Ontological knowledge base for concrete bridge rehabilitation project management

Wu, Chengke; Wu, Peng; Wang, Jun; Jiang, Rui; Chen, Mengcheng; Wang, Xiangyu

doi:10.1016/j.autcon.2020.103428

Cited by 76 publications

(39 citation statements)

References 36 publications

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“…As for scripting, design constraints are mostly manual scripted; however, research has started to develop automatic methods. For example, approaches to extract constraints-related information from text format to computer recognisable format have been studied and developed [ 45 , 46 , 47 , 48 ]. Yet, manually scripting of design constraints is still more common and flexible so far.…”

Section: Generative Design and Building Information Modellingmentioning

confidence: 99%

Generative Design in Building Information Modelling (BIM): Approaches and Requirements

Wang

et al. 2021

Sensors

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The integration of generative design (GD) and building information modelling (BIM), as a new technology consolidation, can facilitate the constructability of GD’s automatic design solutions, while improving BIM’s capability in the early design phase. Thus, there has been an increasing interest to study GD-BIM, with current focuses mainly on exploring applications and investigating tools. However, there are a lack of studies regarding methodological relationships and skill requirement based on different development objectives or GD properties; thus, the threshold of developing GD-BIM still seems high. This study conducts a critical review of current approaches for developing GD in BIM, and analyses methodological relationships, skill requirements, and improvement of GD-BIM development. Accordingly, novel perspectives of objective-oriented, GD component-based, and skill-driven GD-BIM development as well as reference guides are proposed. Finally, future research directions, challenges, and potential solutions are discussed. This research aims to guide designers in the building industry to properly determine approaches for developing GD-BIM and inspire researchers’ future studies.

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Section: Generative Design and Building Information Modellingmentioning

confidence: 99%

Generative Design in Building Information Modelling (BIM): Approaches and Requirements

Wang

et al. 2021

Sensors

Self Cite

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“…Mathematical Problems in Engineering [59][60][61], whale optimization algorithm (WOA) [62][63][64], grey wolf optimizer (GWO) [65,66], bacterial foraging optimization (BFO) [67], and grasshopper optimization algorithm (GOA) [68]. e aim of optimization is to determine a suitable value for one or more parameters between all possible values for them in order to minimize or maximize a function and it can be applied to find feasible answer to many potential real-life applications such as deployment optimization [69], adaptive control concepts [35,[70][71][72], computer vision techniques [73], transportation networks [74], image and video processing [75][76][77][78][79][80], decision-making approaches [81][82][83], power allocation systems [84], sensor fusion approaches [85], monitoring systems [86][87][88][89], and deep learning models [19,[90][91][92][93]. e PSO algorithm as an optimization algorithm is a social interaction model between independent particles that use their social knowledge to find the minimum and maximum value of a function [15].…”

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

Chafi

Afrakhte

2021

Mathematical Problems in Engineering

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Electrical load forecasting plays a key role in power system planning and operation procedures. So far, a variety of techniques have been employed for electrical load forecasting. Meanwhile, neural-network-based methods led to fewer prediction errors due to their ability to adapt properly to the consuming load's hidden characteristic. Therefore, these methods were widely accepted by the researchers. As the parameters of the neural network have a significant impact on its performance, in this paper, a short-term electrical load forecasting method using neural network and particle swarm optimization (PSO) algorithm is proposed, in which some neural network parameters including learning rate and number of hidden layers are determined in order to forecast electrical load using the PSO algorithm precisely. Then, the neural network with these optimized parameters is used to predict the short-term electrical load. In this method, a three-layer feedforward neural network trained by backpropagation algorithm is used beside an improved gbest PSO algorithm. Also, the neural network prediction error is defined as the PSO algorithm cost function. The proposed approach has been tested on the Iranian power grid using MATLAB software. The average of three indices beside graphical results has been considered to evaluate the performance of the proposed method. The simulation results reflect the capabilities of the proposed method in accurately predicting the electrical load.

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“…Speaking of bridges, there is a wider interest in concrete or steel facilities, given the high incidence of these typologies around the world (Trzeciak & Borrmann, 2018). There are numerous IFC extensions, that range from those used to enable BIM-based descriptions of structural health monitoring systems in compliance with IFC modeling capabilities (Theiler & Smarsly, 2018) to those collecting domain knowledge of bridge rehabilitation to improve information integration and constraint management (Wu et al, 2021). In the context of structural analysis requirements, (Park et al, 2020) propose an extended IFC-based bridge information modeling method using a process to apply the meshfree structural analysis method to the IFCbased model.…”

Section: Semantic and Bim In The Infrastructure Fieldmentioning

confidence: 99%

Digital Survey Ai and Semantics for Railway Masonry Bridges Health Assessment

Garozzo

Santagati

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Masonry arch railway bridges represent a historically built heritage to be preserved. The multidisciplinary approach requires the construction of a common language, namely the creation of a formal conceptualisation of bridge domain that could serve as basis for both adding new layers of knowledge in H-BIM modeling and aiding the automatic segmentation of masonry bridge point clouds, thus supporting the semi-automatic creation of information models. The presented research aims at showing the results of an in-depth analysis conducted on masonry arched bridges computational ontologies; following this, the authors propose a semantic conceptualization in the masonry bridge domain, structured with three group of key concepts needed in the process of knowledge: bridge elements, materials, and defects. As a case study the masonry bridges of the Sicilian Circumetnea railway are chosen.

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Ontological knowledge base for concrete bridge rehabilitation project management

Cited by 76 publications

References 36 publications

Generative Design in Building Information Modelling (BIM): Approaches and Requirements

Generative Design in Building Information Modelling (BIM): Approaches and Requirements

Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

Digital Survey Ai and Semantics for Railway Masonry Bridges Health Assessment

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