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Nonlinear bending of sandwich beams made of FG-GPLRC faces and FGP core using different micromechanical models for approximating material properties

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International Journal of Solids and Structures 2025年 317卷

作者： Wattanasakulpong, Nuttawit Department of Mechanical and Robotic Engineering School of Engineering and Technology Walailak University Thasala Nakhon Si Thammarat80160 Thailand Center of Excellence for Sustainable Disaster Management Walailak University Nakhon Si Thammarat80160 Thailand

This study aims to apply various micromechanical models for predicting effective material properties of sandwich beams built from high-strength materials of faces reinforced by graphene platelets (GPLs) and high-flexural core of porous materials. GPLs content at the faces and pores at the core are varied in form of functionally graded materials with various patterns of distribution. When the material properties of the sandwich beams are successfully defined, they are brought to evaluate their structural performance in terms of bending resistances under various kinds of transverse distributed loads. Generalized beam theory consisting of several higher-order shear deformable functions is employed to create the governing equations based on a von Kármán type nonlinear strain–displacement relationship. Many important parameters such as GPLs content, porous coefficient, beam's geometry, sandwich thickness ratio and others which affect significantly the bending results of deflection and stresses of the beams are taken into account. All new results are presented in data and graphical forms which can serve as a benchmark for future research. © 2025 Elsevier Ltd

关键词： Structural properties

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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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A mutual information-Dempster-Shafer based decision ensemble system for land cover classification of hyperspectral data

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地球科学前沿 2017年第4期11卷 774-783页

作者： Parham PAHLAVANI Behnaz BIGDELI Center of Excellence in Geomatics Engineering in Disaster Management School of Surveying and Geospatial EngineeringCollege of EngineeringUniversity of TehranTehran ***Iran

Hyperspectral images contain extremely rich spectral information that offer great potential to discriminate between various land cover ***,these images are usually composed of tens or hundreds of spectrally close bands,which result in high redundancy and great amount of computation time in hyperspectral ***,in the presence of mixed coverage pixels,crisp classifiers produced errors,omission and *** paper presents a mutual information-Dempster-Shafer system through an ensemble classification approach for classification of hyperspectral ***,mutual information is applied to split data into a few independent partitions to overcome high ***,a fuzzy maximum likelihood classifies each band ***,Dempster-Shafer is applied to fuse the results of the fuzzy *** order to assess the proposed method,a crisp ensemble system based on a support vector machine as the crisp classifier and weighted majority voting as the crisp fusion method are applied on hyperspectral ***,a dimension reduction system is utilized to assess the effectiveness of mutual information band splitting of the proposed *** proposed methodology provides interesting conclusions on the effectiveness and potentiality of mutual informationDempster-Shafer based classification ofhyperspectral data.

关键词： mutual information Dempster-Shafer hyperspectral classification support vector machine

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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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3D reconstruction of buildings from LiDAR data considering various types of roof structures

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2017年第5期38卷 1451-1482页

作者： Pahlavani, Parham Amirkolaee, Hamed Amini Bigdeli, Behnaz Center of Excellence in Geomatics Eng. in Disaster Management Department of Surveying and Geomatics Eng. College of Eng. University of Tehran Tehran Iran Center of Excellence in Geomatics Eng. in Disaster Management Department of Surveying and Geomatics Eng. College of Eng. University of Tehran Tehran Iran Center of Excellence in Geomatics Eng. in Disaster Management Department of Surveying and Geomatics Eng. College of Eng. University of Tehran Tehran Iran

In this article, a different approach has been proposed to detect and reconstruct buildings in 3D space. In this regard, some potentially primary features were produced and a genetic algorithm (GA) was employed in order to select the optimum features. The selected features were utilized to detect the building by using k-nearest neighbour (k-NN) algorithm. The detection results were used as inputs of reconstruction procedure. The proposed approach for 3D reconstruction consists of three main steps: roof planes were separated in the first step. Then, the corners of each plane boundary were extracted in order to provide the roof reconstruction possibility. Finally, the walls were reconstructed and merged to roofs and the final 3D model was obtained. Results evaluation indicated that the average value of buildings detection in the test areas was 87.84% in Quality. Moreover, the average value of buildings reconstruction in the test areas was 76.95% in object based Quality, and 95.66% in Quality of building planes that were greater than 25 m(2), respectively. Also, the average of altimetric and planimetric RMS of the test areas were 0.3 m and 0.75 m, respectively.

关键词： THREE-dimensional display systems BUILDING design & construction LIDAR (Optics) GENETIC algorithms ROOF design & construction

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Seismic performance of interior precast concrete beam-column connections with T-section steel inserts under cyclic loading

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Earthquake engineering and engineering Vibration 2018年第2期17卷 355-369页

作者： Rattapon Ketiyot Chayanon Hansapinyo Department of Civil Engineering Chiang Mai University Center of Excellence for Natural Disaster Management Chiang Mai University

An experimental investigation was conducted to study the performance of precast beam-column concrete connections using T-section steel inserts into the concrete beam and joint core,under reversed cyclic *** 2/3-scale interior beam-column subassemblies,one monolithic concrete specimen and five precast concrete specimens were *** precast specimen was a simple connection for a gravity load resistant *** precast specimens were developed with different attributes to improve their seismic *** test results showed that the performance of the monolithic specimen M1 represented ductile seismic *** of columns and joints could be prevented,and the failure of the frame occurred at the flexural plastic hinge formation at the beam ends,close to the column *** the precast specimens,the splitting crack along the longitudinal lapped splice was a major *** precast P5 specimen with double steel T-section inserts showed better seismic performance compared to the other precast ***,the dowel bars connected to the steel inserts were too short to develop a *** design of the precast concrete beams with lap splice is needed for longer lap lengths and should be done at the beam mid span or at the low flexural stress region.

关键词： precast concrete beam-column connection cyclic loading

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Travel itinerary planning in public transportation network using activity-based modeling

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Journal of Applied Sciences 2009年第14期9卷 2532-2543页

作者： Kolyaie, S. Delavar, M.R. Malek, M.R. GIS Division Department of Surveying and Geomatics Engineering College of Engineering University of Tehran Tehran Iran Center of Excellence in Geomatics Engineering and Disaster Management Department of Surveying and Geomatics Engineering College of Engineering University of Tehran Tehran Iran Department of GIS Faculty of Geodesy and Geomatics Engineering KN Toosi University of Technology Tehran Iran

This study presents a new geospatial information system-based solution that assists people in trip planning to reach multiple destinations considering opening hours of points of interest and duration needed to perform multiple activities using multimodel public transportation media. People encounter with complexities in finding optimum paths in a multimodel public transportation system as they need to consider interactions of different modes of transportations in a more specific and constrained spatio-temporal structure. In this situation people's context in the form of their activities diary becomes an important issue affecting trip planning and selecting mode of transportation. Activities diary constrains the spatio-temporal order of activities and enables us to study the effect of activities in transportation planning. In this study, the impact of activity diaries and spatio-temporal dimensions of activities in trip planning and optimum path findings are discussed. A new algorithm is proposed for planning itinerary considering network, time and duration constraints. The algorithm assumes that the activities are mandatory and the locations and duration of each activity are fixed. The travelers' destinations, time and duration constraints provide the input of the algorithm. Using connectivity rules and policies, Dijkstra algorithm for finding the shortest paths is selected. The results determine the possible order of activities and their start and end time. The possible sequences of activities optimized based on minimizing waiting time during the activities i.e., time that person neither is doing an activity nor is traveling between activity locations and must wait till activity can be undertaken according to the time constraints. Finally, efficiency of the algorithm is determined according to the time complexity as a function of number of activity locations in the worst and normal cases. It has been proved that the time complexity of the algorithm depends on the t

关键词： Geographic information systems

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Automated 3D modeling of buildings in suburban areas based on integration of image and height data

Automated 3D modeling of buildings in suburban areas based o...

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1st International Workshop on 3D Geoinformation, 2006

作者： Khoshelham, Kourosh Center of Excellence in Surveying Engineering and Disaster Management Dept. of Surveying and Geomatics Engineering University of Tehran Iran

ISBN: (纸本)354036997X

This paper presents an automated method for 3D modeling of buildings in suburban areas through the integration of image and height data. The method is based on matching a CAD model of the building against the image, while the selection of the CAD model relies on clues derived from height data. The matching procedure makes use of straight line segments extracted from the image and grouped on the basis of proximity and parallelism relations. For the selection of the model, a process of fitting planar faces to height data guided by a segmentation of image data is employed. Roof planes are recognized by taking into account the height of each plane over a DTM of the scene. The integration strategy proposed in this paper is capable of exploiting accurate and reliable information from both sources of data. Incomplete image regions can be refined by using clues from height data, and incomplete image edges can be efficiently handled in the model matching process. The reconstruction strategy takes advantage of the high accuracy of laser range data, but is not influenced by the low spatial resolution of the DSM. An experiment is conducted to evaluate the performance of the proposed approach. Results indicate the promising performance of the proposed approach in reconstructing buildings with an acceptable accuracy at a reasonable computational cost.

关键词： Image segmentation

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A mutual information-Dempster-Shafer based decision ensemble system for land cover classification of hyperspectral data

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Frontiers of Structural and Civil engineering 2017年第4期11卷 774-783页

作者： Parham PAHLAVANI Behnaz BIGDELI Center of Excellence in Geomatics Engineering in Disaster Management School of Surveying and Geospatial Engineering College of Engineering University of Tehran Tehran *** Iran

Hyperspectral images contain extremely rich spectral information that offer great potential to discrimi- nate between various land cover classes. However, these images are usually composed of tens or hundreds of spectrally close bands, which result in high redundancy and great amount of computation time in hyperspectral classification. Furthermore, in the presence of mixed coverage pixels, crisp classifiers produced errors, omission and commission. This paper presents a mutual informa- tion-Dempster-Shafer system through an ensemble classi- fication approach for classification of hyperspectral data. First, mutual information is applied to split data into a few independent partitions to Then, a fuzzy maximum overcome high dimensionality. likelihood classifies each band subset. Finally, Dempster-Shafer is applied to fuse the results of the fuzzy classifiers. In order to assess the proposed method, a crisp ensemble system based on a support vector machine as the crisp classifier and weighted majority voting as the crisp fusion method are applied on hyperspectral data. Furthermore, a dimension reduction system is utilized to assess the effectiveness of mutual information band splitting of the proposed method. The proposed methodology provides interesting conclusions on the effectiveness and potentiality of mutual information- Dempster-Shafer based classification ofhyperspectral data.

关键词： mutual information Dempster-Shafer hyper- spectral classification support vector machine

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Development of a shared ride constrained path finding system

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Journal of Applied Sciences 2009年第12期9卷 2228-2237页

作者： Teimouri, M. Delavar, M.R. Malek, M.R. GIS Division Department of Surveying and Geomatics Engineering College of Engineering University of Tehran Tehran Iran Center of Excellence in Geomalics Engineering and Disaster Management Department of Surveying and Geomatics Engineering College of Engineering University of Tehran Tehran Iran Department of GIS Faculty of Geodesy and Geomatics Engineering K.N. Toosi University of Technology Tehran Iran

This study introduces a novel idea of shared riding constrained path finding developed using a geospatial information system. The idea is to use more efficiently the available (volunteering to pick up passenger) vehicles with free capacity on the street network. The travel path of available means on the street network formed a time-dependent network called transportation network. The space-time network concept utilized in this study in order to abstract the transportation network. The chronological-shortest path tree algorithm with the proposed modifications presents all reasonable shared ride constrained paths on the kind of transportation network. The paths may not coincide with the shortest path of origin to destination locations based on the street network. The traveler's origin and destination locations with arbitrary latest arrival time provide the input of the algorithm. The algorithm results provide the paths with the in-vchicle time, walking lime, waiting time and length of each path as the fare with passenger. Then, passenger can select the best trip regarding his preferences. Finally, improving the proposed solution with incorporating travel lime reliability through the model description as a possible extension is discussed. © 2009 Asian Network for Scientific Information.

关键词： Lime

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