scholarly journals EXPERT PANEL ON IN-SITU VISUAL INSPECTIONS FOR MASONRY CHURCHES MAINTENANCE STAGE

2021 ◽  
Vol 27 (6) ◽  
pp. 454-471
Author(s):  
Manuel Carpio ◽  
Jesús Ortega ◽  
Andrés J. Prieto
Keyword(s):  
Preventive Maintenance ◽  
Human Error ◽  
Expert Panel ◽  
High Dispersion ◽  
Preventive Conservation ◽  
Heritage Buildings ◽  
Conservation Actions ◽  
Input Variables ◽  
Masonry Churches

The incorporation of protocols in heritage building preservation is important for the definition of preventive conservation actions. Such integration is needed to avoid restoration actions and to promote preventive maintenance instead of corrective maintenance actions. This paper presents the application of an innovative digital management system using artificial intelligence that can quantify the suitability of a sample. This kind of application can support the maintenance management of buildings and minimise human error in data collection. The fuzzy system showed slight differences between the members of the expert panel during the in-situ visual inspection. These results indicate that, despite differences between various experts’ evaluation of a building, the proposed digital method helps minimise the uncertainty in the results. The paper highlights input variables, which present high dispersion (load state modification, fire and occupancy), and input parameters, which present low dispersion (preservation, roof design and overloads). Fuzzy systems can adequately manage the uncertainties associated with different experts’ assessment of sample that present constructive homogeneity. This study can give advantages to stakeholders during the inspection, diagnosis and evaluation stages in the improvement of mitigation policies focused on preventive maintenance programs dedicated to the resilience of heritage buildings, specifically churches emplaced in Chile.

scholarly journals Expert Panel, Preventive Maintenance of Heritage Buildings and Fuzzy Logic SYSTEM: An Application in Valdivia, Chile

2021 ◽  
Vol 13 (12) ◽  
pp. 6922
Author(s):  
Manuel Carpio ◽  
Andrés J. Prieto
Keyword(s):  
Fuzzy Logic ◽  
Human Error ◽  
Expert Panel ◽  
Fuzzy Model ◽  
Lessons Learned ◽  
Digital Tool ◽  
Building Maintenance ◽  
Large Dispersion ◽  
Input Parameters ◽  
Heritage Buildings

The maintenance of buildings is a highly complex decision process, which is generally due to professional experts having to consider several arduous evaluations, especially regarding uncertainty related to why, when and how to intervene. This study concerns the analysis of the uncertainty associated with professional experts’ surveys during the decision-making process during building maintenance. For this purpose, a case study of a timber-structure building was examined. An expert panel of 66 professionals with expertise in construction engineering carried out a systematic and automated evaluation. This kind of digital method is capable of managing the uncertainty associated with the evaluation processes by different specialists. Experts can evaluate various nuances and approximations in the model’s input parameters. The fuzzy model helps to harmonize the results since minor variations in the evaluation of the input parameters do not generate a large dispersion over the model’s output variable. The novelty of this study concerns the application of a digital methodology based on a fuzzy logic model to assist a professional expert panel in different areas—architecture, engineering and construction. This study is oriented through an artificial intelligence based method applied by specialists to set intervention priorities, support maintenance management of the examined building and minimise human error during data collection and uncertainty related to making decisions. The lessons learned from the results obtained in this study promote the use of this kind of digital tool to manage the uncertainty associated with in-situ visual inspections.

scholarly journals Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere

2021 ◽  
Vol 13 (7) ◽  
pp. 1250
Author(s):  
Yanxing Hu ◽  
Tao Che ◽  
Liyun Dai ◽  
Lin Xiao
Keyword(s):  
Machine Learning ◽  
Northern Hemisphere ◽  
Snow Depth ◽  
Learning Algorithm ◽  
Random Forest Regression ◽  
Machine Learning Methods ◽  
Long Time ◽  
In Situ Observations ◽  
Input Variables

In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets—Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series of daily snow depth over the Northern Hemisphere (NHSD) snow depth, ERA-Interim snow depth and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) snow depth—were used as input variables. The first three snow depth datasets are retrieved from passive microwave brightness temperature or assimilation with in situ observations, while the last two are snow depth datasets obtained from meteorological reanalysis data with a land surface model and data assimilation system. Then, three machine learning methods, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), were used to produce a fused snow depth dataset from 2002 to 2004. The RFR model performed best and was thus used to produce a new snow depth product from the fusion of the five snow depth datasets and auxiliary data over the Northern Hemisphere from 2002 to 2011. The fused snow-depth product was verified at five well-known snow observation sites. The R2 of Sodankylä, Old Aspen, and Reynolds Mountains East were 0.88, 0.69, and 0.63, respectively. At the Swamp Angel Study Plot and Weissfluhjoch observation sites, which have an average snow depth exceeding 200 cm, the fused snow depth did not perform well. The spatial patterns of the average snow depth were analyzed seasonally, and the average snow depths of autumn, winter, and spring were 5.7, 25.8, and 21.5 cm, respectively. In the future, random forest regression will be used to produce a long time series of a fused snow depth dataset over the Northern Hemisphere or other specific regions.

scholarly journals Gap analyses of priority wild relatives of food crop in current ex situ and in situ conservation in Indonesia

2021 ◽  
Author(s):  
Wiguna Rahman ◽  
Joana Magos Brehm ◽  
Nigel Maxted ◽  
Jade Phillips ◽  
Aremi R. Contreras-Toledo ◽  
...  
Keyword(s):  
Protected Areas ◽  
Protected Area ◽  
In Situ Conservation ◽  
Wild Relatives ◽  
Ex Situ ◽  
Area Network ◽  
Complementarity Analysis ◽  
Ex Situ Collections ◽  
Conservation Actions

AbstractConservation programmes are always limited by available resources. Careful planning is therefore required to increase the efficiency of conservation and gap analysis can be used for this purpose. This method was used to assess the representativeness of current ex situ and in situ conservation actions of 234 priority crop wild relatives (CWR) in Indonesia. This analysis also included species distribution modelling, the creation of an ecogeographical land characterization map, and a complementarity analysis to identify priorities area for in situ conservation and for further collecting of ex situ conservation programmes. The results show that both current ex situ and in situ conservation actions are insufficient. Sixty-six percent of priority CWRs have no recorded ex situ collections. Eighty CWRs with ex situ collections are still under-represented in the national genebanks and 65 CWRs have no presence records within the existing protected area network although 60 are predicted to exist in several protected areas according to their potential distribution models. The complementarity analysis shows that a minimum of 61 complementary grid areas (complementary based on grid cells) are required to conserve all priority taxa and 40 complementary protected areas (complementary based on existing protected areas) are required to conserve those with known populations within the existing in situ protected area network. The top ten of complementary protected areas are proposed as the initial areas for the development of CWR genetic reserves network in Indonesia. It is recommended to enhanced coordination between ex situ and in situ conservation stakeholders for sustaining the long term conservation of CWR in Indonesia. Implementation of the research recommendations will provide for the first time an effective conservation planning of Indonesia’s CWR diversity and will significantly enhance the country’s food and nutritional security.

Mechanically Aspirated Solar Radiation Shields: A CFD and Neural Network Design Analysis

2001 ◽  
Author(s):  
B. M. Fichera ◽  
R. L. Mahajan ◽  
T. W. Horst
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Temperature Sensor ◽  
Temperature Measurements ◽  
Analysis And Design ◽  
Computational Fluid Dynamics Analysis ◽  
Radiation Shields ◽  
Input Variables ◽  
The Relationship

Abstract Accurate air temperature measurements made by surface meteorological stations are demanded by climate research programs for various uses. Heating of the temperature sensor due to inadequate coupling with the environment can lead to significant errors. Therefore, accurate in-situ temperature measurements require shielding the sensor from exposure to direct and reflected solar radiation, while also allowing the sensor to be brought into contact with atmospheric air at the ambient temperature. The difficulty in designing a radiation shield for such a temperature sensor lies in satisfying these two conditions simultaneously. In this paper, we perform a computational fluid dynamics analysis of mechanically aspirated radiation shields (MARS) to study the effect of geometry, wind speed, and interplay of multiple heat transfer processes. Finally, an artificial neural network model is developed to learn the relationship between the temperature error and specified input variables. The model is then used to perform a sensitivity analysis and design optimization.

Artificial intelligence applied to the preventive conservation of heritage buildings

2019 ◽  
pp. 245-249
Author(s):  
A.J. Prieto ◽  
R. Ortiz ◽  
J.M. Macías-Bernal ◽  
M.J. Chávez ◽  
P. Ortiz
Keyword(s):  
Artificial Intelligence ◽  
Preventive Conservation ◽  
Heritage Buildings

In situ synthesis of Au–Pd bimetallic nanoparticles on amine-functionalized SiO2 for the aqueous-phase hydrodechlorination of chlorobenzene

RSC Advances ◽  
2014 ◽  
Vol 4 (89) ◽  
pp. 48254-48259 ◽  
Author(s):  
Xinkui Wang ◽  
Qinggang Liu ◽  
Zihui Xiao ◽  
Xiao Chen ◽  
Chuan Shi ◽  
...  
Keyword(s):  
Electronic Properties ◽  
Aqueous Phase ◽  
Reduction Method ◽  
Bimetallic Nanoparticles ◽  
In Situ Synthesis ◽  
High Dispersion ◽  
Amine Functionalized

The homogeneous Au–Pd NPs have been prepared through a facile in situ reduction method. The optimal AuPd1.0/SiO2 catalyst could complete the conversion of chlorobenzene due to the high dispersion and modified electronic properties of Pd.

In-Situ Static and Dynamic Testing and Numerical Modelling of the Dome of the Siena Cathedral (Italy)

2015 ◽  
pp. 85-114
Author(s):  
Gianni Bartoli ◽  
Michele Betti ◽  
Saverio Giordano ◽  
Maurizio Orlando
Keyword(s):  
Numerical Model ◽  
Numerical Modelling ◽  
13Th Century ◽  
Structural Behaviour ◽  
Destructive Testing ◽  
Dynamic Vibration ◽  
Heritage Buildings ◽  
Siena Cathedral ◽  
Vibration Tests

The chapter reports on the in-situ experimental campaign and the numerical modelling that were performed to assess the static and dynamic behaviour of the Cupola of the Siena Cathedral in Italy: an irregular polygonal masonry structure built in the 13th century and composed of two domes. The research was motivated by the failure of some of the stone-trusses which connect the two masonry domes and consists of: a) single and double flat-jack tests in the internal dome, b) dynamic vibration tests on the Cupola under environmental (wind) and artificial (vibrodyne) loads and c) dynamic vibration tests on the double colonnade located below the Cupola (hammer impact tests). Results of tests were employed to identify a numerical model of the Cupola, which allowed to simulate its structural behaviour and to account for the failure of the stone-trusses between the two domes. The numerical model was later extended to the whole Cathedral. Through the discussion of an emblematic case study, the chapter shows a careful application of non-destructive testing (NDT) and numerical modelling in the field of assessment (and rehabilitation) of heritage buildings.

scholarly journals An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2736 ◽  
Author(s):  
Jueying Bai ◽  
Qian Cui ◽  
Wen Zhang ◽  
Lingkui Meng
Keyword(s):  
Soil Moisture ◽  
Vegetation Cover ◽  
Land Surface ◽  
Feature Space ◽  
Entire Study ◽  
Vegetation Indexes ◽  
Input Variables ◽  
Entire Study Area ◽  
Infrared Data

A method is proposed for the production of downscaled soil moisture active passive (SMAP) soil moisture (SM) data by combining optical/infrared data with synthetic aperture radar (SAR) data based on the random forest (RF) model. The method leverages the sensitivity of active microwaves to surface SM and the triangle/trapezium feature space among vegetation indexes (VIs), land surface temperature (LST), and SM. First, five RF architectures (RF1–RF5) were trained and tested at 9 km. Second, a comparison was performed for RF1–RF5, and were evaluated against in situ SM measurements. Third, two SMAP-Sentinel active–passive SM products were compared at 3 km and 1 km using in situ SM measurements. Fourth, the RF5 model simulations were compared with the SMAP L2_SM_SP product based on the optional algorithm at 3 km and 1 km resolutions. The results showed that the downscaled SM based on the synergistic use of optical/infrared data and the backscatter at vertical–vertical (VV) polarization was feasible in semi-arid areas with relatively low vegetation cover. The RF5 model with backscatter and more parameters from optical/infrared data performed best among the five RF models and was satisfactory at both 3 km and 1 km. Compared with L2_SM_SP, RF5 was more superior at 1 km. The input variables in decreasing order of importance were backscatter, LST, VIs, and topographic factors over the entire study area. The low vegetation cover conditions probably amplified the importance of the backscatter and LST. A sufficient number of VIs can enhance the adaptability of RF models to different vegetation conditions.

Use of hygrothermal modeling to validate the application of an open-cell spray-foam insulation application to a high-rise heritage building in Winnipeg, Manitoba

2019 ◽  
Vol 46 (11) ◽  
pp. 1021-1031 ◽  
Author(s):  
John A. Wells ◽  
Robert Spewak
Keyword(s):  
Sensor Data ◽  
Analysis Tool ◽  
Building Stock ◽  
Accurate Analysis ◽  
Open Cell ◽  
Existing Building ◽  
Moisture Sensor ◽  
Heritage Buildings ◽  
Interior Face

The increasing cost of new building construction has made repurposing existing building stock economically more viable compared with a green site new build. In addition to capital cost benefits, increasing urban densification through repurposing existing buildings is one of the solutions for enhancing the urban environment. This paper summarizes the investigative work completed to substantially improve the energy efficiency of a heritage 10 storey building in Winnipeg, Manitoba. The investigative work for the remediation involved hygrothermal modeling to rationalize the introduction of thermal insulation to the interior face of the exterior walls. Based on the modelling, an open-cell spray foam was applied to the interior face of the masonry walls. Temperature, moisture, and relative humidity sensors were placed in various locations throughout the building to monitor the exterior walls, primarily to identify if deleterious levels of moisture were accumulating in the masonry. The results were also used to compare the in-situ performance of the building with the predicted performance from the hygrothermal models. The investigation confirmed that obtaining accurate in-situ moisture readings in masonry products is significantly impacted by initial internal moisture levels, necessitating pre-test calibration. Regardless, moisture sensor data accumulated to date indicate that after six years of monitoring, deleterious levels of moisture in the masonry and plaster are not occurring in the exterior walls, which is in good agreement with hygrothermal model results for open-cell foam. The results of this investigation confirm that the implementation of hygrothermal modeling is an effective and accurate analysis tool in the long-term durability assessment of building envelopes for heritage buildings.

scholarly journals Characterization of Polyamide 6/Multilayer Graphene Nanoplatelet Composite Textile Filaments Obtained Via In Situ Polymerization and Melt Spinning

Polymers ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1787
Author(s):  
Jelena Vasiljević ◽  
Andrej Demšar ◽  
Mirjam Leskovšek ◽  
Barbara Simončič ◽  
Nataša Čelan Korošin ◽  
...  
Keyword(s):  
Melt Spinning ◽  
In Situ Polymerization ◽  
Complex Viscosity ◽  
Polyamide 6 ◽  
High Dispersion ◽  
Multilayer Graphene ◽  
Main Challenge ◽  
Dispersion State ◽  
Continuous Filament

Studies of the production of fiber-forming polyamide 6 (PA6)/graphene composite material and melt-spun textile fibers are scarce, but research to date reveals that achieving the high dispersion state of graphene is the main challenge to nanocomposite production. Considering the significant progress made in the industrial mass production of graphene nanoplatelets (GnPs), this study explored the feasibility of production of PA6/GnPs composite fibers using the commercially available few-layer GnPs. To this aim, the GnPs were pre-dispersed in molten ε-caprolactam at concentrations equal to 1 and 2 wt %, and incorporated into the PA6 matrix by the in situ water-catalyzed ring-opening polymerization of ε-caprolactam, which was followed by melt spinning. The results showed that the incorporated GnPs did not markedly influence the melting temperature of PA6 but affected the crystallization temperature, fiber bulk structure, crystallinity, and mechanical properties. Furthermore, GnPs increased the PA6 complex viscosity, which resulted in the need to adjust the parameters of melt spinning to enable continuous filament production. Although the incorporation of GnPs did not provide a reinforcing effect of PA6 fibers and reduced fiber tensile properties, the thermal stability of the PA6 fiber increased. The increased melt viscosity and graphene anti-dripping properties postponed melt dripping in the vertical flame spread test, which consequently prolonged burning within the samples.

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