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Developing a GIS-based rough fuzzy set granulation model to handle spatial uncertainty for hydrocarbon structure classification,case study:Fars domain,Iran

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Geo-Spatial Information Science 2022年第3期25卷 399-412页

作者： Sahand Seraj Mahmoud Reza Delavar School of Surveying and Geospatial Engineering University of TehranTehranIran Center of Excellence in Geomatics Engineering in Disaster Management University of TehranTehranIran

It is well agreed that geologic risk occurs during hydrocarbon exploration because diverse uncertainties accompany the entire hydrocarbon system parameters such as the source rock,reservoir rock,trap and seal *** order to overcome such attributes with uncertainties,a number of soft computing methods are *** granules could be provided by the Rough Fuzzy Set Granulation(RFSG)with a thorough quality *** is capable of attribute reduction that has been claimed to be essential in investigating the hydrocarbon *** paper is an endeavor to recommend a Geospatial Information System(GIS)-based model with the aim of categorizing the hydrocarbon structures map consistent with the uncertainty range concepts of geologic risk in the rough fuzzy sets and granular *** model used the RFSG for the attribute reduction by a Decision Logic language(DLlanguage).The RFSG was employed in order to classify hydrocarbon structures according to geological risk and extract the fuzzy rules with a predefined range of *** order to assess the precisions of the fuzzy decisions on the hydrocarbon structure classification,the fuzzy entropy and fuzzy cross-entropy are *** proposed RFSG model applied for 62 structures as the training data,average fuzzy entropy has been calculated as 0.85,whereas the average fuzzy cross-entropy has been calculated *** it can be discerned,just seven structures had cross-entropies greater than 0.1,while three structures were larger than *** is implied that the precision of the proposed model is about 89%.The results yielded two reductions for the condition attributes and 11 fuzzy rules being filtered by the granular computing values.

关键词： Rough fuzzy set granular computing Geospatial Information Science(GIS) petroleum system hydrocarbon structure

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A Novel Constrained Filter Integrated with an Extended Kalman Filter in Underground Pipeline Navigation Using MEMS IMU

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Gyroscopy and Navigation 2022年第1期13卷 7-22页

作者： Afshar, I.H. Delavar, M.R. Moshiri, B. GIS Department School of Surveying and Geospatial Engineering College of Engineering University of Tehran Tehran Iran Center of Excellence in Geomatic Engineering in Disaster Management School of Surveying and Geospatial Engineering College of Engineering University of Tehran Tehran Iran Center of Excellence in Electrical and Computer Engineering School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

Abstract—: To produce a 3D map of the Tehran’s first gas transfer pipeline (Tehran—Kuhnamak), a methodology has been developed in this research, in which a strapdown inertial navigation system (SINS) based on micro-electro-mechanical system (MEMS) and inertial measurement unit (IMU) is applied on pipeline inspection gauges (PIGs) to sense data every 4 millimeters of 111 kilometers of the whole pipeline. The navigation solution is based on an extended Kalman filter (EKF) using Allan variance (AVAR) to analyze and tune the EKF initial inputs. A new constrained PIG filter (CPF) is proposed in this paper in integration with EKF, in which two Euler angles (pitch and yaw) of the PIG are updated due to non-holonomic state constraints between pipe junctions. Besides, 98 magnetic control points have been used to increase robustness about every kilometer, which is coordinated by GPS. Furthermore, odometer measurements have been employed as measurements in the EKF. The results show that using such a hybrid approach has improved the PIG positioning accuracy by about 81% compared with that of the Basic EKF. In addition, positioning accuracy in comparison with the latest methods like EKF/pipeline junctions (PLJ) has increased by 32%. Furthermore, the proposed method is 55% better than EKF/PLJ in the algorithm runtime. © 2022, Pleiades Publishing, Ltd.

关键词： Extended Kalman filters

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Residual strength index prediction of circular concrete-filled steel tubular columns through advanced machine learning methods

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Asian Journal of Civil engineering 2024年第1期25卷 747-760页

作者： Narang, Aishwarya Kumar, Ravi Dhiman, Amit Center of Excellence in Disaster Mitigation & Management IIT Roorkee Roorkee 247667 India Mechanical & Industrial Engineering Department IIT Roorkee Roorkee 247667 India Chemical Engineering Department IIT Roorkee Roorkee 247667 India

Concrete-filled steel tube (CFST), which has a long-standing relationship for their exceptional mechanical performance and cost-effectiveness, is frequently utilized as the primary load-bearing element in various constructions. In CFSTs, there are challenging constraints between steel tubular and core concrete, and there is a highly nonlinear relationship between geometric and material parameters and high-temperature behavior. Therefore, a thorough understanding of the fire performance of these columns is required. Fifty-six experimental tests from the literature are collected for circular CFST columns into a database and statistically assessed for their residual strength index (RSI). The database was then divided into a test set and a training set, which were used to train and improve the machine learning models. In this study, models to forecast the RSI of circular CFST columns are developed using four machine learning (ML) techniques with feature ranking analysis, namely: Gaussian progress regression (GPR), ensemble, support vector machines (SVM), and artificial neural network (ANN). Quadratic SVM and bi-layered ANN models outperformed the other ML models with R ² values of 0.93 and 0.98, respectively. The relevant experimental test results corroborated the parametric study's findings that the trained ML models could typically represent the influence of each key input attribute. As a result, ML is an adequate substitute for empirical and theoretical formulations. The use of ML models will reduce the research expense, and practicing engineers will find it especially helpful to evaluate the performance of these columns at higher temperatures. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Artificial neural network CFST Fire performance Gaussian progress regression Residual strength index

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Utilization of New Fly Ash Type from Selective Catalytic Reduction (SCR) Process as an Additive in Portland Cement 4th

Utilization of New Fly Ash Type from Selective Catalytic Red...

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4th International Conference on Environmental Sciences and Renewable Energy (ESRE)

作者： Suwan, T. Jongwijak, T. Jitsangiam, P. Buachart, C. Charatpangoon, B. Jitpairod, K. Department of Civil Engineering Faculty of Engineering Chiang Mai University Chiang Mai Thailand Center of Excellence in Natural Disaster Management Chiang Mai University Chiang Mai Thailand Chiang Mai University Chiang Mai Thailand

ISBN: (纸本)9789811994395

Fly ash is one of the by-products of coal-fired power plants for electricity generation. In Thailand, the largest plant is Mae Moh power station in Lampang province, which generates fly ash of approximately 6,000–8,000 tons per day. Those fly ashes can be used in the cement and concrete industry as an additive. However, the new installation of nitrogen gas (NOx) treatment, called the "Selective Catalytic Reduction system (SCR)", could possibly leave some of the excess ammonia in the collected fly ash. The main aims of this study are to investigate the characteristics of the SCR-fly ash compared to the typical high calcium fly ash (HC-fly ash). Moreover, the evaluation of the properties of OPC-fly ash blended cement pastes using those two types of fly ash were also carried out. The preliminary results show that the SCR-fly ash can be used as an additive in cement and concrete, but, it should be rested for over a month to release the contaminated ammonia. The outcomes could be a source of information on utilizing this new type of SCR-fly ash and could also be an alternative approach for recycling industrial by-products. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fly ash

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Semantic Segmentation Uncertainty Assessment of Different U-net Architectures for Extracting Building Footprints

Semantic Segmentation Uncertainty Assessment of Different U-...

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2024 Mid-term Symposium on Spatial Information to Empower the Metaverse

作者： Haghighi Gashti, Ehsan Delavar, Mahmoud Reza Guan, Haiyan Li, Jonathan GIS Department School of Surveying and Geospatial Eng. College of Engineering University of Tehran Tehran Iran Centre of Excellence in Geomatic Eng. in Disaster Management and Land Administration in Smart City Lab. School of Surveying and Geospatial Eng. College of Engineering University of Tehran Tehran Iran School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and Technology Nanjing210044 China Department of Geography and Environmental Management University of Waterloo WaterlooONN2L 3G1 Canada

Automatic extraction of building footprints from aerial and space imageries has been found ever increasing importance in urban planning, disaster management, and environmental monitoring. However, achieving accurate building footprint extraction poses significant challenges due to diverse building characteristics and their similarities to their background elements. While conventional methods in building footprint extraction have mainly relied on image processing techniques, recent advancements in deep learning, particularly semantic segmentation algorithms like U-Net, have shown promise in addressing these challenges through machine learning. This study explores different depths of the U-Net model for building footprint extraction, aiming to identify the optimum architecture while investigating the semantic uncertainty of the building footprint extraction. Utilizing aerial imagery from cities including Berlin, Paris, Chicago, and Zurich, collected from Google Maps and OpenStreetMap (OSM) data, five U-Net models have been compared with varying depths. In addition, the impact of dataset sizes and learning rates on model performance has been investigated. Results confirmed that the U-Net-32-1024 model achieves the highest intersection over union (IoU), Accuracy, and F1-score. Moreover, increasing the training dataset size leads to significant improvements in model performance with IoU, Accuracy and F1-score reaching their values of 73.73%, 88.65% and 88.53%. However, challenges remain in accurately delineating buildings in dense urban areas. Nonetheless, our findings demonstrated the effectiveness of U-Net models in building footprint extraction. © Author(s) 2024.

关键词： Semantic Segmentation

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Current Status of Seismic Performance Research on Bamboo Construction and its Prospective Applications in Thailand 9

Current Status of Seismic Performance Research on Bamboo Con...

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Joint 9th International Conference on Digital Arts, Media and Technology with 7th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Liu, Jiaxing Rong, Wei Putra, I Nyoman Gede Maha Tah, Joseph Hansapinyo, Chayanon Khamaksorn, Achara Chiang Mai University College of Arts Media and Technology Chiang Mai Thailand Universitas Warmadewa Architecture Department Bali Denpasar Indonesia University Oxford Faculty of Technology Design and Environment Oxford Brookes United Kingdom Chiang Mai University Center of Excellence in Natural Disaster Management Department of Civil Engineering Chiang Mai Thailand

ISBN: (纸本)9798350318241

The purpose of this conference paper is to reveal the current state of seismic performance research on bamboo structural buildings based on a systematic literature review. The significant contribution by China to this domain is highlighted and the scarcity of knowledge management tools in the research of seismic performance for bamboo construction is emphasised. It is demonstrated that the most of the studies are of reconstituted bamboo, while there is relatively little research on raw bamboo. Although these studies unquestionably offer valuable references for establishing seismic standards for bamboo construction consistent with the Thai region, it is important to also consider the native resource conditions of Thailand. It is suggested in this paper that future research endeavours should involve the use of indigenous Thai bamboo materials for a deeper investigation, and that the SNA theory should be applied in order to pinpoint and assimilate expert knowledge within the realm of bamboo construction. © 2024 IEEE.

关键词： Knowledge management

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Properties of Porous Concrete Using Expanded Polystyrene (EPS) Foam Waste as Cement Binder Admixture 4th

Properties of Porous Concrete Using Expanded Polystyrene (EP...

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4th International Conference on Environmental Sciences and Renewable Energy (ESRE)

作者： Boonpeng, C. Suwan, T. Liu, W. Hansapinyo, C. Paphawasit, B. Department of Civil Engineering Faculty of Engineering Chiang Mai University Chiang Mai Thailand Center of Excellence in Natural Disaster Management Chiang Mai University Chiang Mai Thailand College of Transportation and Civil Engineering Fujian Agriculture and Forestry University Fuzhou China College of Arts Media and Technology Chiang Mai University Chiang Mai Thailand

ISBN: (纸本)9789811994395

Porous Concrete is becoming more popular and widespread as it can drain the water from the top surface to the underneath effectively. It is then applied to the road or pavement surface construction. However, some typical properties of cement-based porous concrete are limited, e.g., low strength, poor durability, or scaling-off of pavement. Conventional polymer admixtures, such as unsaturated polyester and epoxy resins, are thus used to reduce those problems. However, the conventional polymer admixture is quite expensive and less environmentally friendly. This study focuses on utilizing the Expanded polystyrene (EPS) foam wastes as an admixture to improve cement-based porous concrete properties. The results show that 10% addition of EPS foam waste admixture provided better compressive strength than that of typical porous concrete with no admixture from 12.02 to 17.89 MPa. This approach of recycling EPS foam waste could be one of the alternative admixtures for the cement and concrete sector. The outcomes could not only improve the specific property for the cement-based porous concrete as construction materials but also promote the recycling of EPS foam wastes and allow communities to be greener and cleaner. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Polyester resins

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A Novel Machine Learning Framework for Seismic Vulnerability Assessment of Urban Infrastructure

A Novel Machine Learning Framework for Seismic Vulnerability...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Kaushik Gondaliya Sneha Kumari Jignesh Amin Abdullah Ansari RC Bush dept. of Civil (Structural Engineering) Gujarat Technological University–School of Engineering and Technology Ahmedabad India Center of Excellence in Disaster Management DMI Patna Bihar India Earthquake Monitoring Center Sultan Qaboos University Muscat Oman dept. of civil engineering MNIT Jaipur Rajasthan

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The assessment of seismic vulnerability in urban infrastructure presents a significant challenge in rapidly growing cities, as conventional evaluation methods frequently do not yield reliable and accurate insights for emergency response and planning. Machine learning algorithms demonstrate potential in structural analysis; however, a notable gap exists in methodologies capable of integrating diverse data sources for thorough vulnerability assessment. This paper presents a novel framework integrating Geographic Information Systems (GIS) data, structural analysis, and machine learning techniques to overcome existing limitations. The proposed methodology utilizes ArcGIS Pro for spatial data management, SAP2000 for conducting Nonlinear Static Pushover Analysis (NSPA) to assess structural response to seismic forces, and employs advanced machine learning algorithms to train the ANN model applicable to various urban structures, including residential buildings, bridges, and water tanks. The framework employs artificial neural networks for damage prediction and utilizes Monte Carlo simulation for uncertainty quantification, thereby providing a probabilistic method for vulnerability assessment. The proposed system processes structural and seismic data via an automated pipeline, facilitating rapid risk assessment and decision-making support. This approach addresses existing gaps in probabilistic seismic vulnerability assessment and provides a theoretical foundation for developing resilient urban infrastructure systems. The framework can be applied in urban planning, emergency response, and infrastructure prioritization, providing essential support for decision-makers in disaster management.

关键词： Accuracy Uncertainty Machine learning Artificial neural networks Probabilistic logic Emergency services Real-time systems Data models Resilience Geographic information systems

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EARTHQUAKE PREDICTION EVALUATION BASED on VLF DATA USING A NOVEL INTERSECTION-UNION METHOD 24

EARTHQUAKE PREDICTION EVALUATION BASED on VLF DATA USING A N...

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2021 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission IV

作者： Barghi, W. Delavar, M.R. Shahabadi, M. Zare, M. Eslaminezhad, S.A. Bayat, H. Department of GIS School of Surveying and Geospatial Eng. College of Engineering University of Tehran Tehran Iran Center of Excellence in Geomatic Eng. in Disaster Management School of Surveying and Geospatial Engineering College of Engineering University of Tehran Tehran Iran Photonics Research Lab Center of Excellence on Applied Electromagnetic Systems School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Engineering Seismology Department International Institute of Earthquake Engineering and Seismology Tehran Iran

Electromagnetic phenomena, especially those in the Very Low Frequency/Low Frequency (VLF/LF) bands are promising for short-term earthquake prediction. Seismo-ionospheric perturbations cause a variety of changes in different receiver-transmitter VLF/LF signal paths. Therefore, independent and simultaneous observations at different points thus in different VLF/LF signal propagation paths are necessary to better predict the earthquake. Most of the previous research on VLF data have been based on one path or limited number of paths which examined perturbations in the time domain and less attention has been paid to estimate the location of the earthquake. In the present research, using wavelet analysis, the temporal variations of seismo-ionospheric perturbations and the approximate time of earthquake are predicted. Clear disturbances are observed two weeks before the Kumamoto earthquake happened in Japan in 2016. The novelty of this study is to present an approach called Intersection-Union method to predict earthquake location. Based on the geometry of a VLF/LF network, the Intersection-Union method was introduced to estimate the earthquake epicenter. This method is based on the overlay of earthquake occurrence probable areas. With simultaneous use of different propagation paths by the Intersection-Union method, an area with a radius of about 300 km was determined as the probable location of the earthquake epicenter. The accuracy of the proposed method is 300 km compared with 1000 km accuracy of other earthquake location prediction scenarios. © Copyright: Author(s) 2021.

关键词： Earthquakes

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Preface: Workshop "ISSDQ 2023: Artificial Intelligence and Uncertainty Modeling in Spatial Analysis"

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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2023年第1-W1-2023期10卷 1161-1162页

作者： Delavar, M.R. Stein, A. Shi, J.W.Z. Center of Excellence in Geomatic Eng. in Disaster Management and Land Administration in Smart City Lab. School of Surveying and Geospatial Eng. College of Engineering University of Teheran Teheran Iran Department of Earth Observation Science University of Twente Enschede Netherlands Department of Land Surveying and Geo-informatics The Hong Kong Polytechnic University Kowloon Hong Kong

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