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Read the invisible pores in CT images
Read the invisible pores in CT images - A novel method for estimating porosity and homogeneity of porous materials -
Porous materials are ubiquitous—from ceramics to soils and rocks to human bone and various components, that are everywhere in daily life and are critical to virtually medical and all industrial construction and energy processes. Many construction materials are porous, such as typical concrete and cement. In recent years, with increasing interest in the lunar exploration and base construction, building materials manufactured through sintering of the in situ resources (i.e., lunar regolith) and characterization on the sintered porous materials have attracted more and more attention. Most porous materials are composed of matrix and pores at microscale or nanoscale which are invisible to human eyes. Most people heard of CT (Computed Tomography) scan, when they are getting the physical examination in the hospital. X-ray CT has been widely used to analyze porous materials for its major advantage of quantitative measurement of pore structures even at nanoscopic scale. The Korea Institute of Civil Engineering and Building Technology (KICT, President Kim Byung-Suk) owns a high-tech multi-tube industrial CT scanner, which has been successfully applied in various research fields and provided services for many research entities and enterprise in domestic and overseas. Over years’ experience in running CT scanning and image processing lay a sound foundation for developing new analysis method of material characterization. The KICT announced that the research team led by Dr. Hyusoung Shin has developed a new method, named the statistical phase fraction (SPF) method, to estimate porosity and to evaluate homogeneity of porous materials dominated by sub-resolution pores via CT image analysis.A traditional and the simplest segmentation technique is the binarization method, where a threshold pixel intensity value is selected to divide the image into two portions and pores are usually grouped into the portion that has pixel values smaller than the threshold. However this approach can only deal with pore size that is great than or equal to 1 pixel. The research team introduces a new term of “Mixel” (Fig. 1), which represents a pixel or a voxel consisting of two or more phases. This method employs Gaussian function fitting on the CT histogram and the CT number for each single phase (e.g., air, water, pure solid) that was included in the sample. The total porosity for any given bulk volume with irregular shape can be estimated. Based on a lot of trial and error crossing many years, this method has been successfully applied in estimation of porosity and evaluation of homogeneity of sintered lunar regolith simulant, which is expected to be a candidate construction material for future moon construction (Fig. 2). Dr. Li Zhuang from KICT commented that “The SPF method has a big advantage over existent methods that it can estimate local porosity for any arbitrary part of a given sample without destroying the sample. This is extremely useful for evaluation of sample homogeneity.” The research team saw great potential in the newly developed SPF method for its applications to other porous fine-grained materials, such as bentonite and engineered cement used for construction materials of nuclear waste disposal facilities.
###The Korea Institute of Civil Engineering and Building Technology (KICT) is a government sponsored research institute established to contribute to the development of Korea’s construction industry and national economic growth by developing source and practical technology in the fields of construction and national land management.This research is supported by the R&D project “Development of environmental simulator and advanced construction technologies over TRL6 in extreme conditions” backed by the Ministry of Science and ICT. An article explaining the results of this research was published in the Scientific Reports, a renowned international journal for the multidisciplinary topics in September 2022.porous materials -
Regdate 2023/03/15
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A Renewable Energy-Based Bi-directional Heat Trade System
A Renewable Energy-Based Bi-directional Heat Trade SystemReducing Carbon Emissions by Leveraging Excess Heat from Buildings
6-Mar-2023 7:00 AM EST, by National Research Council of Science and Technologyfavorite_borderCredit: Korea Institute of Civil Engineering and Building TechnologyPhoto of demonstration facilityPreviousNextNewswise — Global energy trends are shifting toward Digitalization, Decarbonization, and Decentralization. Global warming and climate anomalies attributable to environmental pollution featured in the daily news indicate that the threats of climate change are no longer far-fetched. According to a report published by the Intergovernmental Panel on Climate Change (IPCC), carbon emissions from urban areas in 2020 accounted for 67% to 72% of the total emissions. Globally, efforts are being made to reduce carbon emissions from urban areas and buildings through provision of renewable energy and construction of zero-energy buildings.A research team at the Department of Building Energy Research of the Korea Institute of Civil Engineering and Building Technology (KICT, President Kim Byung-suk), has developed a bi-directional heat trade system that utilizes excess heat from renewable energy including solar heat in an effort to achieve carbon neutrality in buildings.Solar heat, geothermal heat, and fuel cells are increasingly used either individually or together in hybrid systems to reduce the heating and cooling energy used in buildings. However, such systems are likely to produce excess heat due to mismatch between the building’s heat demand and the renewable-based heat supply. Excess heat, or waste heat, is heat that is not used after intermittent heat production. For example, during the spring in Korea when solar radiation is adequate, sufficient heat can be generated. However, the season does not require a high level of heating, thereby leading to waste of some of the heat. The research team, led by Research Fellow Yongki Kim, developed a system that helps buildings trade the excess heat bi-directionally via heating pipes after self-consumption in areas where there are high concentrations of buildings.The team configured a network of heating pipes for the three buildings of the KICT located in Ilsan, Korea and used an array of solar and geothermal heat and fuel cells as the sources of heat. Two 944m2 solar thermal collectors were installed in the outdoor parking lot and on the rooftop, and a heat pump for a 310kWth geo-thermal heat source, a 10kWp fuel-cell system and two thermal storage facilities of 40m3 and 10m3 capacities were built.The simulation and the proof of concept proved that the twin pipe is effective in the network of low-temperature heat pipes with about 10% heat loss. When there is enough sunlight, hot water for heating is supplied by solar heat to the secondary pipe of district heating through a heat exchanger. When there is insufficient sunlight, hot water can be supplied through the heat pump system of the geothermal source and the fuel-cell system. The bi-directional heat trade system can be controlled both manually and automatically at the integrated control center.Renewable heat energy applied to a building is usually for self-consumption. In a small-scale district heating system, supply and demand facilities are separated and heat is supplied unidirectionally. In this study, the bi-directional heat trade system was implemented, improving the utilization rate of renewable heat source facilities and system efficiency.Yongki Kim, the head of the research team said, “The system has potential to increase the use of renewable heat energy in cities and buildings, which will ultimately reduce their carbon emissions.”
Regdate 2023/03/07
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Safety technology for hydrogen infrastructure in underground space Safety technology for hydrogen infrastructure in underground spaceActive control system to minimize impact from hydrogen leaks and blastsPeer-Reviewed PublicationNATIONAL RESEARCH COUNCIL OF SCIENCE & TECHNOLOGYPrintEmail App IMAGE: HYDROGEN EXPLOSION TEST WAS CONDUCTED IN ENCLOSURE. HERE, ENCLOSURE IS COMPRISED OF REINFORCED CONCRETE AND ALMOST SEALED. HYDROGEN SUPPLIED INTO ENCLOSURE WAS 20%. THIS SERIES OF PHOTO SHOWS STRUCTURE DAMAGE DUE TO EVOLUTION OF DEFLAGRATION IN ENCLOSURE JUST AFTER IGNITION. view more CREDIT: KOREA INSTITUTE OF CIVIL ENGINEERING AND BUILDING TECHNOLOGYAs an energy source that would help countries achieve carbon neutrality and energy security, hydrogen energy is being sought after globally as the energy source of the future. To this end, the European Union(EU) has introduced its strategy on hydrogen, implementing its plan to invest €470 billion(623 trillion Korean won) in 10 years to build a hydrogen-based society in the region. Germany, one of the most ardent supporters of global green initiatives, has put forward a national hydrogen strategy to invest a total of 1.2 trillion Korean won by 2030. The South Korean government is also investing in hydrogen city projects and infrastructure construction to inch closer to getting the hydrogen economy up and running.The Korea Institute of Civil Engineering and Building Technology (KICT, President Kim Byung-suk) announced its plan to develop technologies pertaining to the entire course of an underground hydrogen infrastructure project, from its design and construction to its operation and management. Such technologies would fundamentally improve the safety of hydrogen facilities. The construction of new infrastructure in the CBD area may bring a more efficient integration with other renewable energy networks and help the development of source technologies for hydrogen infrastructure construction, technologies for which South Korea has depended on sourcing from other advanced countries. Safe and reliable infrastructure is crucial to the establishment of a hydrogen ecosystem. However, any ground-level hydrogen facility project tends to face fierce opposition from local residents, and the alternative of building them peripherally makes the project less cost-effective and efficient. Dr. Kim Yangkyun of the Hydrogen-infrastructure Research Cluster at KICT has developed the core safety engineering technologies for building reliable hydrogen infrastructure underground along with an active control system to mitigate the impact of possible hydrogen leaks and blasts. The new system can help control the ambient hydrogen concentration within an underground facility at all times via forced ventilation and can reduce risk up to 80% compared with similar above-ground facilities thanks to the introduction of roof-type vents that minimize blast overpressure in times of an emergency. Basically, any underground hydrogen infrastructure is an enclosed space. All risks of a potential blast should be eliminated by keeping the ambient hydrogen concentration below the Lower Flammable Limit (LFL) whenever a leak occurs. The active control system that Dr. Kim Yangkyun’s research team proposed maintains the quality of the atmosphere of the enclosed space to a normal level and can prevent blast accidents at times of emergency hydrogen gas leaks. An optimized interpretation was used, including multiple factors (shape, location, intake, and outtake capacities of the inlet/outlet) to formulate the conditions for ordinary times and for an emergency where the concentration of hydrogen gas in the facility is kept below the LFL or 4% of hydrogen by volume. If the active control system malfunctions and an explosion occurs, such an impact should be minimal. The roof-type vent of the deflagration venting system can reduce damage from blast overpressure inside the facility to a 20th. The real-scale experiment of vented deflagration conducted at KICT in 2021 showed that the maximum overpressure reduction effect could be obtained due to a sudden drop in blast overpressure when the explosion vent is bigger than the vent coefficient standard of 2.2. The effectiveness remained constant regardless of the hydrogen concentration or point of the deflagration. Another model was presented to calculate the size of the roof-type vent for the safe design of the underground hydrogen facility. The improved model was built on the minimum vent size model specified in guide NFPA68 of the US National Fire Protection Association to apply to underground hydrogen facilities. The research team focused on the fusion of functions: ventilations in normal time and after a blast accident. Dr. Kim Yangkyun, the head of the research team said, “The dual system of active control ventilation and the roof type vent is an integrated security technology for both emergency and non-emergency situations responding to all risks incurred in an accident by making the most of the limited cross-section area of the vent.” Regdate 2023/02/27
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New way to predict the damage and aging of bridges by using D.N.A. technologies New way to predict the damage and aging of bridges by using D.N.A. technologiesProviding a diverse range of bridge maintenance information services via a platform that offers aging predictions based on AIPeer-Reviewed PublicationNATIONAL RESEARCH COUNCIL OF SCIENCE & TECHNOLOGYPrintEmail App IMAGE: THIS IS A PROCESS OF SETTING THE CARBONATION DEPTH-RELATED DATA COLLECTED IN REGIONS A AND B AS BASIC DATA AND GENERATING A FUTURE PREDICTION CURVE FOR CARBONATION DEPTH THROUGH A TRAINING PROCESS USING ARTIFICIAL INTELLIGENCE TECHNOLOGY. view more CREDIT: KOREA INSTITUTE OF CIVIL ENGINEERING AND BUILDING TECHNOLOGYThe Korea Institute of Civil Engineering and Building Technology (KICT, President Kim Byung-Suk) announced that it has developed the D.N.A. (Data, Network, and AI) technologies to predict the levels of damage and aging of bridges for preventive maintenance. As of 2021, the percentage of Korean bridges aged 30 years or more stands at a relatively low 12.5%. However, this ratio is expected to increase in the next decade to 39.3% by 2031 and rapidly spike up to 76.1% in 20 years. For the preemptive management of these aging bridges, the levels and characteristics of performance deterioration for each bridge need to be understood by accumulating comprehensive and strategic data as well as technology that can predict the degree of obsolescence of the bridges based on the collected data. A KICT research team within the Department of Structural Engineering Research, led by Dr. Ki-Tae, Park, has garnered more than 5 million data elements either directly or indirectly related to aging of bridges from 2021 to 2022. An AI learning model was applied on the established data to develop a prediction curve algorithm that can forecast the spread of damage over time, including a carbonation model of the bridges. The credibility of the technology was further improved by securing additional data on the bridges using IoT technology onsite and from experimental data that considers the environmental conditions of Korea. The developed bridge aging prediction technology incorporated artificial intelligence technologies to analyze aging data in order to forecast the future level of damage to the bridges. The prediction accuracy of the aging level assessment algorithm stands at 90.8% as of the end of 2022, which is expected to be further improved up to 95% by 2023. Within the international technical level, academic research is in progress and it was investigated with accuracy level of about 85% as of 2021. KICT plans to provide the results derived from the developed bridge aging prediction technology via a platform where multiple customers can utilize the data. The BMAPS(Bridge Maintenance-Aided Platform Service) platform will offer prediction results and diverse bridge maintenance information services, such as the load-carrying capacity (the ability to support weight) predictions for aging small and medium-sized bridges. The platform will be open to the public in 2nd half of 2023. The platform will be offered in Korean for its use within Korea. In consideration of the development status, an English version of the platform will be prepared by 2024, through which international users can also benefit from the platform as a source of reference. As the research findings can be used as an objective data source to calculate the maintenance costs of bridges, it will significantly contribute to the preventive maintenance of bridge facilities, thereby reducing massive potential maintenance costs that may occur in the future. Dr. Ki-Tae, Park, the lead researcher, commented that “securing preventive maintenance information on bridges by using various data, AI, and network-based platform technologies will contribute to the longevity of bridges” and further added, “that the prediction services will expand in the future to include not only bridges but a wide range of infrastructure.” The research was initiated by the support of the Ministry of Science and ICT and developed under a major KICT initiative, “Development of a Smart Maintenance Platform and Utilization Technologies for Old Bridge Structures based on D.N.A. (2021~2023).” Regdate 2023/02/21
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New Way To Predict Deadly Rip Currents At The Beach
New Way To Predict Deadly Rip Currents At The BeachSurge in rip current deaths prompts calls for better prediction technology
15-Feb-2023 7:00 AM EST, by National Research Council of Science and Technologyfavorite_borderCredit: KOREA WATER RESOURECES ASSOCIATIONThe procedure to compute the risk index in the rips prediction system with a ocean forecast model.PreviousNextNewswise — Rip currents are a serious threat to beachgoers at any coast around the world. There are reported number of fatalities caused by rip currents every year in the U.S. and Australia. According to Surf Lifesaving Australia, rip currents were responsible for at least 21 drowning deaths per year.Historically, it has been difficult to measure rip currents that are mostly invisible and random in time and location. And its prediction involves limitations due to weather and ocean forecasts as well as the rip current model. In other words, it is difficult to predict its location as well as its occurrence, especially for transient rip currents, so-called flash rips that can be observed in directional random wave environments. Therefore, the prediction of rip currents is still an ongoing issue in the ocean forecasting field.The Korea Institute of Civil Engineering and Building Technology (KICT, President Kim, Byung-Suk) has announced a new approach for predicting the rip currents. A research team, led by Dr. Junwoo Choi of Department of Hydro Science and Engineering Research, developed a new prediction system which has already been tested at ten beaches in South Korea through a project funded by the Korea Hydrographic and Oceanographic Agency. Ten beaches including Haeundae beach in Busan, showed higher than 80% of accuracy rate. No fatality has been reported at the beaches during its operation.The advanced rip-current prediction system implementing the new approach produces a sequence of rip-current risk index at a specific coast. The prediction system can be operated with an ocean observation system or an ocean forecasting system to support the time-varying inputs of wave height, wave period, wave direction, spectral spreading, and tidal elevation.The rip-current risk index is computed by a function of the rip-current likelihoods varied according to the six parameters which are established by utilizing in-advance numerical simulations of rip currents at each individual coast. Note that the six parameters are the input conditions of the wave-current model, FUNWAVE (published by Univ. of Delaware) that was employed because it can solve flash rips induced by phase-resolving directional random waves.The performance of the present approach has been checked by operating the rip-current prediction system with a real-time ocean observation system at ten popular beaches in South Korea. Dr. Junwoo Choi said, “The needs of a rip-current prediction system is clear and explicit, and the risk index can help lifeguards and save swimmers in the coast covered by the present rip-current system.”
Regdate 2023/02/15
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First Step for Smart Port Facilities, Maintain Fenders with Drone & AI combination
First Step for Smart Port Facilities, Maintain Fenders with Drone & AI combinationA new method for auto-segmentation of fenders from numerous drone images
30-Jan-2023 7:00 AM EST, by National Research Council of Science and Technology4favorite_borderCredit: Korea Institute of Civil Engineering and Building TechnologyThe encoder-decoder structure reduces and increases the size of the feature map using the stride convolution modules and pixel shuffle.PreviousNextNewswise — With the advent of the fourth industrial revolution, there is an increasing need throughout the globe for the maintenance of port facilities by utilizing drones. Moreover, it has become more essential to ensure proactive maintenance of port facilities to secure their sustainable safety and serviceability since the number of aging port facilities in Republic of Korea, which are to exceed 30 years of service life by 2030, is expected to increase by about 50%.In particular, it is critical in terms of port operations to ensure the safe docking of ships for loading and unloading purposes. Fenders perform a critical role in these situations. Fenders are installed on the sea side of the superstructure of quay wall to prevent damage on vessel hull and structure caused by the force of the ship berthing and frictional force. However, since most fenders are inaccessible via land directly, inspectors should commonly approach by using floating boats and visually inspect the condition of the fenders. It is very dangerous, time-consuming, and difficult to obtain detailed damage information due to sea waves and other risks.The Korea Institute of Civil Engineering and Building Technology (KICT, President Kim, Byung-Suk) has announced a new inspection approach to automatically detect fenders incorporating an AI model and a vision sensor on the unmanned aerial vehicle. It especially utilized a deep learning network with the densely connected encoder–decoder format. It is one of the networks widely used for pixel-level object detection, inspired by the eccentric function of the human vision.The AI algorithm, developed by Department of Structural Engineering Research of KICT, research team led by Dr. Min, Jiyoung, was named ‘densely connected receptive field pyramid (DRFP)’ or ‘tiny version of DRFP (DRFPt)’. It aimed to precisely and quickly extract fenders in the pixel-level from numerous UAV images. In order to efficiently search a wide area at once and to reduce the computational complexity, the standard convolution and the dilated convolution were densely connected in a pyramid form. And a dataset of fenders was collected by using UAV on various port facilities. The detection performance of the proposed model was compared to the other deep learning models in literature. The results showed that the proposed model reliably detected fenders in images taken from various angles, with IoU and F1 scores exceeding 88%, despite changes in the color or shape caused by the tide. Here, IoU (Intersection over Union) means the ratio of the overlap area to the combined area of estimation and ground truth. F1 score is a statistical measure of the accuracy of a test. 100% means perfect overlap and accuracy.There are numerous risk factors in every nook and cranny of port facilities that pose potential threats to the inspectors. Therefore, many port authorities are actively attempting to adopt new remote inspection technologies such as UAVs (unmanned aerial vehicles) and USVs (unmanned surface vehicles), both to ensure the safety of the inspectors and to facilitate their detailed and quantitative inspections on structural members that are hard to access via land. These unmanned vehicles are typically equipped with vision sensors through which they continually record video footage or single photographs as they continue to maneuver around the structure.Considering the massive scale of port structures that extend many kilometers, the original data size of video recordings at high resolutions is usually too large for regular computers to manage. For example, about 4,000 aerial photographs taking up 50GB of storage were collected in a 1.25km stretch of capping concrete and main caisson structure at Incheon Port in Republic of Korea, which were captured by a 4k camera with 50% overlapping carried on a drone. Thus, to ensure effective management of the massive aerial photograph data over time, it is important to quickly extract only the target objects that require maintenance from the photos or videos and to store and manage the necessary quantitative information on the condition of the target objects.Main researcher Dr. Min, Jiyoung said, “We are planning to upgrade this model to the fender health inspection system. It will enable us to quantitatively detect damage such as missing sections or cracks from only UAV images. This UAV-AI combination technology will automatically evaluate the fender serviceability in the future, securing the safety of inspectors and reducing the time cost in the field.”
Regdate 2023/01/30
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How a LDM-Service Platform Makes Automated Driving Better?
○ Source: Newswise○ URL: https://www.newswise.com/articles/how-a-ldm-service-platform-makes-automated-driving-better
How an LDM-Service Platform Makes Automated Driving Better?An I2V LDM-service platform for seamless automated driving19-Dec-2022 7:00 AM EST, by National Research Council of Science and Technologyfavorite_borderCredit: Korea Institute of Civil Engineering and Building TechnologyLDM-service platformPreviousNextNewswise — The day has come. Today, we can easily see people driving automated car on the road; not just in the Sci-Fi or 007 movies. The Korea Institute of Civil Engineering and Building Technology (KICT, President Kim, Byung-Suk) has developed a platform that make an automated driving vehicle overcome risks through dynamic information from infrastructure.In the case of automated driving in urban areas, surrounding cognition failure can occur when a pedestrian is shaded by a bus or a roadside tree. That is a potential risk factor of automated driving, which becomes an obstacle to the realization of fully automated driving. A research team in KICT, led by Dr. Hyoungsoo Kim, has conducted a study to develop an I2V LDM (Local Dynamic Map) service platform that recognizes and shares potential risk factors in the shaded area that automated vehicles cannot do. Following study was funded by Korea Agency for Infrastructure Technology Advancement (KAIA) and the host organization of the study was Korea Transportation Safety Authority (TS). The platform recognizes and tracks vehicles, motorcycles and pedestrians at an intersection and provides location information messages for automated vehicles in real time. The LDM platform was designed as six types of modules. The roadside detecting module produces information from detectors and traffic signals installed at each intersection. The roadside data processing module transmits the information generated at the intersection to the automated vehicle and the center in the form of messages. In the center (processing module and prediction module), LDM database management and prediction information are generated, and then transmitted to the automated vehicle. The vehicle interface module informatizes the message received from the roadside data processing module into information and transmits it to the vehicle with monitoring in the evaluation module.Demonstration of the designed platform was conducted. Detectors and traffic signal transmission boards were installed at urban intersections, and SPaT, MAP, RSA, and TIM of SAE (Society of Automotive Engineers) J2735 messages were provided from roadside data processing devices. In the scenario, the LDM platform recognizes a jaywalking boy who is shaded by the parked vehicle, and delivers RSA messages containing a pedestrian location information to the automated vehicle to prevent an accident. The RSA message was successfully transmitted to the vehicle every 10 Hz (0.1 sec.). Considering that the 50 km/hr vehicle moves 1.4 m/0.1 sec., the performance of this platform is believed to be close to the human driver’s cognitive response time. Dr. Kim’s research team noted that the proposed platform is designed to share dynamic information between infrastructure and vehicles in real time. Dr. Hyoungsoo Kim said that “KICT is conducting an infrastructure guidance study that induces safe driving by guiding vehicles in conflict sections as a follow-up to this study.”
Regdate 2022/12/15
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New AI technology to Measure the Noisiness of Upstairs Neighbors
Source: NewswiseURL: https://www.newswise.com/articles/new-ai-technology-to-measure-the-noisiness-of-upstairs-neighborsNew AI technology to Measure the Noisiness of Upstairs NeighborsA new method for predicting footstep sounds of upstairs residents using a CNN model
14-Dec-2022 7:00 AM EST, by National Research Council of Science and Technology1favorite_borderCredit: Korea Institute of Civil Engineering and Building TechnologyApartment floor plan and experimental setup for measuring footstep vibration and soundsPreviousNextNewswise — Some people can’t sleep well because of the noise from above: Noisy upstairs neighbors. In South Korea, these sleepless nights happen in many places because of the noise from upstairs neighbors. Living in the apartment units means dealing with a level of noise from the neighborhood on a daily basis.The Korea Institute of Civil Engineering and Building Technology (KICT, President Kim, Byung-Suk) has announced a new approach for predicting the footstep sounds of upstairs residents using a convolutional neural network(CNN) model based on vibration signals. The CNN models are widely applied in computer vision tasks. The vibration sensors are designed to be installed on the wall and floor slab of a residential building to monitor footstep-induced vibration in real–timeUpstairs floor noise causes stress to occupants and leads to conflicts between neighbors. According to a survey conducted by Korea Environment Corporation in 2022, the most common noise complaints sources in apartment units are footsteps, accounting for 67.2%. Furthermore, hammering rated 10.6%, and furniture dragging sound showed 5.5%. The biggest sources of neighbor noise in apartment units, which are mostly box-frame reinforced-concrete structures, are heavy-weight impact sound. However, there isn’t any method to obtain objective sound information.Numerical methods, such as statistical energy analysis and finite element analysis can be considered for predicting heavy-weight impact sounds. However, it is difficult to predict the sound when the material properties of the structure are complex or the constraint conditions are diversified. Furthermore, physics-based models require several hours for computation. The presented algorithm is a method for predicting the actual impact sound, especially footsteps in the rooms of buildings. A dataset was experimentally collected and its performance was compared according to the location of the vibration sensors and the resolution of the short-time Fourier transform (STFT) feature, which represents footstep-induced vibrations. The sound level for 2 s was predicted with 0.99 dB as the mean absolute error. When complaints about inter-floor noise are raised, there is insufficient evidence of sound level or which house occupants made the sound. Therefore, third parties who mediate disputes about the inter-floor noise disturbances between the neighbors, have to make decisions based on the subjective opinions of the person who filed the complaint and the neighbors who were suspected of making the sounds. However, in the future, an ‘impact monitoring system' that predicts sound based on vibration could be beneficial to alter the behavior of the neighbors causing the excessive sound. Or, it is possible that the stored data can be used by mediators in case of disputes to identify the sound source household and assess the disturbance. Main researcher Shin noted that it is more important to accumulate occupants’ perceived quality of inter-floor noise and indoor sound environments under actual living conditions. It can be used as basic data for identifying the sound perceived as ‘noisy’ by the neighbors. Shin said that “To reduce problems caused by noise between floors, it is important to quantify the noise exposure to occupants. This AI-based technology will make effective monitoring of the inter-floor noise so people will less suffer from neighbors’ noise in the future.”
Regdate 2022/12/14
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Predicting future landscape of a river
Source: EurekAlart!URL: https://www.eurekalert.org/news-releases/973487NEWS RELEASE 8-DEC-2022Predicting future landscape of a riverA eco-morphodynamic modelling was performed to predict the future landscape evolution of an actual sandy, monsoon-driven riverReports and ProceedingsNATIONAL RESEARCH COUNCIL OF SCIENCE & TECHNOLOGYPrintEmail App IMAGE: VEGETATION DYNAMICS IN 2016 view more CREDIT: KOREA INSTITUTE OF CIVIL ENGINEERING AND BUILDING TECHNOLOGYClimate change is changing the environmental condition of rivers; hence, it is no longer possible to manage modern rivers with methods that have been practiced under the past environmental conditions. A joint research team, Korea Institute of Civil Engineering and Building Technology(KICT) and Deltares of the Netherlands, conducted a research on prediction of the future changes in river landscapes using an eco-morphodynamic model applied to an actual river. According to the study result, the vegetation cover increases continuously until 2031, and the area covered by willow trees occupies up to 20% of the river area. Using this modeling, efficiency in river management can be achieved by planning management practices in advance. Eco-morphodynamic model developed by Deltares is a model that combines a vegetation model with Delft3D software, which is widely used in the field of river hydraulics. The Delft3D computes flow velocity, water depth and elevation of a riverbed. Then the vegetation model simulate the germination, settlement, growth and mortality of vegetation based on the Delft3D computation. Simultaneously, vegetation properties are converted to flow resistance and fed back into Delft3D. KICT and Deltares applied the eco-morphodynamic model to Naeseongcheon Stream in Korea which belongs to a temperate monsoon climate region with large seasonal hydrological fluctuations. Most of the Naeseongcheon Stream has characteristics as a natural river. As its riverbed is mainly composed of sand, the movement due to hydrological fluctuations and consequently, the vegetation dynamics are active. KICT has been conducting long-term monitoring including LiDAR and hydrological surveys and vegetation map production since 2012, before significant vegetation establishment in Naeseongcheon Stream began. These monitoring data were used to build and verify the eco-morphodynamic modelling. The modelling area is approximately 5 km long curved reach, located in the middle-lower section of the Naeseongcheon Stream. The width is approximately 300 m, and the grid of the model was constructed considering the actual vegetation distribution which had occurred narrowly along the shoreline. After conducting a modelling for the past data(2012-2019 period), the results were compared with the observed data. Compared with the ratio of coverage of tree species in the land cover map made with aerial photos, the area fraction of willow trees in the model result had similar coverage ratio (In 2014, actual : 2.02%, model : 2.21%). In 2016, the model adequately reproduced the actual situation by simulating the survival and growth of vegetation in the spring and the mortality of vegetation after the flood. Considering climate change scenario, the joint research team has performed a long-term modelling from 2012 to 2031. The vegetation cover continued to increase until 2031, and the area of trees reached 20% in 2031. This eco-morphodynamic model, jointly performed by KICT and Deltares, is a fully coupled model that links the hydrology-vegetation-morphololgy and able to reproduce the actual phenomenon better than other models. It has the advantage of increasing the model's reliability through application and verification in the actual river with abundant observed data. With this model, we can predict future changes in river landscape as well as ecosystem diversity and potential flood risks due to vegetation development. Dr. Lee said “This eco-morphodynamic model is able to aid decision making for implementing appropriate river and vegetation management by simulating the landscape of future rivers according to climate change, though it needs continuous improvement to reflect the complexity of real rivers.”
Regdate 2022/12/08
