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This study aims to use social media data mining to revitalize and support existing urban infrastructure monitoring strategies by extracting valuable insights from public opinion, as current strategies struggle with issues such as adaptability to changing conditions, public engagement and cost effectiveness.

Twitter messages or “Tweets” about public infrastructure in the Philippines were gathered and analyzed to discover reoccurring concerns in public infrastructure, emerging topics in public debates and the people’s general view of infrastructure services.

This study proposes a topic model for extracting dominating subjects from aggregated social media data, as well as a sentiment analysis model for determining public opinion sentiment toward various urban infrastructure components.

The findings of this study highlight the potential of social media data mining to go beyond the limitations of traditional data collection techniques, as well as the importance of public opinion as a key driver for more user-involved infrastructure management and as an important social aspect that can be used to support planning and response strategies in routine maintenance, preservation and improvement of urban infrastructure systems.

The purpose of this paper is to further validate a wireless sensor system developed at Clarkson University for structural monitoring of highway bridges. The particular bridge monitored employs a fiber reinforced polymer (FRP) panel system which is fairly innovative in the field of civil engineering design. The superstructure was monitored on two separate occasions to determine a change in structural response and see how the structural system performs over time.

A series of wireless sensor units was deployed at various locations of the steel superstructure, to measure both the modal response from acceleration measurements as well as quasi‐static and dynamic strain response. Ambient and forced loading conditions were applied to measure the response. Data results were compared over two separate periods approximately nine months apart.

The first eight mode shapes were produced from output‐only system identification providing natural frequencies ranging from approximately 6 to 42 Hz. The strain response was monitored over two different testing periods to measure various performance characteristics. Neutral axis, distribution factor, impact factor and end fixity were determined. Results appeared to be different over the two testing periods. They indicate that the load rating of the superstructure decreased over the nine month period, possibly due to deterioration of the materials or composite action.

The results from the two testing periods indicate that further testing needs to be completed to validate the change in response. It is difficult to say with certainty that the significant change in response is due to bridge deterioration and not other factors such as temperature effects on load rating. The sensor system, however, proved to provide high quality data and responses indicating its successful deployment for load testing and monitoring of highway infrastructure.

The paper provides a depiction of the change in structural behavior of a bridge superstructure using a wireless sensor system. The wireless system provided high‐rate data transmission in real time. Load testing at two different points in time, eight months apart, showed a significant change in bridge behavior. The paper provides a practical and actual physical load test and rating during these two periods for quantifiable change in response. It is shown that the wireless system is capable of infrastructure monitoring and that possible deterioration is expected with this particular bridge design. Additionally, the load testing occurred during different seasons, which could create cause for temperature effects in load rating. This can provide a basis for future performance monitoring techniques and structural health monitoring.

This article aims to explore valuable insights into the construction progress monitoring (CPM) research domain, which include main research topics, knowledge gaps and future research themes. For a long time, CPM has been significantly researched with increasing enthusiasm. Although a few review studies have been carried out, there is non-existence of a quantitative review study that can deliver a holistic picture of CPM.

The science mapping-based scientometric analysis was systematically processed with 1,835 CPM-related journal articles retrieved from Scopus. The co-authorship analysis and direct citation analysis were carried out to identify the most influential researchers, countries and publishers of the knowledge domain. The co-occurrence analysis of keyword was assessed to reveal the most dominating research topics and research trend with the visual representation of the considered research domain.

This study reveals seven clusters of main research topics from the keyword co-occurrence analysis. The evolution of research confirms that CPM-related research studies were mainly focused on fundamental and traditional CPM research topics before 2007. The period between 2007 and 2020 has seen a shift of research efforts towards digitalization and automation. The result suggests Building Information Modelling (BIM) as the most common, growing and influential research topic in the CPM research domain. It has been used in combination with different data acquisition technologies (e.g. photogrammetry, videogrammetry, laser scanning, Internet of Things (IoT) sensors) and data analytics approaches (e.g. machine learning and computer vision).

This study provides the horizon of potential research in the research domain of CPM to researchers, policymakers and practitioners by availing of main research themes, current knowledge gaps and future research directions.

This paper represents the first scientometric study depicting the state-of-the-art of the research by assessing the current knowledge domain of CPM.

Adequate, reliable, and efficient urban infrastructure systems (UIS) are fundamental to sustainable development, social mobility, and economic vitality. As communities continuously rely on basic infrastructure services to support their daily communal functions, major components of UIS are subject to heavy use, and thus rapidly deteriorate over time; hence, it is critical that efficient infrastructure management strategies practices are in place. As current strategies remain confronted with various limitations including adaptability to changing conditions, lack of public engagement, and cost-effectiveness, this study explores social media data mining as an approach to revitalise and support current urban infrastructure monitoring strategies by extracting valuable insights from public opinion. Twitter messages or ‘Tweets’ pertaining to public infrastructure in The Philippines were collected and analysed to identify recurring issues in public infrastructure, emerging topics in public discussions, and the overall perception of the public on infrastructure services. This study presents a topic model that extracts dominant topics from aggregated social media data and a sentiment analysis model that determines public opinion sentiment in relation to different urban infrastructure components. The findings of this study highlight the potential of social media data mining to surpass the limits of conventional data collection techniques and the importance of public opinion as a key driver for a more user-involved decision-making in infrastructure management and as an important social aspect that can be utilised to support planning and response strategies in routine maintenance, preservation, and improvement of UIS.

Recent United Nations Climate Change Conferences recognise extreme climate change of heatwaves, floods and droughts as threatening risks to the resilience and success of public–private partnership (PPP) infrastructure projects. Such conferences together with available project reports and empirical studies recommend project managers and practitioners to adopt smart technologies and develop robust measures to tackle climate risk exposure. Comparatively, artificial intelligence (AI) risk management tools are better to mitigate climate risk, but it has been inadequately explored in the PPP sector. Thus, this study aims to explore the tools and roles of AI in climate risk management of PPP infrastructure projects.

Systematically, this study compiles and analyses 36 peer-reviewed journal articles sourced from Scopus, Web of Science, Google Scholar and PubMed.

The results demonstrate deep learning, building information modelling, robotic automations, remote sensors and fuzzy logic as major key AI-based risk models (tools) for PPP infrastructures. The roles of AI in climate risk management of PPPs include risk detection, analysis, controls and prediction.

For researchers, the findings provide relevant guide for further investigations into AI and climate risks within the PPP research domain.

This article highlights the AI tools in mitigating climate crisis in PPP infrastructure management.

This article provides strong arguments for the utilisation of AI in understanding and managing numerous challenges related to climate change in PPP infrastructure projects.

The aim of this paper is to discuss how new technologies for supporting scientific research will possibly influence the librarians' work.

The discussion is conducted in a context that takes into account the emergence of e‐infrastructures as means to realise a new model of producing, using and sharing information resources and even to change the concept of information resource itself. At the core of this innovation there are virtual research environments, i.e. evolved versions of the current “research libraries”.

The environments provide scientists with collaborative and customised environments supporting results production and exchange around the globe in a cost‐efficient manner. The experiences made with these innovative research environments within the D4Science project is reported.

On the basis of this experience, possible professional profiles are suggested for librarians working in these new evolved “research libraries”.

The purpose of this study is to review the existing fatigue and vibration-based structural health monitoring techniques and highlight the advantages of combining both approaches.

Fatigue monitoring requires a fatigue model of the material, the stresses at specific points of the structure, a cycle counting technique and a fatigue damage criterion. Firstly, this paper reviews existing structural health monitoring (SHM) techniques, addresses their principal classifications and presents the main characteristics of each technique, with a particular emphasis on modal-based methodologies. Automated modal analysis, damage detection and localisation techniques are also reviewed. Fatigue monitoring is an SHM technique which evaluate the structural fatigue damage in real time. Stress estimation techniques and damage accumulation models based on the S-N field and the Miner rule are also reviewed in this paper.

A vast amount of research has been carried out in the field of SHM. The literature about fatigue calculation, fatigue testing, fatigue modelling and remaining fatigue life is also extensive. However, the number of publications related to monitor the fatigue process is scarce. A methodology to perform real-time structural fatigue monitoring, in both time and frequency domains, is presented.

Fatigue monitoring can be combined (applied simultaneously) with other vibration-based SHM techniques, which might significantly increase the reliability of the monitoring techniques.

The swivel construction method is a specially designed process used to build bridges that cross rivers, valleys, railroads and other obstacles. To carry out this construction method safely, real-time monitoring of the bridge rotation process is required to ensure a smooth swivel operation without collisions. However, the traditional means of monitoring using Electronic Total Station tools cannot realize real-time monitoring, and monitoring using motion sensors or GPS is cumbersome to use.

This study proposes a monitoring method based on a series of computer vision (CV) technologies, which can monitor the rotation angle, velocity and inclination angle of the swivel construction in real-time. First, three proposed CV algorithms was developed in a laboratory environment. The experimental tests were carried out on a bridge scale model to select the outperformed algorithms for rotation, velocity and inclination monitor, respectively, as the final monitoring method in proposed method. Then, the selected method was implemented to monitor an actual bridge during its swivel construction to verify the applicability.

In the laboratory study, the monitoring data measured with the selected monitoring algorithms was compared with those measured by an Electronic Total Station and the errors in terms of rotation angle, velocity and inclination angle, were 0.040%, 0.040%, and −0.454%, respectively, thus validating the accuracy of the proposed method. In the pilot actual application, the method was shown to be feasible in a real construction application.

In a well-controlled laboratory the optimal algorithms for bridge swivel construction are identified and in an actual project the proposed method is verified. The proposed CV method is complementary to the use of Electronic Total Station tools, motion sensors, and GPS for safety monitoring of swivel construction of bridges. It also contributes to being a possible approach without data-driven model training. Its principal advantages are that it both provides real-time monitoring and is easy to deploy in real construction applications.

This paper aims to provide top-level design and basic platform for intelligent application in China high-speed railway.

Based on the analysis for the future development trends of world railway, combined with the actual development needs in China high-speed railway, The definition and scientific connotation of intelligent high-speed railway (IHSR) are given at first, and then the system architecture of IHSR are outlined, including 1 basic platform, 3 business sectors, 10 business fields, and 18 innovative applications. At last, a basic platform with cloud edge integration for IHSR is designed.

The rationality, feasibility and implementability of the system architecture of IHSR have been verified on and applied to the Beijing–Zhangjiakou high-speed railway, providing important support for the construction and operation of the world’s first IHSR.

This paper systematically gives the definition and connotation of the IHSR and put forward the system architecture of IHSR for first time. It will play the most important role in the design, construction and operation of IHSR.

Wireless sensor networks (WSN), as a solution for buried water pipe monitoring, face a new set of challenges compared to traditional application for above-ground infrastructure monitoring. One of the main challenges for underground WSN deployment is the limited range (less than 3 m) at which reliable wireless underground communication can be achieved using radio signal propagation through the soil. To overcome this challenge, the purpose of this paper is to investigate a new approach for wireless underground communication using acoustic signal propagation along a buried water pipe.

An acoustic communication system was developed based on the requirements of low cost (tens of pounds at most), low power supply capacity (in the order of 1 W-h) and miniature (centimetre scale) size for a wireless communication node. The developed system was further tested along a buried steel pipe in poorly graded SAND and a buried medium density polyethylene (MDPE) pipe in well graded SAND.

With predicted acoustic attenuation of 1.3 dB/m and 2.1 dB/m along the buried steel and MDPE pipes, respectively, reliable acoustic communication is possible up to 17 m for the buried steel pipe and 11 m for the buried MDPE pipe.

Although an important first step, more research is needed to validate the acoustic communication system along a wider water distribution pipe network.

This paper shows the possibility of achieving reliable wireless underground communication along a buried water pipe (especially non-metallic material ones) using low-frequency acoustic propagation along the pipe wall.