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The digitalisation, automation and robotisation of road inspection and maintenance technologies make it possible to collect bigger volumes of data and additional types of information about road infrastructure. Methodologies and tools to support road asset management decision-making are needed to exploit this new information, progressing towards predictive maintenance and improving different aspects of road asset management. This study presents a Digital Twin-based Decision Support Tool to assist road operators in road inspection, maintenance and upgrade. The goal of the paper is twofold. First, the architecture of the Digital Twin-based Decision Support Tool is presented, describing the main components and functionalities. The system is based on a Digital Twin (DT) that mirrors real road assets to integrate different sources of data and support the processing of low-level data into high-level information. The decision support tool (DST) is able to analyse the collected information and compute the road pavement condition to derive optimal intervention plans, addressing road section conditions, human and technical resources and other external constraints. Second, the application of the proposed architecture to road pavement maintenance is described, considering the Italian highway A24 and its connections with Rome´s ring road, managed by Strada dei Parchi SpA. Road pavement data, such as the International Roughness Index (IRI) and the Sideway Force Coefficient (SFC), are integrated into the DT to be analysed through Artificial Intelligence-clustering techniques to perform the sectioning and clustering of road sections according to their status and quality index. The paper shows the benefits derived from the integration of DT technologies with DSTs for improving processes of road maintenance.

Information and automation technologies play a pivotal role in achieving cyber-physical integration within Construction 4.0. In this transformed landscape, the evolution of the construction management paradigm carefully considers the enhancement of business models and organizational structures to prioritize stakeholders’ well-being, environmental sustainability, and heightened resilience. A significant challenge lies in effectively managing and coordinating a myriad of multi-source and heterogeneous entities using information and automation technologies. The key obstacle is synchronizing these elements based on cyber-physical interoperation to optimize multiple objectives seamlessly. Hence synchronization emerges as a crucial factor for orchestrating and sustaining harmonious relationships among multiple entities or activities within a delimited spatial-temporal framework. This ensures seamless and aligned coordination throughout dynamic processes. Therefore, this paper presents a strategic roadmap for the synchronized construction management, derived from a thorough analysis of fundamental elements in Construction 4.0, aimed at advancing the current construction management practices. Moreover, to articulate this synchronization approach systematically, an Orthogonally Synchronized Digital Twin (SDT) model with regular expression is formulated, built upon the proposed roadmap for reshaped construction management. This study provides valuable insights for stakeholders in the construction industry, including architects, engineers, project managers, and policymakers. The findings guide decision-making on digital twin adoption in construction, supporting practitioners to enhance efficiency and improve outcomes, offering a roadmap for industry advancement towards human-centrality, sustainability, and resilience. Future research should focus on validating the proposed roadmap and SDT model in real-world scenarios, exploring synergies between AI and digital twins, and investigating advanced technologies for holistic smart cities management.

Road networks often carry more than 80% of a country's total passenger-km and over 50% of its freight ton-km according to the World Bank. Efficient maintenance of road networks is highly important. Road asset management, which is essential for maintenance programs, consist of monitoring, assessing and decision making necessary for maintenance, repair and/or replacement. This process is highly dependent on adequate and timely pavement condition data. Current practice for collecting and analysing such data is 99% manual. To optimize this process, research has been performed towards automation. Several methods to automatically detect road assets and pavement conditions are proposed. In this paper, we present an analysis of the current state of practice of road asset monitoring, a discussion of the limitations, and a qualitative evaluation of the proposed automation methods found in the literature. The objective of this paper is to understand the issues associated with current processes, and assess the available tools to address these problems. The current state of practice is categorized into: 1) type of data collected; 2) type of asset covered and 3) amount of information provided. The categories are evaluated in terms of a) accuracy; b) applicability (efficiency); c) cost; and d) overall improvement to current practice. Despite the methods available, the outcome of the study indicates that current condition monitoring lacks efficiency, and none provide a holistic solution to the problem of road asset condition monitoring.

A bridge or tunnel strike is an incident in which a vehicle that is taller than the clearance underneath the structure (over-height), typically a lorry or double-decker bus, collides with the structure causing damage. This can lead to injuries, fatalities and/or, in worst case scenario, train derailments. Bridge and tunnel strikes are costly and expensive. The annual maintenance costs to repair and service the structure have been reported to range in the tens-to-hundreds of thousands (£) while the average cost per strike ranges between £5,000 to £25,000. In this paper, we present a comprehensive synthesis of the nature and scope of the problem of bridge and tunnel strikes, followed by the current state of practice and current state of research. Bridge and tunnel strikes still occur with high frequency, and prevention systems (passive, sacrificial and active) available on the market are often too expensive. Bridge-owners are seeking an affordable yet reliable system that is cheap enough for widespread installation without compromising the accuracy and performance of such a system.

Vision-based traffic surveillance systems are among the most reliable, inexpensive, and highly applicable methodologies for surveying traffic conditions. The implementation of these strategies, however, is limited under certain conditions, such as the presence of vehicle occlusions or poor illumination conditions that lead to either overcounted or undercounted traffic data. The proposed motion-based methodology is intended to overcome these limitations by using a new technique for full-body occlusion handling of vehicles. The methodology is based on five main steps: calibration, detection, tracking, counting, and occlusion handling. The proposed methodology was tested with various 30-min videos and 452 preidentified cases of occlusion. Preliminary results indicated that the proposed methodology was reliable and robust in providing traffic density analysis. Future work may rely on the extension of the proposed methodology to deal with the detection of vehicles moving toward multiple directions.

Vision-based traffic surveillance systems are among the most reliable, inexpensive, and highly applicable methodologies for surveying traffic conditions. The implementation of these strategies, however, is limited under certain conditions, such as the presence of vehicle occlusions or poor illumination conditions that lead to either overcounted or under-counted traffic data. The proposed motion-based methodology is intended to overcome these limitations by using a new technique for full-body occlusion handling of vehicles. The methodology is based on five main steps: calibration, detection, tracking, counting, and occlusion handling. The proposed methodology was tested with various 30–min videos and 452 preidentified cases of occlusion. Preliminary results indicated that the proposed methodology was reliable and robust in providing traffic density analysis. Future work may rely on the extension of the proposed methodology to deal with the detection of vehicles moving toward multiple directions.

Current procedures in post-earthquake safety and structural assessment are performed by a skilled triage team of structural engineers/certified inspectors. These procedures, in particular the physical measurement of the damage properties, are time-consuming and qualitative in nature. Spalling has been accepted as an important indicator of significant damage to structural elements during an earthquake, and thus provides a sound springboard for a model in machine vision automated assessment procedures as is proposed in this research. Thus, a novel method that automatically detects regions of spalling on reinforced concrete columns and measures their properties in image data is the specific focus of this work. According to this method, the region of spalling is first isolated by way of a local entropy-based thresholding algorithm. Following this, the properties of the spalled region are depicted by way of classification of the extent of spalling on the column. The region of spalling is sorted into one of three categories by way of a novel global entropy-based adaptive thresholding algorithm in conjunction with well-established image processing methods in template matching and morphological operations. These three categories are specified as the following: (1) No spalling; (2) Spalling of cover concrete; and (3) Spalling of the core concrete (exposing reinforcement). In addition, the extent of the spalling along the length of the column is quantified. The method was tested on a database of damaged RC column images collected after the 2010 Haiti Earthquake, and comparison of the results with manual measurements indicate the validity of the method.

Three-dimensional (3-D) spatial data of a transportation infrastructure contain useful information for civil engineering applications, including as-built documentation, on-site safety enhancements, and progress monitoring. Several techniques have been developed for acquiring 3-D point coordinates of infrastructure, such as laser scanning. Although the method yields accurate results, the high device costs and human effort required render the process infeasible for generic applications in the construction industry. A quick and reliable approach, which is based on the principles of stereo vision, is proposed for generating a depth map of an infrastructure. Initially, two images are captured by two similar stereo cameras at the scene of the infrastructure. A Harris feature detector is used to extract feature points from the first view, and an innovative adaptive window-matching technique is used to compute feature point correspondences in the second view. A robust algorithm computes the nonfeature point correspondences. Thus, the correspondences of all the points in the scene are obtained. After all correspondences have been obtained, the geometric principles of stereo vision are used to generate a dense depth map of the scene. The proposed algorithm has been tested on several data sets, and results illustrate its potential for stereo correspondence and depth map generation.

Three-dimensional (3-D) spatial data of a transportation infrastructure contain useful information for civil engineering applications, including as-built documentation, on-site safety enhancements, and progress monitoring. Several techniques have been developed for acquiring 3-D point coordinates of infrastructure, such as laser scanning. Although the method yields accurate results, the high device costs and human effort required render the process infeasible for generic applications in the construction industry. A quick and reliable approach, which is based on the principles of stereo vision, is proposed for generating a depth map of an infrastructure. Initially, two images are captured by two similar stereo cameras at the scene of the infrastructure. A Harris feature detector is used to extract feature points from the first view, and an innovative adaptive window-matching technique is used to compute feature point correspondences in the second view. A robust algorithm computes the nonfeature point correspondences. Thus, the correspondences of all the points in the scene are obtained. After all correspondences have been obtained, the geometric principles of stereo vision are used to generate a dense depth map of the scene. The proposed algorithm has been tested on several data sets, and results illustrate its potential for stereo correspondence and depth map generation.

Adoption of digital information tools in the construction sector provides fertile ground for the birth and growth of companies that specialize in applications of technologies to design and construction. While some of the technologies are new, many implement ideas proposed in construction research decades ago that were impractical without a sound digital building information foundation. Building Information Modelling (BIM) itself can be traced to a landmark paper from 1975; ideas for artificially intelligent design and code checking tools date from the mid-1980s; and construction robots have laboured in research labs for decades. Yet only within the past five years has venture capital actively sought startup companies in the ‘Construction Tech’ sector. Following a set of digital construction innovations through their known past and their uncertain present, we review their increasingly optimistic future, all through the lens of their dependence on digital information. The review identifies new challenges, yielding a set of research topics with the potential to unlock a range of future applications that apply artificial intelligence.