The current methods that aim at monitoring the structural health status (SHS) of road pavements allow detecting surface defects and failures. This notwithstanding, there is a lack of methods and systems that are able to identify concealed cracks (particularly, bottom-up cracks) and monitor their growth over time. For this reason, the objective of this study is to set up a supervised machine learning (ML)-based method for the identification and classification of the SHS of a differently cracked road pavement based on its vibro-acoustic signature. The method aims at collecting these signatures (using acoustic-sensors, located at the roadside) and classifying the pavement’s SHS through ML models. Different ML classifiers (i.e., multilayer perceptron, MLP, convolutional neural network, CNN, random forest classifier, RFC, and support vector classifier, SVC) were used and compared. Results show the possibility of associating with great accuracy (i.e., MLP = 91.8%, CNN = 95.6%, RFC = 91.0%, and SVC = 99.1%) a specific vibro-acoustic signature to a differently cracked road pavement. These results are encouraging and represent the bases for the application of the proposed method in real contexts, such as monitoring roads and bridges using wireless sensor networks, which is the target of future studies.

Smart cities need technologies that can be really applied to raise the quality of life and environment. Among all the possible solutions, Internet of Things (IoT)-based Wireless Sensor Networks (WSNs) have the potentialities to satisfy multiple needs, such as offering real-time plans for emergency management (due to accidental events or inadequate asset maintenance) and managing crowds and their spatiotemporal distribution in highly populated areas (e.g., cities or parks) to face biological risks (e.g., from a virus) by using strategies such as social distancing and movement restrictions. Consequently, the objective of this study is to present an IoT system, based on an IoT-WSN and on algorithms (Neural Network, NN, and Shortest Path Finding) that are able to recognize alarms, available exits, assembly points, safest and shortest paths, and overcrowding from real-time data gathered by sensors and cameras exploiting computer vision. Subsequently, this information is sent to mobile devices using a web platform and the Near Field Communication (NFC) technology. The results refer to two different case studies (i.e., emergency and monitoring) and show that the system is able to provide customized strategies and to face different situations, and that this is also applies in the case of a connectivity shutdown.

Natural disasters and the poor management of civil engineering structures and infrastructures require timely action and new tools such as specially designed structural health monitoring platforms. This paper proposes an innovative platform based on a network of wirelessly connected, low-power, and renewable-energy-fed sensor units. The platform is a multipurpose tool for diagnostics, maintenance, and supervision, capable of simultaneously carrying out damage detection, localization, identification, and “multiclass” and “multi-material” level quantification of different types of failures. In addition, it works as a decision support tool for emergency management and post-disaster assessment, here tailored for an Italian theme park. The platform uses innovative algorithms based on the concept of the vibro-acoustic signature of the asset monitored. The vibro-acoustic signatures of the monitored assets are gathered by the microphones and accelerometers of the platform’s sensor units. Then, almost simultaneously, they are analyzed using specifically designed wavelet-based and convolutional-neural-network-based algorithms, which are able to extract crucial information about the structural and environmental conditions of both the asset and the areas of the thematic park. In addition, the platform shows escape routes during an emergency, indicating meeting points and helping people to proceed safely along a recognizable escape route to a safe place, as demonstrated by the simulations.

Traffic produces vibrations and noise that affect the livability and structural integrity of the built environment. Despite the fact that many studies focused on traffic-induced vibrations and noise, there is a lack of studies linking the vibrations propagating into the road pavement and the related acoustic response (or acoustic signature) as a means to assess the structural health status. Indeed, monitoring this response can lead to an estimation of the road layer structural condition and an identification of cracks that occurred because of the traffic. Consequently, the objectives of this study are to (i) model the involved phenomena through a Finite Element Method (FEM) analysis; (ii) compare data and simulations; and (iii) set up an Extended Finite Element Model (EXFEM) that is able to forecast the change of the road acoustic signature over time because of the presence of occurred cracks. Loads and sound-related phenomena (generation, transmission, interaction with cracks) were simulated through an EXFEM software. In addition, in order to estimate the effectiveness of the study, the aforementioned simulations were compared with real data gathered from a Dense Graded Friction Course road pavement in different and controlled structural conditions through a specially designed, microphone-based electronic system. Even if further studies are needed to better fix the measurement chain and better carry out the FEM analyses, preliminary results show that the EXFEM model is able to reproduce, with good approximation, the measured signals and that this model can be used to forecast the effects of different types of cracks on the propagation of vibration into road pavements.

High–speed railways (HSRs or AV) and high–capacity railways (HCRs or AC, herein in the sense of open to freight trains) are crucial for the social and economic development of regions and nations. Their design, construction, and maintenance should comply with many requirements, including environment–, finance–, and policy–related ones. To this end, it is noted that the 2030 Agenda for Sustainable Development Goals (UN–SDGs, United Nations Member States, 2015) lists 17 targets, including decent work and economic growth (number 8), industry, innovation and culture (n. 9), and take urgent action to combat climate change and its impacts (n. 13). Despite the above, when analysing costs, many uncertainties arise. In light of the foregoing, the main objectives of the study presented in this paper have been confined to the definition of a model for the estimation of HSR and HSR/HCR infrastructure cost. Theoretical considerations and data derived from Italian (both HSR and HSR/HCR), Spanish, and French HSR projects were used to set up and validate the proposed model. Results demonstrate that, under given conditions, it is possible to explain cost variability in terms of four main factors, namely high capacity (ACF), speed (SF), national (NF), and freight train factor (K), where this latter mainly refers to the need for longer tracks when freight trains are the main type of traffic.