Automatic Crack Detection in Built Infrastructure Using Unmanned Aerial Vehicles

This paper addresses the problem of crack detection which is essential for health monitoring of built infrastructure. Our approach includes two stages, data collection using unmanned aerial vehicles (UAVs) and crack detection using histogram analysis. For the data collection, a 3D model of the structure is first created by using laser scanners. Based on the model, geometric properties are extracted to generate way points necessary for navigating the UAV to take images of the structure. Then, our next step is to stick together those obtained images from the overlapped field of view. The resulting image is then clustered by histogram analysis and peak detection. Potential cracks are finally identified by using locally adaptive thresholds. The whole process is automatically carried out so that the inspection time is significantly improved while safety hazards can be minimised. A prototypical system has been developed for evaluation and experimental results are included.Comment: In proceeding of The 34th International Symposium on Automation and Robotics in Construction (ISARC), pp. 823-829, Taipei, Taiwan, 201

Pengelompokan Bidang Keilmuan Di Teknologi Informasi Dengan Metode K-Means Dan Optimasi Simple Additive Weighting (Saw) Dalam Penentuan Kesesuain Terhadapa Keilmuan

POLNES JURTI terdiri dari tiga prodi, terdapat 9 Bidang kompetensi keilmuan yaitu, Mobile Computing, Computer Controlled Infrastructure, Computer Vision, Robotic & Artificial Intelligent, Advanced Applied Computer, Human Computer Interaction, Intelligent Computing, Cloud Computing, Multimedia. Pada masing-masing prodi memiliki area kompetensi. Tujuan penelitian mengarahkan ke bidang kompetensi keilmuan yang lebih sesuai, algortima K-means merupakan sebuah algoritma yang mengkelompokan data berdasarkan jarak terdekat dari suatu cluster, MAPE digunakan sebagai perhitungan error pada masing-masing cluster dari perhitungan, SAW adalah metode penjumlahan terbobot, SAW pada penelitian ini dilakukan untuk menentukan responden yang paling sesuai dengan hasil cluster pada K-means. hasil perhitungan K-means tidak bisa menentukan cluster sesuai dengan masing-masing prodi. Karna K-means sendiri menghitung berdasarkan hasil dari nilai Res, dan membandingkan nilai tersebut pada masing-masing cluster, hasil perhitungan error dengan menggunakan MAPE, perhitungan error menunjukkan bahwa cluster pada K-means sangat akurat amat akurat dalam pembagian cluster berdasarakan hasil perhitungan dari kuisioner, Hasil dari perhitungan SAW menujukan bahwa ada nilai yang sama pada salah satu responden mengakibatkan rangking pada suatu cluster menjadi sama, seperti pada cluster CCI. Pada rangking 3 dan 7 masingmasing mempunya dua responden, Hal tersebut membuat perhitungan SAW pada cluster menjadi kurang optimal, karna dalam satu rangking terdapat dua responden

Enhanced discrete particle swarm optimization path planning for UAV vision-based surface inspection

© 2017 In built infrastructure monitoring, an efficient path planning algorithm is essential for robotic inspection of large surfaces using computer vision. In this work, we first formulate the inspection path planning problem as an extended travelling salesman problem (TSP) in which both the coverage and obstacle avoidance were taken into account. An enhanced discrete particle swarm optimization (DPSO) algorithm is then proposed to solve the TSP, with performance improvement by using deterministic initialization, random mutation, and edge exchange. Finally, we take advantage of parallel computing to implement the DPSO in a GPU-based framework so that the computation time can be significantly reduced while keeping the hardware requirement unchanged. To show the effectiveness of the proposed algorithm, experimental results are included for datasets obtained from UAV inspection of an office building and a bridge

Building Information Modelling, Artificial Intelligence and Construction Tech

Development and 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 themselves new, the majority are based on ideas that have proliferated in construction research for decades but could not be implemented 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 it is only within the past five years that venture capital has actively sought startup companies in the ‘Construction Tech’ sector. We follow a set of digital construction innovations through their known past and their uncertain present, and 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 make extensive use of artificial intelligence.Centre for Digital Built Britai

Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden

With cities reinforcing greener ways of urban mobility, encouraging urban cycling helps to reduce the number of motorized vehicles on the streets. However, that also leads to a significant increase in the number of bicycles in urban areas, making the question of planning the cycling infrastructure an important topic. In this paper, we introduce a new method for analyzing the demand for bicycle parking facilities in urban areas based on object detection of social media images. We use a subset of the YFCC100m dataset, a collection of posts from the social media platform Flickr, and utilize a state-of-the-art object detection algorithm to detect and classify moving and parked bicycles in the city of Dresden, Germany. We were able to retrieve the vast majority of bicycles while generating few false positives and classify them as either moving or stationary. We then conducted a case study in which we compare areas with a high density of parked bicycles with the number of currently available parking spots in the same areas and identify potential locations where new bicycle parking facilities can be introduced. With the results of the case study, we show that our approach is a useful additional data source for urban bicycle infrastructure planning because it provides information that is otherwise hard to obtain

Earthquake Engineering Research Institute 2021 Undergraduate Seismic Design Competition

Since 2004, the Earthquake Engineering Research Institute (EERI) has hosted the undergraduate Seismic Design Competition to promote the study of earthquake engineering. This year, a team of students from the California Polytechnic State University, San Luis Obispo competed against 36 other colleges and universities from across the world in the 19th annual competition, virtual for the first time due to the COVID-19 pandemic. This report summarizes and expands on the material prepared by the 2021 team to guide the exploration of the implementation of an addition to an existing hospital that needs retrofitting. This includes the potential design sequence that could be implemented to complete such a project in the real world from research to analysis and design. Furthermore, this report highlights the depth of interdisciplinary subjects that this competition demands of participating teams and hopes to spark interest in other undergraduate students to participate in the competition. Design Prompt: The mayor of Seattle, WA is making a plea to acquire urgent funds to increase hospital space to keep up with the healthcare demand arising from the COVID-19 pandemic. Since there is a pressing need for space, an existing hospital structure in the Greater Seattle Area was chosen to expand with a proposed vertical extension that would increase patient capacity, with possibility of a seismic retrofit based on a performance assessment

CPVC-könyökidomok optikai ellenőrzése és a gyártási folyamat hibadiagnosztikája

A tanulmány során CPVC-anyagú könyökidomok optikai ellenőrzése valósult meg 2 dimenzióban detektálható geometriai paraméterekre vonatkozóan. Az eredmények kiértékelése alapján hibadiagnosztika került felállításra a gyártósorra vonatkozóan statisztikai számítások révén. Az optikai ellenőrzés a National Instruments Vision Development Module fejlesztőkörnyezetben valósult meg, az adatok kiértékelése pedig a Microsoft Excel használatával történt

Heuristics for optimizing 3D mapping missions over swarm-powered ad hoc clouds

Drones have been getting more and more popular in many economy sectors. Both scientific and industrial communities aim at making the impact of drones even more disruptive by empowering collaborative autonomous behaviors -- also known as swarming behaviors -- within fleets of multiple drones. In swarming-powered 3D mapping missions, unmanned aerial vehicles typically collect the aerial pictures of the target area whereas the 3D reconstruction process is performed in a centralized manner. However, such approaches do not leverage computational and storage resources from the swarm members.We address the optimization of a swarm-powered distributed 3D mapping mission for a real-life humanitarian emergency response application through the exploitation of a swarm-powered ad hoc cloud. Producing the relevant 3D maps in a timely manner, even when the cloud connectivity is not available, is crucial to increase the chances of success of the operation. In this work, we present a mathematical programming heuristic based on decomposition and a variable neighborhood search heuristic to minimize the completion time of the 3D reconstruction process necessary in such missions. Our computational results reveal that the proposed heuristics either quickly reach optimality or improve the best known solutions for almost all tested realistic instances comprising up to 1000 images and fifteen drones

Deep learning in the construction industry: A review of present status and future innovations

The construction industry is known to be overwhelmed with resource planning, risk management and logistic challenges which often result in design defects, project delivery delays, cost overruns and contractual disputes. These challenges have instigated research in the application of advanced machine learning algorithms such as deep learning to help with diagnostic and prescriptive analysis of causes and preventive measures. However, the publicity created by tech firms like Google, Facebook and Amazon about Artificial Intelligence and applications to unstructured data is not the end of the field. There abound many applications of deep learning, particularly within the construction sector in areas such as site planning and management, health and safety and construction cost prediction, which are yet to be explored. The overall aim of this article was to review existing studies that have applied deep learning to prevalent construction challenges like structural health monitoring, construction site safety, building occupancy modelling and energy demand prediction. To the best of our knowledge, there is currently no extensive survey of the applications of deep learning techniques within the construction industry. This review would inspire future research into how best to apply image processing, computer vision, natural language processing techniques of deep learning to numerous challenges in the industry. Limitations of deep learning such as the black box challenge, ethics and GDPR, cybersecurity and cost, that can be expected by construction researchers and practitioners when adopting some of these techniques were also discussed