A novel approach for detection and classification of re-entrant crack using modified CNNetwork

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shadrack Fred Mahenge ◽  
Ala Alsanabani
Keyword(s):  
Deep Learning ◽  
Crack Size ◽  
Visible Range ◽  
Successful Completion ◽  
Content Type ◽  
Human Eye ◽  
Novel Approach ◽  
Inspection Process ◽  
Structure Collapse ◽  
Human Inspection

Purpose In the purpose of the section, the cracks that are in the construction domain may be common and usually fixed with the human inspection which is at the visible range, but for the cracks which may exist at the distant place for the human eye in the same building but can be captured with the camera. If the crack size is quite big can be visible but few cracks will be present due to the flaws in the construction of walls which needs authentic information and confirmation about it for the successful completion of the wall cracks, as these cracks in the wall will result in the structure collapse. Design/methodology/approach In the modern era of digital image processing, it has captured the importance in all the domain of engineering and all the fields irrespective of the division of the engineering, hence, in this research study an attempt is made to deal with the wall cracks which are found or searched during the building inspection process, in the present context in association with the unique U-net architecture is used with convolutional neural network method. Findings In the construction domain, the cracks may be common and usually fixed with the human inspection which is at the visible range, but for the cracks which may exist at the distant place for the human eye in the same building but can be captured with the camera. If the crack size is quite big can be visible but few cracks will be present due to the flaws in the construction of walls which needs authentic information and confirmation about it for the successful completion of the wall cracks, as these cracks in the wall will result in the structure collapse. Hence, for the modeling of the proposed system, it is considered with the image database from the Mendeley portal for the analysis. With the experimental analysis, it is noted and observed that the proposed system was able to detect the wall cracks, search the flat surface by the result of no cracks found and it is successful in dealing with the two phases of operation, namely, classification and segmentation with the deep learning technique. In contrast to other conventional methodologies, the proposed methodology produces excellent performance results. Originality/value The originality of the paper is to find the portion of the cracks on the walls using deep learning architecture.

scholarly journals Deep learning for detecting distresses in buildings and pavements: a critical gap analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Faris Elghaish ◽  
Sandra T. Matarneh ◽  
Saeed Talebi ◽  
Soliman Abu-Samra ◽  
Ghazal Salimi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Crack Detection ◽  
Gap Analysis ◽  
Network Models ◽  
Structure Type ◽  
Neural Network Models ◽  
Content Type ◽  
Critical Gap ◽  
Inspection Process ◽  
Structured Analysis

Purpose The massive number of pavements and buildings coupled with the limited inspection resources, both monetary and human, to detect distresses and recommend maintenance actions lead to rapid deterioration, decreased service life, lower level of service and increased community disruption. Therefore, this paper aims at providing a state-of-the-art review of the literature with respect to deep learning techniques for detecting distress in both pavements and buildings; research advancements per asset/structure type; and future recommendations in deep learning applications for distress detection. Design/methodology/approach A critical analysis was conducted on 181 papers of deep learning-based cracks detection. A structured analysis was adopted so that major articles were analyzed according to their focus of study, used methods, findings and limitations. Findings The utilization of deep learning to detect pavement cracks is advanced compared to assess and evaluate the structural health of buildings. There is a need for studies that compare different convolutional neural network models to foster the development of an integrated solution that considers the data collection method. Further research is required to examine the setup, implementation and running costs, frequency of capturing data and deep learning tool. In conclusion, the future of applying deep learning algorithms in lieu of manual inspection for detecting distresses has shown promising results. Practical implications The availability of previous research and the required improvements in the proposed computational tools and models (e.g. artificial intelligence, deep learning, etc.) are triggering researchers and practitioners to enhance the distresses’ inspection process and make better use of their limited resources. Originality/value A critical and structured analysis of deep learning-based crack detection for pavement and buildings is conducted for the first time to enable novice researchers to highlight the knowledge gap in each article, as well as building a knowledge base from the findings of other research to support developing future workable solutions.

A cell-based smoothed point interpolation method (CS-PIM) for 2D thermoelastic problems

2017 ◽  
Vol 27 (6) ◽  
pp. 1249-1265 ◽  
Author(s):  
Yijun Liu ◽  
Guiyong Zhang ◽  
Huan Lu ◽  
Zhi Zong
Keyword(s):  
Thermal Stresses ◽  
Interpolation Method ◽  
Content Type ◽  
Novel Approach ◽  
Convergence Results ◽  
Point Interpolation Method ◽  
Point Interpolation ◽  
Thermoelastic Problems ◽  
Gradient Smoothing ◽  
Practical Implications

Purpose Due to the strong reliance on element quality, there exist some inherent shortcomings of the traditional finite element method (FEM). The model of FEM behaves overly stiff, and the solutions of automated generated linear elements are generally of poor accuracy about especially gradient results. The proposed cell-based smoothed point interpolation method (CS-PIM) aims to improve the results accuracy of the thermoelastic problems via properly softening the overly-stiff stiffness. Design/methodology/approach This novel approach is based on the newly developed G space and weakened weak (w2) formulation, and of which shape functions are created using the point interpolation method and the cell-based gradient smoothing operation is conducted based on the linear triangular background cells. Findings Owing to the property of softened stiffness, the present method can generally achieve better accuracy and higher convergence results (especially for the temperature gradient and thermal stress solutions) than the FEM does by using the simplest linear triangular background cells, which has been examined by extensive numerical studies. Practical implications The CS-PIM is capable of producing more accurate results of temperature gradients as well as thermal stresses with the automated generated and unstructured background cells, which make it a better candidate for solving practical thermoelastic problems. Originality/value It is the first time that the novel CS-PIM was further developed for solving thermoelastic problems, which shows its tremendous potential for practical implications.

scholarly journals Deep Learning for Transient Image Reconstruction from ToF Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1962
Author(s):  
Enrico Buratto ◽  
Adriano Simonetto ◽  
Gianluca Agresti ◽  
Henrik Schäfer ◽  
Pietro Zanuttigh
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Light Response ◽  
Real Data ◽  
Depth Image ◽  
Learning Approach ◽  
Multiple Reflections ◽  
Noisy Input ◽  
Novel Approach ◽  
Incoming Light

In this work, we propose a novel approach for correcting multi-path interference (MPI) in Time-of-Flight (ToF) cameras by estimating the direct and global components of the incoming light. MPI is an error source linked to the multiple reflections of light inside a scene; each sensor pixel receives information coming from different light paths which generally leads to an overestimation of the depth. We introduce a novel deep learning approach, which estimates the structure of the time-dependent scene impulse response and from it recovers a depth image with a reduced amount of MPI. The model consists of two main blocks: a predictive model that learns a compact encoded representation of the backscattering vector from the noisy input data and a fixed backscattering model which translates the encoded representation into the high dimensional light response. Experimental results on real data show the effectiveness of the proposed approach, which reaches state-of-the-art performances.

Deep Learning based Thermal Crack Detection on Structural Concrete Exposed to Elevated Temperature

2021 ◽  
pp. 136943322098663
Author(s):  
Diana Andrushia A ◽  
Anand N ◽  
Eva Lubloy ◽  
Prince Arulraj G
Keyword(s):  
Deep Learning ◽  
Crack Detection ◽  
Detection Method ◽  
Concrete Structures ◽  
Vital Role ◽  
Subjective Knowledge ◽  
Thermal Cracks ◽  
Reinforced Cement ◽  
Novel Method ◽  
Human Inspection

Health monitoring of concrete including, detecting defects such as cracking, spalling on fire affected concrete structures plays a vital role in the maintenance of reinforced cement concrete structures. However, this process mostly uses human inspection and relies on subjective knowledge of the inspectors. To overcome this limitation, a deep learning based automatic crack detection method is proposed. Deep learning is a vibrant strategy under computer vision field. The proposed method consists of U-Net architecture with an encoder and decoder framework. It performs pixel wise classification to detect the thermal cracks accurately. Binary Cross Entropy (BCA) based loss function is selected as the evaluation function. Trained U-Net is capable of detecting major thermal cracks and minor thermal cracks under various heating durations. The proposed, U-Net crack detection is a novel method which can be used to detect the thermal cracks developed on fire exposed concrete structures. The proposed method is compared with the other state-of-the-art methods and found to be accurate with 78.12% Intersection over Union (IoU).

Human skill integrated motion planning of assembly manipulation for 6R industrial robot

2019 ◽  
Vol 46 (1) ◽  
pp. 171-180
Author(s):  
Yi Liu ◽  
Ming Cong ◽  
Hang Dong ◽  
Dong Liu
Keyword(s):  
Deep Learning ◽  
Motion Planning ◽  
Industrial Robot ◽  
Robotic Assembly ◽  
3D Vision ◽  
Content Type ◽  
Human Skill ◽  
Depth Images ◽  
Positioning Method ◽  
Assembly Technology

Purpose The purpose of this paper is to propose a new method based on three-dimensional (3D) vision technologies and human skill integrated deep learning to solve assembly positioning task such as peg-in-hole. Design/methodology/approach Hybrid camera configuration was used to provide the global and local views. Eye-in-hand mode guided the peg to be in contact with the hole plate using 3D vision in global view. When the peg was in contact with the workpiece surface, eye-to-hand mode provided the local view to accomplish peg-hole positioning based on trained CNN. Findings The results of assembly positioning experiments proved that the proposed method successfully distinguished the target hole from the other same size holes according to the CNN. The robot planned the motion according to the depth images and human skill guide line. The final positioning precision was good enough for the robot to carry out force controlled assembly. Practical implications The developed framework can have an important impact on robotic assembly positioning process, which combine with the existing force-guidance assembly technology as to build a whole set of autonomous assembly technology. Originality/value This paper proposed a new approach to the robotic assembly positioning based on 3D visual technologies and human skill integrated deep learning. Dual cameras swapping mode was used to provide visual feedback for the entire assembly motion planning process. The proposed workpiece positioning method provided an effective disturbance rejection, autonomous motion planning and increased overall performance with depth images feedback. The proposed peg-hole positioning method with human skill integrated provided the capability of target perceptual aliasing avoiding and successive motion decision for the robotic assembly manipulation.

Transdural herniation of the thoracic spinal cord: untethering via a posterolateral transpedicular approach

2004 ◽  
Vol 1 (2) ◽  
pp. 223-227 ◽  
Author(s):  
Ryder Gwinn ◽  
Fraser Henderson
Keyword(s):  
Spinal Cord ◽  
Posterior Approach ◽  
Thoracic Spinal Cord ◽  
Dural Repair ◽  
Content Type ◽  
Transpedicular Approach ◽  
Thoracic Spinal ◽  
Novel Approach ◽  
Spinal Cord Herniation ◽  
Thoracic Cord

✓ Anterior spinal cord herniation is a well-documented condition in which the thoracic cord becomes tethered within a defect in the anterior dura mater. Typical procedures have involved a posterior approach with direct manipulation of the thoracic cord to expose and blindly release its point of tethering. The authors report three cases in which a novel approach for the treatment of anterior thoracic cord herniation was performed, cord manipulation and traction are minimized, and direct dural repair of the defect is performed.

Chiasmatic optic glioma treated with chemotherapy

1985 ◽  
Vol 63 (6) ◽  
pp. 862-866 ◽  
Author(s):  
Jeffrey G. Rosenstock ◽  
Roger J. Packer ◽  
Larissa Bilaniuk ◽  
Derek A. Bruce ◽  
Jerri-Lynne Radcliffe ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Young Children ◽  
Progressive Disease ◽  
Actinomycin D ◽  
Initial Therapy ◽  
Optic Glioma ◽  
Newly Diagnosed ◽  
Content Type ◽  
Novel Approach ◽  
Clinical Stabilization

✓ Chiasmatic optic glioma is a rare tumor with an erratic natural history, usually seen in young children. A prior study from this institution demonstrated that these lesions were frequently lethal, despite initial clinical stabilization following radiation therapy, and that visual, intellectual, and late endocrinological disabilities were prevalent. A novel approach was developed in 1977, when an initial clinical response to vincristine was recorded in a child with a recurrent optic glioma. Since then, all children with recurrent optic glioma and all children aged 6 years old and under with newly diagnosed optic glioma have been offered a program of initial therapy with vincristine and actinomycin D for six cycles over 18 months. The four children with recurrent tumor who were treated with that regimen remain clinically stable 13 to 115 months after chemotherapy. Twelve children (eight under 24 months old) with newly diagnosed optic glioma have been treated with this program, and three are still on therapy. Four developed progression while on therapy, and five remain stable from 1 to 60 months posttherapy. The four children who developed progressive disease have been treated with radiation therapy and remain stable. Six of the 12 children showed shrinkage of their tumor on computerized tomography while receiving chemotherapy. This program may serve as an alternative to initial radiation therapy in young children.

scholarly journals Deep Learning-Based Object Detection for Unmanned Aerial Systems (UASs)-Based Inspections of Construction Stormwater Practices

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2834
Author(s):  
Billur Kazaz ◽  
Subhadipto Poddar ◽  
Saeed Arabi ◽  
Michael A. Perez ◽  
Anuj Sharma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Pollution Prevention ◽  
Aerial Images ◽  
Unmanned Aerial Systems ◽  
Construction Sites ◽  
State Regulations ◽  
Novel Approach ◽  
Model Training ◽  
Aerial Systems

Construction activities typically create large amounts of ground disturbance, which can lead to increased rates of soil erosion. Construction stormwater practices are used on active jobsites to protect downstream waterbodies from offsite sediment transport. Federal and state regulations require routine pollution prevention inspections to ensure that temporary stormwater practices are in place and performing as intended. This study addresses the existing challenges and limitations in the construction stormwater inspections and presents a unique approach for performing unmanned aerial system (UAS)-based inspections. Deep learning-based object detection principles were applied to identify and locate practices installed on active construction sites. The system integrates a post-processing stage by clustering results. The developed framework consists of data preparation with aerial inspections, model training, validation of the model, and testing for accuracy. The developed model was created from 800 aerial images and was used to detect four different types of construction stormwater practices at 100% accuracy on the Mean Average Precision (MAP) with minimal false positive detections. Results indicate that object detection could be implemented on UAS-acquired imagery as a novel approach to construction stormwater inspections and provide accurate results for site plan comparisons by rapidly detecting the quantity and location of field-installed stormwater practices.

Reflecting on perceived deinstitutionalization of gender‐biased employment practices in accountancy

2012 ◽  
Vol 9 (4) ◽  
pp. 300-336 ◽  
Author(s):  
Rosalind H. Whiting
Keyword(s):  
Organizational Change ◽  
Cultural Change ◽  
Sample Selection ◽  
Age Groups ◽  
Young Female ◽  
Human Resource Managers ◽  
Employment Practices ◽  
Content Type ◽  
Novel Approach ◽  
Chartered Accountants

PurposeThe purpose of this paper is to explore the changes in gender‐biased employment practices that it is perceived have occurred in New Zealand accountancy workplaces over the last 30 years, using Oliver's model of deinstitutionalization.Design/methodology/approachSequential interviewing was carried out with 69 experienced chartered accountants and three human resource managers, and at a later date with nine young female accountants.FindingsEvidence is presented of perceived political, functional and social pressures cumulatively contributing to deinstitutionalization of overt gender‐biased employment practices, with social and legislative changes being the most influential. Deinstitutionalization appears incomplete as some more subtle gender‐biased practices still remain in New Zealand's accountancy workplaces, relating particularly to senior‐level positions.Research limitations/implicationsThis study adds to understanding of how professions evolve. The purposeful bias in the sample selection, the small size of two of the interviewee groups, and the diversity in the interviewees' workplaces are recognized limitations.Practical implicationsIdentification of further cultural change is required to deinstitutionalize the more subtle gender‐biased practices in accountancy organizations. This could help to avoid a serious deficiency of senior chartered accountants in practice in the future.Originality/valueThis paper represents one of a limited number of empirical applications of the deinstitutionalization model to organizational change and is the first to address the issue of gender‐biased practices in a profession. The use of sequential interviewing of different age groups, in order to identify and corroborate perceptions of organizational change is a novel approach.

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