In recent years the use of UAVs (Unmanned Aerial Vehicles), as known as drones, has increased exponentially for infrastructure monitoring, usually using remote sensing payloads. The drop in prices of these systems, the improvements in their specifications and the change in the regulations for their use have made more and more people use them for both recreational and professional uses. In some hard-accessible structures, such as bridges or dams, these vehicles are a powerful tool to carry out different types of inspections using remote sensors, such as different types of camaras, LiDAR sensors or RADAR sensors. The data acquired by these vehicles can be used by SHM (Structural Health Monitoring) methods, to acquire the 3D geometric model of the structure to be used by a DT (digital twin) or to detect different pathologies, such as cracks. Also, new UAV systems have been developed in the last years to perform a physical contact between the UAV and the structure, enabling the use of these systems to perform other NDT (Non-Destructive Testing) inspections that use sensors that have to be in contact with the structure to perform reliable measurements, such as ultrasonic sensors. In this work, four different intelligent payloads for contact inspection tasks with UAVs are going to be presented. The first three payloads are focused on maintaining continuous contact between the UAV and the structure while measurements are performed by the contact sensor. Instead, the fourth has been designed to fix the payload to the structure, in this way the UAV only fixes it to the structure without maintaining continuous contact while the measurements are performed. The results of each payload are going to be compared and analysed, defining possible improvements and future work.

Funding

This study was funded by the Recovery, Transformation, and Resilience Plan of the European Union–Next Generation EU (University of Vigo grant ref. 585507).

References

González-deSantos, L.M.; Martínez-Sánchez, J.; González-Jorge, H.; Ribeiro, M.; de Sousa, J.B.; Arias, P. Payload for Contact Inspection Tasks with UAV Systems. Sensors 2019, 19, 3752, doi:10.3390/s19173752.

González-deSantos, L.M.; Martínez-Sánchez, J.; González-Jorge, H.; Navarro-Medina, F.; Arias, P. UAV payload with collision mitigation for contact inspection. Autom. Constr. 2020, doi:10.1016/j.autcon.2020.103200.

González-deSantos, L.M.; Martínez-Sánchez, J.; González-Jorge, H.; Arias, P. Active UAV payload based on horizontal propellers for contact inspections tasks. Meas. J. Int. Meas. Confed. 2020, doi:10.1016/j.measurement.2020.108106.

Alkali-activated materials (AAM, also called geopolymer) are considered as excellent alternative binder to replace Portland cement in concrete because AAM is cement clinker free binder made of industrial by-products or treated and cleaned wastes containing minerals via alkali-activation technology. AAMs have been studied intensively in the past decades. However, industrial-scale manufacture and engineering structures applications of this type of material remain rare. The main challenges concerning scientific and technical aspects are: 1) Qualities and chemical compositions of raw materials largely depend on the adopted processing technique and there are considerable regional differences even amongst the same kinds of materials, like fly ash. These situations largely affect the chemical activity of raw materials and have significant influence on reaction conditions and kinetics, which consequently leads to considerable changes of the generated microstructure and entirely different behavior and performance of the material after hardening. 2) Some uncertainties regarding the long-term performances and degradation mechanisms of geopolymer systems are missing. This primary issue needs to be addressed in order to build acceptance and confidence required for the use of AAMs in industrial scale applications. 3) Studies have shown that AAM concrete has different time depended properties (i.e., higher shrinkage and creep) compared to ordinary Portland cement concrete. This implies that when AAM concrete is used as structural elements in construction where it is restrained externally or internally, the shrinkage of geopolymer concrete will develop a tensile stress, which might cause cracking when beyond the tensile strength of the concrete.

This presentation will review recent researches in these aspects and introduce some projects from materials studies to structural applications.

Ground-penetrating radar (GPR) technology has been widely applied in ground subsurface investigations. The major development of GPR for pavement assessment originated in the early 1980s and since has become a well-established investigation technique for pavements. Analysis of GPR data provides much richer information on layer depths of pavement structure, material conditions, moisture content, voids, and locations of reinforcement and other features. Being able to accurately and reliably assess the underlying conditions of pavements is essential to fully understand both functional and structural deficiencies or failures of pavements and associated causality. This improved understanding will lead to the most cost-effective maintenance and rehabilitation treatments and considerable savings in maintenance and rehabilitation expenditure. The overall goal of this research is to extend the GPR technology in combination with modern data analytics to provide improved pavement investigation capabilities. As a result, new methodologies and analytical procedures were developed to acquire and analyze field GPR data, and infer subgrade density, which is critical for diagnosis of pavement failure and underlying causality [1, 2]. Based on the outcomes of the research, the potential pavement foundation issues of critical state highways in Georgia, USA are presented.

Funding: The work presented in this paper is part of a research project (RP 19-21) sponsored by the Georgia Department of Transportation (GDOT). The contents of this paper reflect the views of the authors, who are solely responsible for the facts and accuracy of the data, opinions, and conclusions presented herein. The contents may not reflect the views of the funding agency or other individuals.

Conflicts of Interest: The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

References

1. Abdelmawla, A.M., Durham, S.A. and Kim, S. Estimation of Subgrade Soil Density Using Ground Penetrating Radar. In Proceedings of 2019 ICSC Conference, Seoul, Korea, July 2019

Abdelmawla, A. and S. Kim. Application of Ground Penetrating Radar to Estimate Subgrade Soil Density, Infrastructures, 5 (2), 12, 2020; https://doi.org/10.3390/infrastructures5020012

1. INTRODUCTION

Transportation agencies automatically collect and analyze pavement cracking data using agency-owned equipment and software or contracted services. The pavement cracking data are then used to determine the most appropriate maintenance and rehabilitation strategies to provide a safe and reliable roadway [1]. However, it requires a high-cost equipment or services [2].

A digital image processing algorithm was developed to compute a unified crack index and crack type index [3,4]. A robust position invariant neural network was developed for digital pavement crack analysis [5]. The accuracy of automated pavement surface image analysis system has been evaluated against the ground-truth cracking data [6]. Image-based data collection procedure was then evaluated against the AASHTO provisional standard for cracking on asphalt-surfaced pavements [7].

Currently, ten state DOT’s are using drones for bridge inspection and six state DOT’s for pavement inspection [8]. Recently, there are increased interests on automatically analyze drone images from integrators/service providers and end-users [9]. This paper presents a low-cost pavement distress data collection using a drone and subsequent drone image analysis using pavement crack analysis software.

This paper discusses the state-of-the-art drone imaging technologies and advanced image analysis algorithms adopting advanced machine learning software tools. Drones were used to capture pavement surface images, which were analyzed using the crack image analysis software. This paper is timely given the increased new development in drone imaging technologies.

1. METHODOLOGY

Drone images were collected and a machine learning algorithm was developed for road segmentation and crack detection.

1) Data Set Preparation

Drone images of pavements were collected using a drone, which were then used for training for developing a machine learning algorithm. A second set of drone images were collected for validation of the developed machine learning algorithm.

2) Pavement Extraction from Drone Images

Drone images cover wide range of earth surface and the first task is to extract pixels, which belong to pavements. To extract pavement pixels from drone images, a semantic segmentation method was used to develop a convolutional network architecture designed to accomplish the this first task.

3) Crack Detection

For a given crack image, a proposed machine learning algorithm was developed to yield a crack detection scheme, where crack regions have higher probability and non-crack regions have lower probability. Figure. 1 shows an example drone image acquisition and analysis result.

(a) Importing a drone image of pavement surface

(b) Automatically analyzing a drone image

Figure 1: Importing and Analyzing a Drone image

1. SUMMARY AND CONCLUSIONS

Increasing number of public agencies and companies are using drones for pavement inspection. Images can be automatically captured by a drone and stored in a point cloud for 3-D modeling. A DJI drone was be used to capture pavement surface images in a high resolution at a low cost. Software was developed to analyze drone image images and analysis results can be integrated with GIS software. In the future, LiDAR camera can be mounted on a drone to measure a depth of cracks.

The template details the sections that can be used in a manuscript. Note that each section has a corresponding style, which can be found in the “Styles” menu of Word. Sections that are not mandatory are listed as such. The section titles given are for articles. Review papers and other article types have a more flexible structure.

1. Improving the sustainability of pavements requires action across all stages of the full life cycle of the pavement:

• Materials extraction
• Materials processing
• Materials transportation
• Construction
• Use
• End of Life

Proposed solutions that do not look at the complete life cycle of the pavement, and do not consider the full system (all interactions of the pavement with other systems in each stage) may result in less than optimal positive outcomes and create the risk of negative unintended consequences. Negative unintended consequences means that the proposed solution make actually go backwards from sustainability goals.

The focus of most efforts in pavement have focused on materials, which is only one part, albeit an important one, of the steps in the project delivery process where changes can be made to improve sustainability. Proposed solutions must be found in every stage of infrastructure delivery:

• Planning ( if new)
• Pavement management to select project (if PMR&R)
• Conceptual design (Scoping)
• Design (PS&E)
• Construction
• Monitor performance

Finally, new approaches for improving pavement sustainability do not change anything until they are completely implemented, meaning that the change is now embedded in policies, specifications, guidance, tools, and is part of every practitioner’s everyday practice. The steps of moving from an idea to complete implementation are:

• Conceptual idea
  • Feasibility analysis using life cycle assessment (LCA) and life cycle cost analysis (LCCA) quantify expected outcomes and cost/benefit, and other assessment of the proposed change to assess which ideas are most promising to move forward
• Research
  • Reassessment as the idea is developed using LCA and LCCA to better calculate its potential for beneficial outcomes and the cost per unit of beneficial outcome
• Development
  • Creating the databases, validated models, tools, policies, specifications, and training
• Implementation
  • Getting approval for implementation, making the changes in all information that is part of the project delivery process, training all users; and supporting users in their daily practice
• Feedback
  • This process above should have feedback for continuous improvement, and new concepts should be developed as the current ones are being implemented

2. Research and development are advancing pavement structural and materials design technologies, and methods for modeling performance and cost and environmental impacts. However, many of these advances are not well integrated when implemented and advances in part of the pavement project delivery process and network management system may not be recognized or considered in other parts. This presentation summarizes the overall vision and milestones completed to date for creating and implementing an integrated systems approach and continuous improvement process for the pavement enterprise in California, including structural design, materials specifications, construction specifications, network pavement asset management, life cycle cost analysis, environmental life cycle assessment, and prioritization of policies for achieving state-wide environmental goals.

In the latest years, Building Information Modeling (BIM) tools have increased the productivity of infrastructure projects through more efficient information management and by fostering communication between different actors of the process. At the same time, the growing need to introduce sustainability indicators calculated through the life cycle assessment (LCA) methodology has prompted an increase in the amount of data to be managed throughout the life cycle of an infrastructure project. The present work consists of developing a BIM-based LCA tool aimed at the calculation of several environmental indicators through the informative content of a road pavement BIM; the tool is specifically designed to avoid errors in LCA calculations during the early design stages, reduce the engineer’s effort through automation and support sustainable decision-making in the infrastructure domain. A LCA-based pavement information model was developed by defining and adding several customized property sets, respectively containing the specific road pavement materials‘ features and some selected environmental impact categories; a bidirectional information exchange path was established between BIM and the LCA-tool to automate the LCA calculations and dynamically update the mentioned environmental indicators property sets whenever the geometry of the pavement and the asphalt materials’ features change. The developed tool allowed to practically integrate pavement-related environmental sustainability requirements into BIM projects, with specific reference to asphalt pavement solutions that apply circular economy principles (i.e. secondary raw materials and cold recycling technologies), in light of more environmentally-friendly pavement construction practices.

References

1. Safari, K.; AzariJafari, H. Challenges and opportunities for integrating BIM and LCA: methodological choices and framework development. Sustain Cities Soc, 2021, 102728.
2. Oreto, C.; Massotti, L.; Biancardo, S.A.; Veropalumbo, R.; Viscione, N.; Russo, F. BIM-Based Pavement Management Tool for Scheduling Urban Road Maintenance. Infrastructures, 2021, 6(11), 148.
3. Oreto, C; Biancardo, S.A.; Veropalumbo, R.; Russo, F. Road Pavement Information Modeling through Maintenance Scenario Evaluation. J Adv Transport, 2021, 823117.
4. Bosurgi, G.; Pellegrino,; Sollazzo, G. Pavement condition information modelling in an I-BIM environment. Int J Pavement Eng, 2021.
5. Van Eldik, M.A.; Vahdatikhaki, F.; Dos Santos, J.M.O.; Visser, M.; Doree, A. BIM-based environmental impact assessment for infrastructure design projects. Automat Constr, 2020, 120, 103379.
6. Tang, F.; Ma, T.; Guan, Y.; Zhang, Z. Parametric modeling and structure verification of asphalt pavement based on BIM-ABAQUS. Automat Constr, 2020, 111, 103066.

On 14 August 2018, 43 people died in the collapse of the Polcevera bridge in Italy. Beyond the human tragedy, this event reminded us all of the degrading state of critical transport infrastructure in the EU. Few months after the tragedy, Regione Lombardia signed a collaboration agreement with Politecnico di Milano to develop criteria for the informed management and planning of interventions, aimed to keep the regional asset at the required performance level. One of the outcomes of the project are the regional monitoring guidelines and their application to nine pilot bridges. The document provides guidance for the design of monitoring systems as decision support tools for problems relevant to maintenance and emergency management, occasional safety assessment, and standardization. In the MoRe regional guidelines the design process of a monitoring system is approached as a stepwise procedure that originates from the needs of the decision-maker and comprises: a) preliminary investigation to acquire knowledge about the specific bridge and the deterioration process to monitor; b) identification of the indicators able to provide information about the structural performance (deterioration processes, actions, and environmental conditions); c) the selection of the technical devices to manage (acquire, process, transmit and store) the monitoring information. During the lecture, the content of the guidelines and the nine pilot monitoring systems installed as demonstrators, will be illustrated.