance), the fundamental relationships between the maintenance level of service and the budget requirements (i.e., efficiency of road maintenance) need more investigation. This is mainly because not knowing how “efficient” state departments of transportation are in being “effective” can lead to excessive and unrealistic maintenance budget expectations. In an effort to address this need, this research aims to develop and implement a comprehensive framework that can measure the overall efficiency of road maintenance operations. This framework is designed to consider the effects of environmental (e.g., climate, location, etc.) and operational (e.g., traffic, load, etc.) factors on such overall efficiency. This paper introduces the efficiency measurement framework and specifically provides an overview of the data and modeling issues faced during the early stages of the implementation of the framework to the Virginia Department of Transportation’s case for the maintenance of bridges.

A companion paper by the writers introduces the framework that was developed using an approach called data envelopment analysis to measure the overall efficiency of road maintenance operations while considering the effects of environmental and operational factors (both of which are beyond the control of the decision maker, i.e., the maintenance manager) on such overall efficiency. Such paper introduced the data envelopment analysis approach, presented the road maintenance efficiency measurement framework and provided an overview of the data and modeling issues faced during the earlier stages of the implementation of the framework to the Virginia Department of Transportation’s case for the maintenance of bridges. This paper presents the core implementation stages of the framework in trying to identify (1) the relative efficiency of seven counties of Virginia in performing bridge maintenance; (2) the benchmarks (peers) and targets that pertain to the inefficient counties; and (3) the effects of the environmental and operational factors on the road maintenance efficiency of counties. The results and discussions presented in this paper can be used to direct decision makers’ attention to develop a better understanding of the reasons why some counties are more efficient than others.