Unmanned Aerial Systems (UASs), which have become a key tool in remote sensing in recent years, have also rapidly entered the practice of Architecture, Engineering, Construction, and Operations (AECO). This paper presents an application study of a methodology for monitoring construction progress and quality using real-time data from a commercial building during the execution phase and the results of an analysis of the costs and benefits of monitoring construction progress and quality with and without a UAS. The methodology used consists of three parts: (1) automated data collection at the construction site, (2) data processing, in which the collected data are processed to generate the outputs necessary for the data analysis, and (3) data analysis to monitor construction progress and quality. The method is based on the detection of structural elements, combined with four principles: the sectional approach, the calculation of the concrete volume, the height-distance measurement, and the detection of defects by visual comparisons. The cost-benefit analysis considers three cases: monitoring of progress (1) by the construction company using the UAS, (2) by external contractors using the UAS, and (3) by the construction company without using the UAS. We show that the in-house operation of the UAS is associated with medium costs. However, a return on investment can be achieved quickly if the strategy for the operation of the UAS is clearly defined. In summary, the use of a UAS for the automated monitoring of the construction progress and quality of commercial buildings is practicable, which can quickly lead to a return on investment. We also show that there is great potential for further improvements.

Interventions on infrastructure networks in cities cause disruptions to the services provided by those but also to other networks that have to be at least partially shut down for the interventions executed. Due to these effects, there is substantial benefit to be obtained by grouping interventions on networks that are spatially close to one another. This benefit is principally due to reduced costs of intervention and reduced service disruption. In this paper, two intervention grouping methodologies to develop work programs for infrastructure networks are investigated. The first is based on static, the second is based on dynamic grouping. The two methodologies are investigated by developing work programs on multiple infrastructure networks in an urban area and compared against the same methodologies, albeit without coordination. In the example, interventions on the objects of five different infrastructure networks are grouped based on failure probability of the objects and their closeness. It is found that the dynamic grouping methodology results in work programs that result in a better consideration and prioritisation of objects that are in urgent need for an interventi, while accounting for the synergies that can be created due to efficient coordination. The advantages, disadvantages and future research directions are discussed.

To ensure that transport infrastructure provides acceptable levels of service with respect to extreme events, the resilience of the infrastructure needs to be estimated and targets for it need to be set. In this paper, the methodology proposed in the Foresee EU research project is presented. The methodology allows managers to measure, and set targets for, the resilience of transport systems in all situations. It requires clear definition of the transport system and how the service provided and the resilience are to be measured. The methodology allows consideration of the fact that transport infrastructure managers need to estimate resilience with various degrees of accuracy depending on the specific problem to be addressed, the time frame at disposition and the expertise available. These various levels of accuracy are covered by proposing the use of (a) simulations, (b) indicators whose values are directly related to increases in expected restoration intervention costs and reductions in service, and (c) the percentage of fulfilment of indicators. Once resilience has been estimated, the methodology provides guidance on how to set resilience targets with or without cost–benefit analysis. For demonstration, the explanation of the steps of the guideline is supported by their use for a simple transport system.

Inland waterways often consist of large numbers of man-made objects to ensure navigability. These objects are of many different types, ages and sizes, and deteriorate in uncountable of different ways. In order to ensure that the deterioration of the objects does not result in a loss of navigability, interventions must be executed. This, however, produces costs, in terms of both labour and material costs and costs of loss of service if the waterway is rendered non-navigable during intervention. In this paper, a methodology is presented to determine optimal multiple time period intervention programmes for inland waterways. The optimal intervention programme is the one that has highest net benefit, i.e. overall benefits minus overall costs, where benefits are the reduction in risk of failure. A genetic algorithm is used to overcome the problem of combinatorial explosion when many objects, in many states, over many time periods are to be considered. The exact formulation of the genome, as well as the genetic fitness function, are presented. They are used to determine an optimal intervention programme for a fictive inland waterway network. The results are presented and discussed, and an outlook is provided on further steps to improve this methodology.

In order to find the optimal intervention program for municipal infrastructure networks (e.g. road, gas or water distribution), a combined methodology is necessary to take into consideration the costs of interventions in terms of owner costs, but also potential losses of service before, during and after the execution of the interventions, and the costs due to the loss of service when interventions are being executed. It is also necessary to take into consideration: 1) intervention cost reductions are possible when interventions are executed on multiple networks simultaneously and 2) interventions on one network can increase the failure probability of another network. This paper presents a methodology to determine optimal intervention programs for multiple infrastructure networks simultaneously using a single mathematical representation, taking into consideration these issues. The service loss is modeled using a new generic model applicable to all networks. The methodology is discussed, and recommendations for future work are provided.

Planning interventions on urban infrastructure networks requires the consideration of costs of interventions and interruptions to the service provided by the infrastructure. It also requires taking into consideration how these costs and interruptions change due to the proximity of close networks. For example, it is less expensive to replace a gas pipe if the road is open, and, if a pipe is being replaced, there is a probability that the adjacent water pipe will be hit, resulting in a loss of service. Determining the optimal intervention programme for a single network is challenging as one has to consider all the objects within the network. Determining the optimal intervention programmes for multiple networks is even more challenging, particularly because the number of possible intervention programmes explodes. In this paper, the application of a unified model of the service provided by urban infrastructure networks to be used in the search for optimal intervention programmes on multiple networks simultaneously is presented. By using a single equation for all networks, the model enables an increase in the speed of calculation compared to traditional models, as a single equation can be used. The reductions in accuracy from more traditional models are discussed.