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.

Thermal conductivity is an important feature of asphalt pavement. However, because of the lacking of a proper experiment device, the experimental analysis of the thermal conductivity of semi-rigid base asphalt pavement is difficult. According to the slab method and Fourier’s heat conduction law, an experiment device is developed to examine the coefficient of thermal conductivity of granular mixtures stacked in large volume and help understanding the thermal conductivity of asphalt pavement. This device is used to examine the thermal conductivity of both asphalt mixtures and cement-stabilised aggregates based on Guang-ba motorway construction. The experimental results show that the coefficient of thermal conductivity of both asphalt mixtures and cement-stabilised aggregates linearly increases with the temperature growth. In general, the asphalt mixtures have better thermal conductivity than the cement-stabilised aggregates. In terms of asphalt mixtures, the void ratio heavily affects their thermal conductivity and the aggregates have larger contribution on the thermal conductivity than the binder. In terms of cement-stabilised aggregates, the most important factor of thermal conductivity is the void ratio, followed by gravel size Also the heat conduction of the asphalt pavement is often instantaneous rather than stable.

Maintenance and rehabilitation (M&R) is necessary to keep pavement networks in good condition. Due to the capital intensity, M&R funding is always insufficient. The annual budget, determining the available funding, is a critical criterion when planning M&R treatments. However, its values are often given, and the determination of the values is seldom discussed. To fill the gap, this paper focuses on both the determination of annual budgets and the budget allocations, and therefore enhances the network-level decision-making on M&R by developing a Multi-Objective Optimization (MOO) method. This method does not only optimize and trade off the annual budgets and their consequences, but also allocates the funding across the entire network through generating the optimized M&R decisions. According to a case study with 50 segments, the developed method successfully and effectively identified non-linear discrete relationship between the minimized annual budgets and the maximized M&R benefits subject to all the constraints, and generated the optimized annual budget allocation for each M&R decision. The achievements of this paper can be used to enhance the efficiency of M&R decisions and contribute to informed pavement management.

Continental collision is a crucial process in plate tectonics, whereas our understanding regarding the tectonic complexities at such convergent plate boundary remains largely unclear in terms of the evolution and the controlling parameters of its lateral heterogeneity. In this study, we conducted a series of two-dimensional numerical experiments to investigate how continental lithospheric thermal structure influences the development of lateral heterogeneity along the continental collision zone. Two end members were achieved: 1) Continuous subduction mode, which prevails when the model has a cold procontinental moho (Tmoho ≤ 450 °C). In this case, a narrow collision orogen develops, and the subducting angle steepens with the increasing retrocontinental Tmoho. 2) Continental subduction with a slab break-off, which generates a relative wide collision orogen, and dominates when the model has a relative hot procontinental Tmoho (≥ 500 °C), especially when the Tmoho ≥ 550 °C. In contrast, Hr is the second-order controlling parameter in varying the continental collision mode, while it prefers to enhance strain localization in the upper part of the continental lithosphere and promote the growth of shear zones there. By comparing the model results with geoscience observations, we suggest that the discrepant evolutionary paths from the continuous subduction underlying the Hindu Kush to the continental subduction after slab break-off beneath eastern Tibet may originate from the inherited lateral inhomogeneity of Indian lithospheric thermal structure. Besides, the high content of crustal radioactive elements may be one of the important factors that controls the formation of large thrust fault zones in the Himalayas.