Deep learning-based weathering type recognition in historical stone monuments

Hatır, M. Ergün; Barstuğan, Mücahit; İnce, İsmail

doi:10.1016/j.culher.2020.04.008

Cited by 45 publications

(16 citation statements)

References 55 publications

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“…The optimization of wind and wave application could extend to many fields of device optimization, layout optimization and energy management. Pattern recognition can actually be considered an improved technique of image processing, but it focuses on the structure of the models [95] instead of being limited to images. The future development of wind and wave energy may transfer offshore and explore more accurate models with lower computation cost.…”

Section: Discussionmentioning

confidence: 99%

Review on Deep Learning Research and Applications in Wind and Wave Energy

2022

Energies

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Wind energy and wave energy are considered to have enormous potential as renewable energy sources in the energy system to make great contributions in transitioning from fossil fuel to renewable energy. However, the uncertain, erratic, and complicated scenarios, as well as the tremendous amount of information and corresponding parameters, associated with wind and wave energy harvesting are difficult to handle. In the field of big data handing and mining, artificial intelligence plays a critical and efficient role in energy system transition, harvesting and related applications. The derivative method of deep learning and its surrounding prolongation structures are expanding more maturely in many fields of applications in the last decade. Even though both wind and wave energy have the characteristics of instability, more and more applications have implemented using these two renewable energy sources with the support of deep learning methods. This paper systematically reviews and summarizes the different models, methods and applications where the deep learning method has been applied in wind and wave energy. The accuracy and effectiveness of different methods on a similar application were compared. This paper concludes that applications supported by deep learning have enormous potential in terms of energy optimization, harvesting, management, forecasting, behavior exploration and identification.

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Section: Discussionmentioning

confidence: 99%

Review on Deep Learning Research and Applications in Wind and Wave Energy

2022

Energies

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“…In addition, the integration of non-destructive tests, simulation models and in situ studies should be investigated for ML-aided diagnosis. Hatir et al (2020) compared ANN and CNN for weathering type recognition; the results' accuracy rates are similar (93.95% and 99.4%, respectively). However, CNN proved to be more effective and reliable, both in terms of time and classification outcomes.…”

Section: Research Backgroundmentioning

confidence: 91%

Decay detection in historic buildings through image-based deep learning

2023

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Nowadays, built heritage condition assessment is realized through on-site or photo-aided visual inspections, reporting pathologies manually on drawings, photographs, notes. The knowledge of the state of conservation goes through subjective and time or cost consuming procedures. This is even relevant for a historic building characterized by geometrical and morphological complexity and huge extension, or at risk of collapse. In this context, advancements in the field of Computer Vision and Artificial Intelligence provide an opportunity to address these criticalities. The proposed methodology is based on a Mask R-CNN model, for the detection of decay morphologies on built heritages, and, particularly on historic buildings. The experimentation has been carried out and validated on a highly heterogeneous dataset of images of historic buildings, representative of the regional Architectural Heritage, such as: castles, monasteries, noble buildings, rural buildings. The outcomes highlighted the significance of this remote, non-invasive inspection technique, in support of the technicians in the preliminary knowledge of the building state of conservation, and, most of all, in the decay mapping of some particular classes of alterations (moist area, biological colonization).

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“…2 The order of the most harmful plant species cause deterioration [17]. It was previously concluded that the plant growth was concentrated in abandoned and unused buildings with a lack of restoration and maintenance practices such as cleaning [10,18,20,23,[62][63][64][65][66][67][68][69]. Besides, the presence of bryophytes and lichens can support vascular plant colonization [10].…”

Section: Co-factors For Plant Invasion and Colonizationmentioning

confidence: 99%

“…Sometimes the roots penetrate to a depth of about 8-10 m and sometimes cover distances of more than 50 m, as in the case of one specific Ficus carica L. (the common fig) [19]. Plants have been classified as one of the weathering types that historical buildings are exposed to, because they develop easily, especially with the availability of appropriate conditions [20]. Furthermore, plants are more urgent to edaphoclimatic conditions, as they act as bioindicators for the microenvironment where they grow [15,21].…”

Section: Introductionmentioning

confidence: 99%

Assessment of the potential effects of plants with their secreted biochemicals on the biodeterioration of archaeological stones

Elgohary

Mansour

Salem

2022

Biomass Conv. Bioref.

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Archaeological sites and structures suffer from several factors of deterioration. The present survey focuses on the biodeterioration by plants, which is not less important than the rest of the deterioration factors; moreover, in many cases, it leads to their occurrence. Despite the importance of plants in our lives and their aesthetic appearance around stone monuments, they pose a threat to their survival. Therefore, it is necessary to have such a review to detail this topic and understand its severity by identifying the most common and harmful plant species, the co-factors for their existence, and the mechanism of damage. Physical damage through causing cracks, detachment, and loss of structural integrity and chemical damage from root secretions-biological damage by encouraging microorganisms and insects supported by examples of sites that have been damaged by plants. The results will help to apply appropriate methods of prevention and control by mentioning the advantages and disadvantages of each method.

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Deep learning-based weathering type recognition in historical stone monuments

Cited by 45 publications

References 55 publications

Review on Deep Learning Research and Applications in Wind and Wave Energy

Review on Deep Learning Research and Applications in Wind and Wave Energy

Decay detection in historic buildings through image-based deep learning

Assessment of the potential effects of plants with their secreted biochemicals on the biodeterioration of archaeological stones

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