Decision makers in the transportation industry search for a systematic approach to select an appropriate structural system, construction method, and material for bridges. Simple mathematical methodologies are needed to consider different stakeholders’ perspectives. With criteria that occur simultaneously in selecting appropriate material, construction technique, and structural system of bridges, invalid and unexpected results may occur from such complexity. The decision-making process is usually done subjectively by designers and requires much data and extensive experience in bridge design. To address these challenges and assume all substantial criteria within the framework, the PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) multi-criteria decision-making method is used. It is not sensitive to the number and definition of the criteria. The PROMETHEE method is based on the pairwise comparison between alternatives for constructing an outranking relationship to show the degree of dominance of one alternative over another. A case study of the Kashkhan Bridge in Iran is presented to demonstrate implementation of the PROMETHEE method and show how such a decision-making methodology can assist experts in making informed decisions based on a set of comprehensive criteria in the conceptual design stage. Some novel and effective criteria in this study are combined and synthesized to select the appropriate superstructure. Criteria weights and preference and indifference thresholds are collected through questionnaires filled out by bridge experts. Results of the case study show that the most appropriate system for the Kashkhan Bridge is the box girder system with the balanced cantilever method and posttensioned concrete material.

With the pressing need to improve the poorly rated transportation infrastructure, asset managers leverage predictive maintenance strategies to lower the life cycle costs while maximizing or maintaining the performance of highways. Hence, the limitations of prediction models can highly impact prioritizing maintenance tasks and allocating budget. This study aims to investigate the potential of different predictive models in reaching an effective and efficient maintenance plan. This paper reviews the literature on predictive analytics for a set of highway assets. It also highlights the gaps and limitations of the current methodologies, such as subjective assumptions and simplifications applied in deterministic and probabilistic approaches. This article additionally discusses how these shortcomings impact the application and accuracy of the methods, and how advanced predictive analytics can mitigate the challenges. In this review, we discuss how advancements in technologies coupled with ever-increasing computing power are creating opportunities for a paradigm shift in predictive analytics. We also propose new research directions including the application of advanced machine learning to develop extensible and scalable prediction models and leveraging emerging sensing technologies for collecting, storing and analyzing the data. Finally, we addressed future directions of predictive analysis associated with the data-rich era that will potentially help transportation agencies to become information-rich.In classifying highway asset items, it should be noted that state agencies have different classification. For example, California Department of Transportation (CalTrans) categorizes transportation assets in primary classes of pavements, bridges, culverts, and Intelligent Transportation Systems (ITS) [4]. However, North Carolina DOT classifies assets into seven primary groups of pavements, bridges, tunnels, roadside features, pavement markings, rest areas, and maintenance yards [10]. Among these categories, that are different from state to state, we reviewed prediction methods for a subset of assets, including pavements, pavement markings, traffic signs, barriers, and culverts.We focused mostly on recent papers published in the past 20 years. To the best of our knowledge, we included most of the significant research studies reporting the application of predictive analytics. The total number of the investigated papers versus years of publication is shown in Figure 2.

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