An approach to produce a GIS database for road surface monitoring

Road Surface Monitoring (RSM) is the process of detecting the distress on paved or unpaved road surfaces. The primary aim of this process is to detect any distress (such as road surface cracks) at early stages in order to apply maintenance on time. Early detection of road cracks can assist maintenance before the repair costs becomes too high. Local authorities should have an effective and easy to use monitoring process in place across the road network to meet their obligations. The process of adding geographical identification metadata to the photos is called “Geo-tagging”. The proposed method in this work entails capturing GPS information when the photo is taken for the road surface distress, then attaching the photo to a map. The location disclosure in the act of geo-tagging of a photo provides qualities to the digital map. In that respect, a specific richness of the GIS dataset arises when they disclose the road surface distress photos. This paper proposes a system for establishing a GIS database consisting of geo-tagged photos for local authorities to automate the process of recording and reporting road surface distresses. This system is easy to use, cost-effective, deployable, and can be used effectively by local authorities

Implementation of UAV for Pavement Functional Performance Assesment

Pavement conditions could be degraded throughout its service life. Hence, a pavement management system is needed to ensure pavement performance according to its design life. To support a reliable pavement, a pavement condition survey needs to be conducted most effective and practical way. One of the technologies used in pavement condition surveys is the Unmanned Aerial Vehicle (UAV) or known as drone. The use of UAVs for road maintenance will reduce the cost and time and with the 3D model, the accuracy level is at the centimeter level which indicates UAVs are an excellent and promising tool for road work. In this study, a PCI method of pavement condition evaluation will be used to assess pavement through both manually surveyed and 3D model calculation with the use of Agisoft application. Later on, statistical test will be carried out such as ANOVA and correlation to determine the relationship between pavement condition obtained from two different survey method as well as the comparison in identifying pavement distress, PCI value and pavement condition. As a result, those two methods can identify identical types of damage, with a fairly high percentage of > 60%. While, manual method could identify a higher percentage of degree of severity than 3D model with the help of application. The manual of pavement condition assessment can identify in more detail than the Agisoft Metashape application. Based on statistical tests, the two methods used show a close and unidirectional relationship

How much does a man cost? A dirty, dull, and dangerous application

Thesis (M.A.) University of Alaska Fairbanks, 2017This study illuminates the many abilities of Unmanned Aerial Vehicles (UAVs). One area of importance includes the UAV's capability to assist in the development, implementation, and execution of crisis management. This research focuses on UAV uses in pre and post crisis planning and accomplishments. The accompaniment of unmanned vehicles with base teams can make crisis management plans more reliable for the general public and teams faced with tasks such as search and rescue and firefighting. In the fight for mass acceptance of UAV integration, knowledge and attitude inventories were collected and analyzed. Methodology includes mixed method research collected by interviews and questionnaires available to experts and ground teams in the UAV fields, mining industry, firefighting and police force career field, and general city planning crisis management members. This information was compiled to assist professionals in creation of general guidelines and recommendations for how to utilize UAVs in crisis management planning and implementation as well as integration of UAVs into the educational system. The results from this study show the benefits and disadvantages of strategically giving UAVs a role in the construction and implementation of crisis management plans and other areas of interest. The results also show that the general public is lacking information and education on the abilities of UAVs. This education gap shows a correlation with negative attitudes towards UAVs. Educational programs to teach the public benefits of UAV integration should be implemented

Pedestrian detection in far infrared images

This paper presents an experimental study on pedestrian classification and detection in far infrared (FIR) images. The study includes an in-depth evaluation of several combinations of features and classifiers, which include features previously used for daylight scenarios, as well as a new descriptor (HOPE - Histograms of Oriented Phase Energy), specifically targeted to infrared images, and a new adaptation of a latent variable SVM approach to FIR images. The presented results are validated on a new classification and detection dataset of FIR images collected in outdoor environments from a moving vehicle. The classification space contains 16152 pedestrians and 65440 background samples evenly selected from several sequences acquired at different temperatures and different illumination conditions. The detection dataset consist on 15224 images with ground truth information. The authors are making this dataset public for benchmarking new detectors in the area of intelligent vehicles and field robotics applications.This work was supported by the Spanish Government through the Cicyt projects FEDORA (GRANT TRA2010-20225-C03-01) and Driver Distraction Detector System (GRANT TRA2011-29454- C03- 02), and the Comunidad de Madrid through the project SEGVAUTO (S2009/DPI-1509)

A Sparsity-Inducing Optimization-Based Algorithm for Planar Patches Extraction from Noisy Point-Cloud Data

Currently, much of the manual labor needed to generate as-built Building Information Models (BIMs) of existing facilities is spent converting raw Point Cloud Datasets (PCDs) to BIMs descriptions. Automating the PCD conversion process can drastically reduce the cost of generating as-built BIMs. Due to the widespread existence of planar structures in civil infrastructures, detecting and extracting planar patches from raw PCDs is a fundamental step in the conversion pipeline from PCDs to BIMs. However, existing methods cannot effectively address both automatically detecting and extracting planar patches from infrastructure PCDs. The existing methods cannot resolve the problem due to the large scale and model complexity of civil infrastructure, or due to the requirements of extra constraints or known information. To address the problem, this paper presents a novel framework for automatically detecting and extracting planar patches from large-scale and noisy raw PCDs. The proposed method automatically detects planar structures, estimates the parametric plane models, and determines the boundaries of the planar patches. The first step recovers existing linear dependence relationships amongst points in the PCD by solving a group-sparsity inducing optimization problem. Next, a spectral clustering procedure based on the recovered linear dependence relationships segments the PCD. Then, for each segmented group, model parameters of the extracted planes are estimated via Singular Value Decomposition (SVD) and Maximum Likelihood Estimation Sample Consensus (MLESAC). Finally, the α-shape algorithm detects the boundaries of planar structures based on a projection of the data to the planar model. The proposed approach is evaluated comprehensively by experiments on two types of PCDs from real-world infrastructures, one captured directly by laser scanners and the other reconstructed from video using structure-from-motion techniques. In order to evaluate the performance comprehensively, five evaluation metrics are proposed which measure different aspects of performance. Experimental results reveal that the proposed method outperforms the existing methods, in the sense that the method automatically and accurately extracts planar patches from large-scaled raw PCDs without any extra constraints nor user assistance.This is the accepted manuscript. The final version is available from Wiley at http://onlinelibrary.wiley.com/doi/10.1111/mice.12063/abstract

Robot-assisted measurement in data-sparse regions

This work investigated the use of low-cost robots, small unmanned aerial vehicles (UAVs) and small unmanned surface vehicles (USVs), to assist researchers in environmental data collection in the Arkavathy River Basin in Karnataka, India. In the late 20th century, river flows in the Arkavathy began to decline severely, and Bangalore’s dependence on the basin for local water supply shifted while the causes of drying remain unknown. Due to the lack of available data for the region, it is difficult for water management agencies to address the issue of declining surface flows; by collecting critical hydrologic data accurately and efficiently through the use of robots, where data is not available or accessible, local water resources can more easily be managed for the greater Bangalore region. Three case study sites, including two irrigation tanks and one urban lake, within the Arkavathy basin were selected where unmanned aerial vehicles and unmanned surface vehicles collected data in the form of aerial imagery and bathymetric measurements. The data were further processed into 3D textured surface models and exported as digital elevations models (DEMs) for post-processing in GIS. From the DEMs, topographic and bathymetric maps were created and storage volumes and surface areas are calculated by relating water surface levels to tank bathymetry. The results are stage-storage and stage-surface area relationships for each case study site. These relationships provide valuable information relating to groundwater recharge and streamflow generation. Sensitivity analysis showed that the topographic surface data used in the stage-storage and stage-surface area curves was validated within ± 0.35 meters. By providing these relationships and curves, researchers can further understand hydrologic processes in the Arkavathy River Basin and inform local water management policies. From these case studies, three formative observations were made, relating to i) interpretation of the data fusion process using information collected from both UAV and USV systems; ii) observations for the human-robot interactions for USV and; iii) field observations for deployment and retrieval in water environments with low accessibility. This work is of interest to hydrologists and geoscientists who can use this methodology to assist in data collection and enhance their understanding of environmental processes

Quantifying road roughness: multiresolution and near real-time analysis

Road roughness is a key parameter for road construction and for assessing ride quality during the life of paved and unpaved road systems. The quarter-car model (QC model), is a standard mathematical tool for estimating suspension responses and can be used for summative or pointwise analysis of vehicle response to road geometry. In fact, transportation agencies specify roughness requirements as summative values for pavement projects that affect construction practices and contractor pay factors. The International Roughness Index (IRI), a summative statistic of quarter-car suspension response, is widely used to characterize overall roughness profiles of pavement stretches but does not provide sufficient detail about the frequency or spatial distribution of roughness features. This research focuses on two pointwise approaches, continuous roughness maps and wavelets analysis, that both characterize overall roughness and identify localized features and compares these findings with IRI results. Automated algorithms were developed to preform finite difference analysis of point cloud data collected by three-dimensional (3D) stationary terrestrial laser scans of paved and unpaved roads. This resulted in continuous roughness maps that characterized both spatial roughness and localized features. However, to address the computational limitations of finite difference analysis, Fourier and wavelets (discrete and continuous wavelet transform) analyses were conducted on sample profiles from the federal highway administration (FHWA) Long Term Pavement Performance data base. The Fourier analysis was performed by transforming profiles into frequency domain and applying the QC filter to the transformed profile. The filtered profiles are transformed back to spatial domain to inspect the location of high amplitudes in the suspension rate profiles. Finite difference analysis provides suspension responses in spatial domain, on the other hand Fourier analysis can be performed in either frequency or spatial domains only. To describe the location and frequency content of localized features in a profile, wavelet filters were customized to separate the suspension response profiles into sub profiles with known frequency bands. Other advantages of wavelets analysis includes data compression, making inferences from compressed data, and analyzing short profiles (\u3c 7.6 m). The proposed approaches present the basis for developing real-time autonomous algorithms for smoothness based quality control and maintenance

Evaluating applications of the unmanned aerial system in construction project management

Using unmanned aerial vehicle systems (UAS) or drones in project management (PM) is a novel methodology aimed at enhancing the performance of the PM system. This technology is still in its infancy, and some serious progress is required to cover and advance in this field. UAS is used in various applications ranging from site mapping, surveying, traffic surveillance, bushfire monitoring and aerial photography. Despite the multiple functions offered by UAS, which are well covered in various sources, industry practitioners still have little confidence and knowledge on this technology. The value of the data collected using UAS technology is still poorly utilised and understood. This project aims to explore areas in PM that can be enhanced while using UAS and understand the added value of adopting this new technology. This research will utilise Unmanned Aerial Vehicle (UAV) with high- definition (HD) cameras to collect real time imageries of construction sites. The collected data, with the aid of a photogrammetric software Pix4D, is used to develop a detailed UAS system to determine the accuracy of performed work, the generation of the corresponding progress payment reports, and referencing and tracking information in real time for a residential project. This study also discusses combining the UAS and 5D Building Information Modelling (BIM) data to develop smart construction sites. The UAS–BIM combination enables the project stakeholders to be fully informed of the work’s progress and quality to prevent mistakes that could lead to additional costs and delays. The paper identified the primary obstacles to applying the UAS via interviews with the project managers and tradespersons involved in the selected project. Assuredly, digital culture is essential for an intelligent construction site to shift the project team from a passive data user to a more proactive analyser to improve performance and site safety. This research is aimed at building a holistic digital system which will be applied and utilised in Construction Project Management (CPM) fields to improve the performance of site management and the quality of work performed. Other obstacles include ethical reservations, legal requirements, liability risks, weather conditions and the continuation of using a UAS in non-open-air construction environments

Smart Compaction for Infrastructure Materials

69A3551847103Compaction is a process of rearranging material particles by various mechanical loadings to densify the materials and form a stable pavement structure. Current methods to assess the compaction quality rely heavily on engineers' experiences or post-compaction methods at selected spots. The experience-based method is prone to cause compaction problems and pavement distresses, particularly when new materials are implemented. Due to the complicated interactions between the compactors and materials, the compaction mechanism of the particulate materials is still unclear. This gap hinders the improvement of compaction quality and the development of intelligent construction. This project was undertaken to investigate the compaction mechanism of the infrastructure material from the mesoscale (particle scale) and develop an innovative compaction monitoring method that determines the compaction condition based on particle kinematics. With the development of sensing technologies, wireless particle-size sensors have become available in research and industry for monitoring particle behaviors during compaction. A wireless sensor, SmartRock, was applied in the project and collected the mesoscale behaviors during compaction. Several lab and field compaction projects were carried out using asphalt mixtures and granular materials, various compaction machines, and pavement structures. It was found that internal particle kinematic behavior is closely correlated to material densification during compaction. The lab and field compaction can be reasonably connected by the particle rotation, and similar three-stage compaction patterns were identified. Three machine learning models were built to predict the compaction condition and the density of the asphalt pavement both in the lab and in the field. The reasonable predictions confirm that the machine learning algorithm is appropriate for compaction prediction. The density results from the pavement cores further verify the applicability and robustness of the intelligent model for compaction prediction. Future studies are still needed to evaluate the model's robustness based on more mixture varieties and field applications