Software for roof defects recognition on aerial photographs

Yudin, Dmitry; Naumov, A. E.; Dolzhenko, A. V.; Patrakova, E.

doi:10.1088/1742-6596/1015/3/032152

Cited by 7 publications

(5 citation statements)

References 2 publications

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“…attractiveness of the factor space of renovation, and, accordingly, individual technical and urban characteristics (factors) of renovation objects, have the greatest impact on the profitability, profitability and investment reliability of renovation REI [3]. After analyzing the specifics of the application of stochastic methods of factor analysis, we determined the need to identify the tightness of the relationship between the studied technical and town-planning factors of the investment attractiveness of real estate renovation by means of a correlation analysis [4].…”

Section: Resultsmentioning

confidence: 99%

Assessing the Investment Attractiveness of Renovation

Zharikov¹

2020

Proceedings of the Russian Conference on Digital Economy and Knowledge Management (RuDEcK 2020)

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The widespread use of mathematical methods is the most important area of improving economic analysis, increasing the effectiveness of the analysis of the organizational activities of investment in the renovation of real estate. One of the most effective tools for implementing the mathematical problem of economic analysis is factor analysis. Factor analysis is a gradual transition from the initial factor system to the final system of factors, the disclosure of a set of quantitatively measured factors that influence the change in the effective indicator. In the presence of many different factors affecting the investment attractiveness of renovation, it is advisable to apply regression analysis. The regression equations and indicators of statistical significance obtained in the work allow suggesting the feasibility and sufficiency of the set of technical and urban planning factors of the renovated property accepted for analysis to obtain reliable estimates of the potential capitalization of the investment and construction project of the renovated real estate. The established close relationship between the variable factors and investment indicators of renovation allows effectively, through the implementation of the proposed regression equation, determining the economic efficiency of the renovated object.

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Section: Resultsmentioning

confidence: 99%

Assessing the Investment Attractiveness of Renovation

Zharikov¹

2020

Proceedings of the Russian Conference on Digital Economy and Knowledge Management (RuDEcK 2020)

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“…Examples of works in the structural domain include the multiclass classifier developed by Rubio et al (2019) which detects structural defects such as concrete delamination and rebar exposure. Despite the above notable works, few efforts exist on performance classification for roofs (Yudin et al , 2018), which is important as roofs represent one of the most vulnerable building components, exhibiting multiple forms of defects that are more than just cracks (e.g. blisters, vegetation, ponding/drainage issues, etc.…”

Section: Background and Literaturementioning

confidence: 99%

“…Aimed at roofing, Hezaveh et al (2017) developed a CNN model to detect hail effects in roof shingles. Other efforts (Yudin et al , 2018) targeted the development of an automated roof detection framework using image segmentation to identify multiple defects and their sizes within the same image. However, the proposed model has low accuracy (mean accuracy < 65%), thus indicating the need to address the defects separately to improve condition assessment accuracy.…”

Section: Introductionmentioning

confidence: 99%

Using image analysis to quantify defects and prioritize repairs in built-up roofs

Mostafa

Hegazy

Hunsperger

et al. 2023

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Purpose This paper aims to use convolutional neural networks (CNNs) to provide an objective approach to classify deteriorated building assets according to the type and extent of damage. This research supports automated inspection of buildings and focuses on roofing elements as one of the most critical and externally distressed elements in buildings. Design/methodology/approach In this paper, 5,000+ images of deteriorated roofs from several buildings were collected to design a CNN system that automatically identifies and sizes roofing defects. Experimenting with different CNN formulations, the best accuracy is achieved using two-stage CNNs. The first-stage CNN classifies images into defect/no defect, while the second stage classifies the defected images according to the damage type. Based on the image classification, optimization is used to prioritize roof repairs by maximizing the return from limited rehabilitation funds. Findings The developed CNNs reached 95% and 97% accuracy for the first and second phases, respectively, which is higher than achieved in previous literature efforts. Using the proposed model to automate inspection and condition assessment activities proved to be faster than conventional methods. Repair/replace strategy for a case study of 21 campus buildings based on their condition and budgetary constraints was suggested. Research limitations/implications Future research includes testing different data acquisition technologies (e.g. infrared imaging), performing severity-based classification and integrating with BIM for defect localization. Originality/value This study provides an objective approach to automate asset condition assessment and improve funding decisions using a combination of image analysis and optimization techniques. The proposed approach is applicable toward other asset types and components.

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“…Совершенствование экспертно-нормативного метода оценки физического износа плоских рулонных кровель, по мнению авторов, должно строиться на повышении количества, периодичности и достоверности получения фактологического материала [6], расширении номенклатуры учитываемых расчетом дефектов и учете удельного веса отдельного дефекта в общем объеме, предложении аппаратно-программного ведения динамического анализа дефектов, реализующих дифференцированные жизненные циклы.…”

Section: повышение качества управления жизненным циклом плоских рулон...unclassified

Flat Roll Roofs Life Cycle Management Improving With Advanced Technical State Inspection

Dolzhenko,

Naumov,

Shchenyatsky

2023

Bulletin of Belgorod State Technological University Named After. V. G. Shukhov

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The quality of life cycle management of flat rolled roofs depends on the effectiveness of the technology for assessing their physical deterioration, the value of which is significantly affected by the operating conditions of roofs and sources of impacts. Flat rolled roofs are the most common technical solutions in the real estate and are very defect-intensive building systems, the physical deterioration of which affects the structural safety and functional reliability not only of them, but of the entire building as a whole. Defects in flat rolled roofs, as well as defects in building structures in general, are in close organizational and technological relationship, in which the life cycle of an individual defect can be a synergistic multiplication or subtraction in defects life cycle parameters. The key flaw of modern methods for assessing physical deterioration are the initial and predominantly manual instrumental studies, in connection with which the methods are overly simplified, templated, subjective in natural assessment of life cycle resource intensity. In this regard, it seems promising targeted research related to the comprehensive improvement for construction and technical flaw detection methods, both in software improving that increases the reliability and quality of the factual result, and algorithms for predictive estimation of individual defects and building systems life cycles. That manages life cycle more effectively in the media of practical methods for dynamic defects analyzing and assessing the technical condition of structures in general. The paper presents mainline in improving of method for assessing the physical deterioration of flat rolled roofs, quantitatively taking into account the combined impact of defects on building system technical condition at its life cycle, significant expanding the operational tools for building technical management.

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Software for roof defects recognition on aerial photographs

Cited by 7 publications

References 2 publications

Assessing the Investment Attractiveness of Renovation

Assessing the Investment Attractiveness of Renovation

Using image analysis to quantify defects and prioritize repairs in built-up roofs

Flat Roll Roofs Life Cycle Management Improving With Advanced Technical State Inspection

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