Infrastructure assets, serving everyone’s daily life, are an essential foundation of any society. Their management faces a wide range of challenges. Hence optimization methods are increasingly applied to assist making management decisions in infrastructure asset management (IAM). A large number of articles apply a broad range of optimization methods in their decision making (DM) and achieve great results. However, they mainly focus on individual methods and a comprehensive knowledge, given the broad range of optimization methods, is hardly discussed. Hence it is valuable to analyze and graphically present the existing knowledge on this subject. This paper, based on a total of 337 articles, provides an overall review of the applications of optimization when making management decisions in IAM, with the intension of enhancing the optimization application and method selection and guiding the future research in this field. More specifically, this paper introduces the application process of optimization when assisting DM in IAM, summarizes the previous application research, and discusses the popular optimization methods applied in DM in IAM. According to the literature review, this paper confirms optimization can effectively assist DM in IAM and a wide range of optimization methods are applicable to assist a variety of DM problems. The recommendations on the applications and selection of optimization methods in the context of IAM are also made to facilitate the applications.

A means of encoding and decoding data using wireless orbital angular momentum (OAM) modes is proposed and analysed. Source data symbols are used to select an OAM mode, which is generated using an 8‐element circular array. A 2‐element array is used to detect the mode by estimating the phase gradient of the received signal, and hence identifying the transmitted data symbol. The results are presented in terms of mode estimation error.

Investment decision making in transportation asset management is typically characterized by a wide diversity of asset types for purposes of optimization at overall system level. To enhance investment analysis and decision making for these multiobjective problem types, the analysis of trade-offs associated with different performance measures can be illuminating and informative. This paper provides techniques for efficient trade-off analysis as part of multiobjective-optimization for transportation asset management. The multiobjective-optimization problem is first formulated by establishing the objectives expressed in terms of network-level performance measures underlying the analysis of trade-offs. Then, the Extreme Points Nondominated Sorting Genetic Algorithm II (NSGA II) technique, an improvement over traditional NSGA II, is applied to generate Pareto frontiers that illustrate the trade-offs. Using candidate projects from a varied range of asset types as a case study, the paper successfully cond...

This article presents a framework and an illustrative example for identifying the optimal pavement maintenance and rehabilitation (M&R) strategy using a mixed-integer nonlinear programming model. The objective function is to maximize the cost-effectiveness expressed as the ratio of the effectiveness to the cost. The constraints for the optimization problem are related to performance, budget, and choice. Two different formulations of effectiveness are derived using treatment-specific performance models for each constituent treatment of the strategy; and cost is expressed in terms of the agency and user costs over the life cycle. The proposed methodology is demonstrated using a case study. Probability distributions are established for the optimization input variables and Monte Carlo simulations are carried out to yield optimal solutions. Using the results of these simulations, M&R strategy contours are developed as a novel tool that can help pavement managers quickly identify the optimal M&R strategy for a given pavement section.

This study explores the relationship between monocentric and polycentric urban forms and commuting patterns in Chinese cities with dense populations and rapid motorization and urbanization, with Wuhan (three subcenters separated by rivers since the city’s formation) and Xi’an (a monocentric city) as case cities. The analysis uses statistical methods and logit models based on 1,194 and 1,501 surveyed households in Wuhan and Xi’an, respectively. Compared with Xi’an and other polycentric cities, Wuhan has shorter commuting distances in the outer areas (3.9 km), smaller car mode share (16.6%), larger nonmotorized mode share (36.8%), more commutes inside the subcenters (91.3%), more public transit use when commuting among the subcenters (67.3%), and less commuting ${\mathrm{CO}}_2$ emissions. The reason lies in its polycentric structure with strong industries in the subcenters and the limited number of and capacity for traffic corridors among the subcenters. These findings can inform sustainable transportation developments in Chinese cities and city cluster areas and can provide empirical evidence and reference values for other global cities.