Damage detection on cooling tower shell based on model textures

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F24%3A00375251" target="_blank" >RIV/68407700:21110/24:00375251 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.14311/CEJ.2024.01.0007" target="_blank" >https://doi.org/10.14311/CEJ.2024.01.0007</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/CEJ.2024.01.0007" target="_blank" >10.14311/CEJ.2024.01.0007</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Damage detection on cooling tower shell based on model textures

Popis výsledku v původním jazyce

Ensuring the structural integrity of cooling towers is paramount for safety and efficient operation. This paper presents a novel approach for detecting damage on cooling tower shells, utilising textures derived from laser scanning and close-range photogrammetry. The proposed method delves beyond the limitations of solely relying on colour information by harnessing the rich details embedded in various textures, including diffuse, normal, displacement, and occlusion. The study demonstrates the efficacy of this approach for identifying significant concrete damage. A Convolutional Neural Network (CNN) trained on diffuse textures successfully detects high damage instances with minimal misdetection. However, accurately pinpointing low damage, often manifesting as subtle cracks, and mimicking other patterns like air pores, ribbing, and colour variations, presents a formidable challenge. To tackle this challenge, the authors introduce a novel "composed raster layer" that merges information from multiple textures. This pre-processed layer amplifies the visual cues associated with low damage, facilitating its differentiation from similar patterns. While the current implementation employing multi-resolution segmentation and rule-based classification exhibits promising results, further optimization is acknowledged to refine the accuracy of low damage detection. The successful application of textures commonly used in rendering techniques underscores their remarkable potential for enhancing damage detection in civil engineering applications. While acknowledging limitations such as the analysis of a single cooling tower and the reliance on specific software for damage detection, the study proposes future research directions. This research holds significant implications for the field of civil engineering by offering a promising approach for automated and efficient damage detection on cooling tower shells.

Název v anglickém jazyce

Damage detection on cooling tower shell based on model textures

Popis výsledku anglicky

Ensuring the structural integrity of cooling towers is paramount for safety and efficient operation. This paper presents a novel approach for detecting damage on cooling tower shells, utilising textures derived from laser scanning and close-range photogrammetry. The proposed method delves beyond the limitations of solely relying on colour information by harnessing the rich details embedded in various textures, including diffuse, normal, displacement, and occlusion. The study demonstrates the efficacy of this approach for identifying significant concrete damage. A Convolutional Neural Network (CNN) trained on diffuse textures successfully detects high damage instances with minimal misdetection. However, accurately pinpointing low damage, often manifesting as subtle cracks, and mimicking other patterns like air pores, ribbing, and colour variations, presents a formidable challenge. To tackle this challenge, the authors introduce a novel "composed raster layer" that merges information from multiple textures. This pre-processed layer amplifies the visual cues associated with low damage, facilitating its differentiation from similar patterns. While the current implementation employing multi-resolution segmentation and rule-based classification exhibits promising results, further optimization is acknowledged to refine the accuracy of low damage detection. The successful application of textures commonly used in rendering techniques underscores their remarkable potential for enhancing damage detection in civil engineering applications. While acknowledging limitations such as the analysis of a single cooling tower and the reliance on specific software for damage detection, the study proposes future research directions. This research holds significant implications for the field of civil engineering by offering a promising approach for automated and efficient damage detection on cooling tower shells.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20101 - Civil engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

The Civil Engineering Journal

ISSN

1210-4027

e-ISSN

1805-2576

Svazek periodika

1

Číslo periodika v rámci svazku

Vol. 33 No. 1 (2024)

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

13

Strana od-do

92-104

Kód UT WoS článku

001217355400007

EID výsledku v databázi Scopus

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