The first aim of this study is to determine the effect of the sample size on the Leeb hardness (HL), which is a non-destructive test, in magmatic rocks. For this scope, cubic samples with edge ...lengths of 3, 4, 5, 6, 7, 8, 9, and 10 cm from 15 different magmatic rocks (igneous, volcanic, and pyroclastic) were prepared, and variations of the HL values were measured. Based on the results, it was determined that as the strength values of magmatic rock samples increase, the effect of sample size on HL value decreases. Additionally, the minimum sample size, at which the HL value did not change, was found to be 7 cm. The second aim of this study is to examine the correlation between the HL values of the rocks’ minimum sample size, and the index properties were examined by the simple regression method. For this purpose, 76 various types of magmatic rock samples were tested in the laboratory in order to determine their dry and saturated HL values and index (P-wave velocity, dry density, porosity, and uniaxial compressive strength) values. The relationship between the dry and saturated HL values and the index properties of the samples was examined by simple regression analysis. Based on this analysis, a strong linear relationship was found between the index properties. In addition to this, a strong exponential relationship was determined between the dry and saturated HL and uniaxial compressive strength (UCS) values of these rocks, and the determination coefficients (
R
2
) were found to be 0.85 and 0.86, respectively. The Leeb hardness test can be used as a non-destructive method where a regular-shaped rock sample is difficult to obtain (in rocks and/or historical structures) and to estimate the rock properties practically in the laboratory and field.
•Physical and mechanical properties of andesite exposed to F–T cycles change.•The most important factor in F–T process is the existence of water.•New macro and micro fractures occur at andesites ...exposed to F–T cycles.•The weathering of andesites depends on their dimensions and position.
Stones used in the construction of cultural and historical buildings are exposed to various direct or indirect atmospheric effects depending on climatic and seasonal conditions. Stones deteriorate partially or fully as a result of this exposure. Therefore, the historicity of these buildings cannot withstand long. The freeze–thaw (F–T) process is one of the prominent conditions of this kind. Water penetration into the building stone via capillarity promotes weathering. When the temperature falls below 0°C, the water freezes in the pores and tiny cracks of the building stones, causing volume expansion and exerting pressure on the stones. This cycle occurs most in areas where the temperature fluctuates above and below freezing often and causes and induces undesired weathering within the building stones. The Konya city, having been an old settlement province from 9000B.C., encompasses quite valuable ancient buildings. Andesitic rocks, which are called Sille Stone in the region, were used in most of these buildings.
In this study, fresh andesitic rocks obtained from the stone quarry were tested in five F–T cycles in the laboratory. Textural changes that occurred in the deteriorated stones were examined by a polarizing microscope. Changes in porosity (n), uniaxial compressive strength (σu), point load strength (IS(50)), Brazilian tensile strength (σt), Böhme abrasion loss (BA), and ultrasonic velocity were statistically evaluated, and the effects of the number of F–T cycles on basic physical and mechanical properties of the stone were determined. In addition, weathering effects in the historical buildings constructed from the Sille andesite were investigated.
Transportation has been one of the basic requirements of humanity since the earliest periods of civilization. One of the architectural structures designed to meet this requirement is historic stone ...bridges. One of the most important stages in these conservation works is the assessment of materials that constitute the structures. Non-destructive testing techniques (NDT) are widely used to obtain qualitative data and also make comparisons. In this study, it was aimed to determine deteriorations on the Mısırlıoğlu Bridge located in Sille settlement of Konya by NDT technique and to form the map from obtained values to perform conservation works. As a result of the analyses performed, considerable deteriorations in the building stones used in the abutments and arches of the structure were determined. Besides, it is detected that uniaxial compressive strength (UCS) value of the fresh samples is high (UCS: 61 MPa) while UCS values of the building stones used at the bridge decrease in the range of low and high (8-51 MPa) due to the atmospheric effects.
•The importance of stone weathering recognition in cultural areas.•Determination of weathering recognition by deep learning (DL).•Test performance of the DL model in monuments.
Stone cultural ...heritages provide meaningful value and information about the culture, religion, economics, and esthetics of the period in which they were built. However, these heritages tend to lose their features due to weathering effects. Human-induced misrecognition in conservation and restoration practices used with these structures may lead to the disappearance of important architectural traces or serious mistakes that can affect monuments’ structural integrity. In this study, recognition models based on deep learning (DL) and Artificial Neural Network (ANN) were developed to eliminate human errors that may arise in weathering recognition. For these models, fresh rock and eight different weathering types commonly observed in the historical structures of the Konya region were initially detected and photographed by field imaging studies. The DL and ANN models were created for 8598 images with these nine different types (fresh rock, flaking, contour scaling, cracking, differential erosion, black crust, efflorescence, higher plants, and graffiti). Although the accuracy rates obtained from the DL and ANN models are 99.4% and 93.95%, respectively, the recall rate (96–100%) in each class of the DL model has been determined to be higher. Based on the results of the DL classification performed with the study's model, the lowest precision rates in the testing phase were found in fresh rock (97%) and flaking (98%), while 100% precision rates were obtained in the other classification groups.
•Deterioration development in archeological sites.•Determination of stone deterioration via artificial intelligence.•Deterioration map with Mask R-CNN.
The detection of deterioration in archeological ...heritage sites is a very time-consuming task that requires expertise. Furthermore, vision-based approaches can cause errors, considering the complex types of deterioration that develop in different scales and forms in monuments. This problem can be solved effectively owing to computer vision algorithms, commonly used in different areas nowadays. This study aims to develop a model that automatically detects and maps deteriorations (biological colonization, contour scaling, crack, higher plant, impact damage, microkarst, missing part) and restoration interventions using the Mask R-CNN algorithm, which has recently come to the fore with its feature of recognizing small and large-sized objects. To this end, a total of 2460 images of Yazılıkaya monuments in the Hattusa archeological site, which is on the UNESCO heritage list, were gathered. In the training phase of the proposed method, it was trained in model 1 to distinguish deposit deterioration commonly observed on the surface of monuments from other anomalies. Other anomalies trained were model 2. In this phase of the models, the average precision values with high accuracy rates ranging from 89.624% to 100% were obtained for the deterioration classes. The developed algorithms were tested on 4 different rock reliefs in Yazılıkaya, which were not used in the training phase. In addition, an image of the Eflatunpınar water monument, which is on the UNESCO tentative list, was used to test the model's universality. According to the test results, it was determined that the models could be successfully applied to obtain maps of deterioration and restoration interventions in monuments in different regions.
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Compressive strength of rocks is an important factor in structural design in rock engineering. Compressive strength can be determined in the laboratory by means of the uniaxial compressive strength ...(UCS) test, or it can be estimated indirectly by simple experiments such as point load strength (PLT) test and Schmidt hammer rebound test. Although the UCS test method is time-consuming and expensive, it is simple when compared to other methods. Therefore, many studies have been performed to estimate UCS values of rocks. Studies indicated that correlation coefficient of rock groups is low unless they are classified as metamorphic, sedimentary, or volcanic. Pyroclastic rocks are widely used as construction materials because of the fact that they crop out over extensive areas in the world. To estimate the UCS values of pyroclastic rocks in Central and Western Anatolia region, Turkey, multiple linear regression (MLR) analysis and gene expression programming (GEP) were employed and during the analysis, and
PLT
,
ρ
d
,
ρ
s
, and
n
were used as the independent variables. Based on the analysis results, it was detected that the GEP methods gave better results than MLR method. Additionally, the correlation coefficient (R
2
) values of training and sets of validation of the GEP-I model are 0.8859 and 0.9325, respectively, and this model, thereby, is detected the best of generation individuals for prediction of the UCS.
In building stones that are widely used in the construction of immovable cultural heritage, preservation efforts are necessary due to various weathering processes. The accuracy and effectiveness of ...the restoration works, specifically for cultural heritage, are important factors. The most basic phase of these works is determining the stone types and preparing geo-lithological maps. However, the time-consuming process and difficult field work may lead to human-induced errors. In this study, a petrographic determination of building stone and a mapping model were developed based on Mask R–CNN in order to prevent human errors. To this end, the city of Konya, Turkey, which is on the UNESCO temporary heritage list, was selected for applying the proposed method. The model was trained with a total of 1800 images collected from nine historic buildings with different ornamental and building stones that constitute the cultural texture of the city. Testing of the model was conducted on the main façade of the Matbah-ı Şerif monument, consisting of 363 building stones with seven different lithologies, for different situations (resolution, shooting distance, and angles). The average precision values for the stone types trained in the model were between 89.10% and 100%, and an accurate lithology determination and map were obtained for each case. These results indicate that the proposed model can provide important bases for restoration works, with its fast and automatic mapping capability as well as its reliable and highly precise lithology determination.
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•The importance of building stone lithology in restoration works.•Determination of building stone lithology via deep learning (DL).•Automatic lithology map via Mask R–CNN.
In the reintegration applications of the destroyed blocks in stone monuments, the compatibility of the petrographic and engineering properties of the material selected for restoration with the ...original building stones provides benefits in the sustainable preservation of the structures. Infrared thermography can be an important tool in evaluating this compatibility. To this end, the restoration stones and original blocks (pyroclastic and travertine) used in Kuruçeşme Han in the city of Konya (Turkey) were examined in situ (infrared thermography, deep moisture meter, and ambient temperature meter) and in the laboratory (petrographic, index-mechanical, and thermal test) environment. To model the atmospheric events of the building stones in laboratory studies, monitoring of the capillary water rise, determination of the cooling process, and monitoring of the drying process of the saturated tests were carried out through infrared thermography. According to the results obtained from the study, it was determined that deteriorations developed due to different thermal behavior both between the pyroclastic and travertine blocks of the monument and between the restoration and the original building stones (pyroclastic and travertine). In addition, it is thought that determining the thermal behavior is very important in the selection of building stones in restoration applications.
•The importance of infrared thermography in restoration applications.•Comparison of in-situ and laboratory investigations via infrared thermography.•The importance of thermal behavior in regions with different lithologies.•The relationship of thermal differences in building blocks with deterioration.•The effect of the microclimatic environment on the deterioration process.
Vision-based periodic examination of the deterioration of stone monuments over time is labour and time intensive. Especially, in cases involving large-scale immovable cultural heritage, the workforce ...is considerably increased, along with the possibility of occurrence of errors. Any misdiagnoses in the deterioration may cause irreversible structural problems in monuments, and thus, it is necessary to develop alternative examination methods. Computer-vision methods represent an effective solution to eliminate both human errors and difficulties in the field. Therefore, this study aims to adopt the Mask R–CNN algorithm, which is a computer-vision method, to detect and map the deteriorations observed in the Gümüşler archaeological site and monastery (cracks, discontinuities, contour scaling, missing parts, biological colonization, presence of higher plants, deposits, efflorescence, and loss of fresco). First, 1740 images were collected from the site, and the model was trained by labelling the distortions in these images according to their types. Later, the model was tested on four outdoor and two indoor views. The developed model achieved an average precision ranging between 91.591% and 100%, and the mean average precision was 98.186%. These results demonstrated that the proposed algorithm can enable mapping to promptly and automatically detect the deterioration in large monuments.
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•The importance of determining types of deterioration in restoration applications.•Determination of stone deterioration with computer-vision.•Automatic deterioration map via Mask R–CNN.
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