Non-geometric Information Research Articles

Blockchain-BIM: reducing cost in green retrofitting for sustainable construction development

During the implementation process, the construction sector significantly affects the environment, leading to environmental pollution and the overconsumption of natural resources. This jeopardizes the sustainability of a healthy living environment, requiring a solid system to control development. A sustainable and environmentally friendly development system is crucial from architectural, construction, and economic perspectives. However, when applying the concept of Green Building to existing buildings, the cost of green retrofits has increased by about 10.77%. A case study was carried out on the existing BRIN (National Research and Innovation Agency) building to provide alternative solutions to reduce these green retrofitting costs, making sustainable construction the primary option in developing the construction sector. One way to address this cost increase is using the Blockchain-Building Information Modeling (BIM) application, an alternative method to determine the most economical cost based on input or a given model. BIM models contain non-geometric information, including materials for building components, quantities, prices, procedures, scales, and sizes. This allows for real-time data exchange and collaborative decision-making between stakeholders, improving visualization and modeling and lowering costs due to better construction outcomes and higher productivity probability. The implementation of Blockchain-BIM is expected to result in green retrofit cost savings of at least 3% on each green level according to the Ministry of Public Works and Housing (PUPR) of the Republic of Indonesia Technical Guidelines No. 01/SE/M/2022, namely Primary, Intermediate, and Main.

A feature selection method for multimodal multispectral LiDAR sensing

Optical remote sensing techniques can indicate the properties of objects by observing different modalities (physical quantities) of the backscattered light at different optical wavelengths. Established examples are reflectance, fluorescence, Raman, or depolarization spectroscopy. LiDAR sensing, on the other hand, allows acquiring the geometry of objects by measuring the propagation delay of optical probing signals. Multimodal multispectral (MM) LiDAR combines these capabilities and extends conventional monochromatic LiDAR in both spectral and modal dimensions within a single instrument, thus enriching point cloud data with non-geometric information. The potentially high dimension of MM LiDAR data, however, poses significant challenges for instrumental design, data acquisition, and data processing. MM LiDAR data are structured as several or all modalities are available in each of the spectral channels. The above challenges can thus be mitigated by feature selection (FS), if the structure of the features is taken into account, i.e., if entire spectral channels or entire modalities are selected or omitted. Herein, we focus on the feature selection method for MM LiDAR and propose a multiclass group feature selection algorithm (MGSVM FS) consisting of a structural sparsity-based embedded feature selection method with an all-in-one support vector machine (SVM). It tackles jointly the challenges arising from the high dimension of the MM data and the need for a multiclass classification task while exploiting the structure of the MM data. In addition, we introduce a complete workflow for evaluating the feature selection and for decision-making. We apply the framework to selecting an optimum spectral and modal configuration for remote material classification using an experimental MM LiDAR system that provides reflectance, distance, and degree of linear polarization in 28 spectral channels of 10 nm width. For the experimental investigation, we use MM LiDAR data obtained in a controlled lab environment from thirty specimens of four material classes relevant for construction. Using all three modalities, we find a configuration with only 3 spectral channels that achieves a classification mean-F1 score of 100% within this small dataset. Similar classification performance can also be achieved with only two modalities when using more spectral channels. MGSVM FS improves the classification mean-F1 score by up to 25% as compared to random selection and outperforms two other commonly used filter and embedded feature selection methods, in this application example. The proposed group feature selection algorithm and decision-making are useful for MM LiDAR, providing a link between instrumental design, data acquisition, and data processing. However, they are also transferable to other application fields related to multiclass classification, regression, and knowledge discovery, with features structured in groups. The collected MM feature dataset, the MGSVM FS algorithm, and the evaluation pipeline are accessible online.11https://github.com/yuhan-yhyh/Dataset_Code_MGSVM-FS-MM-LiDAR.git.

An ontology-based knowledge framework for selection of joining process in plastic assembly

Selection of assembly process is one of the important stage of product development which need to tackle at the earlier stage product realization. Selecting the most appropriate joining process needs various assembly joint information (liaison) which includes both the geometric and non-geometric information. To capture, represent, and reuse this information across domains is a highly knowledge-intensive process that necessitates the use of an active artificial intelligence (AI) tool. In this research, an AI tool called ontology-based knowledge framework is used to assist with identifying the best plastic assembly process to effectively support designers and process planners. The methodology for selecting a joining process and developing a knowledge-based framework is described, together with industrial application of the suggested approach is proposed. To express many key concepts like liaison, process, requirement for variant product etc., a joining process selection (JPS) ontology is created. To get the necessary knowledge for process selection that integrates several instances and knowledge rules, ontology mapping of joining method selection concepts is done using Semantic Web Rule Language (SWRL). The suggested method uses rule-based reasoning to automatically infer the best joining methods. Finally, industrial application of the suggested framework is proposed to assess the applicability of this approach.

A domain model for geometric modeling in support of the automated BIM modeling

PurposeThis research aims to argue that manual geometric modeling is blocking the building information modeling (BIM) promotion to small-size companies. Therefore, it is necessary to study a manner of automated modeling to reduce the dependence of BIM implementation on manpower. This paper aims to make a study into such a system to propose both its theory and prototype.Design/methodology/approachThis research took a prototyping as the methodology, which consists of three steps: (1) proposing a theoretical framework supporting automated geometric modeling process; (2) developing a prototype system based on the framework; (3) conducting a testing for the prototype system on its performance.FindingsPrevious researches into automated geometric modeling only respectively focused on a specific procedure for a particular engineering domain. No general model was abstracted to support generic geometric modeling. This paper, taking higher level of abstraction, proposed such a model that can describe general geometric modeling process to serve generic automated geometric modeling systems.Research limitations/implicationsThis paper focused on only geometric modeling, skipping non-geometric information of BIM. A complete BIM model consists of geometric and non-geometric data. Therefore, the method of combination of them is on the research agenda.Originality/valueThe model proposed by this paper provide a mechanism to translate engineering geometric objects into textual representations, being able to act as the kernel of generic automated geometric modeling systems, which are expected to boost BIM promotion in industry.

BASILICA DI SAN GIACOMO IN COMO (ITALY): DRAWINGS AND HBIM TO MANAGE ARCHEOLOGICAL, CONSERVATIVE AND STRUCTURAL ACTIVITIES

Abstract. This paper aims at presenting the recording and modelling work developed in the framework of restoration and conservation activities for the Basilica di San Giacomo in Como, Italy, whose construction started in the 11th century. The project started in 2022 and involved the application of the Historical Building Information Model (HBIM) methodology through a Scan-to-BIM approach was assessed. High-detailed 3D survey techniques were used to acquire the specific shape of the church and annexed buildings. Then, through different modelling strategies, the HBIM environment allowed the representation of all the architectural elements. The purpose of this model is to have essential support to plan the restoration activities and to give the different experts involved a single three-dimensional tool for managing all the information during the development of the construction works. The possibility to update the model over time with geometric and non-geometric information will provide a powerful tool also for other future activities, such as the installation of a monitoring system that could reveal the displacements of bearing elements of the church.

Building Information Modeling (BIM)-Based Information Management Platform in the Construction Industry

Of late, Building Information Modeling (BIM) is being widely used in managing construction information. As it comprises a 3D object-oriented system with geometric and non-geometric information, BIM allows a comprehensive digital representation of a built facility with great information depth for the construction stakeholders (i.e., architects, engineers, contractors, and subcontractors). Despite BIM being widely used in managing construction information, the components of BIM-based information management are limited studied. Hence, the objective of this paper is to review the components of BIM-based information management through an extensive literature review. Two main databases (i.e., Scopus and Web of Science) are used to obtain the information. The findings indicated that BIM-based information management facilitates construction stakeholders (i.e., architects, engineers, contractors, and subcontractors) to collaborate and integrate information into single sources and hence improves the construction documentation performance (i.e., cost, time, quality, and safety). The paper also concludes discussions that indicate the knowledge gap in current research to be investigated for further research.

Developing a BIM GIS–Integrated Method for Urban Underground Piping Management in China: A Case Study

Underground piping remains one of the key uncertain infrastructures in urban development. Because they are buried underground without a clear vision, the management of the underground piping system is more complicated than other infrastructure. Their records are not usually fully available or accurate, and excavation to validate underground structures is often needed in infrastructure projects. Detection of unexpected obstacles may lead to project delay, increased time spent, and risk of damaging existing piping. The traditional underground piping system management relies on computer-aided design (CAD) drawings and historical empiricism. Still, it cannot meet the strict requirements of infrastructure management nowadays or make the management efficient and sustainable because of the technical limitations and conceptual obsolescence. Therefore, to address this issue, this paper proposes an innovative method that aims to manage the underground piping life cycle. This method is to develop a building information model (BIM) in a geographic information system (GIS) environment by integrating heterogeneous data from both BIM and GIS, which not only achieves three-dimensional (3D) visualization but also seamlessly connects to portfolios, programs, projects, and assets throughout the life cycle of urban underground piping. This is a critical development to integrate and analyze geometric and nongeometric information of aboveground and subsurface building assets and the surrounding environment to stimulate interaction with real-world context. This paper also introduces a case study in China to further prove the feasibility of this method and the format based on it in practice. In the case study, the proposed method is implemented together with a piping sensor to further illustrate the effectiveness of the method over trenchless detection and real-time monitoring for daily management of underground piping. Because the model is visible and the monitoring is in real time, the management would be straightforward. At the same time, this method can analyze, plan, and monitor activity, so the management of urban underground piping systems would become smart. Therefore, an efficient and sustainable management performance can be achieved. This method is meaningful for managing the urban underground piping system, especially for those with expansion, refurbishment, or maintenance for an urban underground piping system.

An Automatic Extraction Method of Rebar Processing Information Based on Digital Image

Reinforced steel is one of the most important building materials in civil engineering and improving the intelligence of steel reinforcement engineering can greatly promote the intelligent development of the construction industry. This research addressed the problems of the slow speed and poor accuracy of manually extracting rebar processing information, which leads to a low degree of rebar processing intelligence. Firstly, based on digital image processing technology, image preprocessing methods such as binarization and grayscale were used to eliminate redundant information in a detail drawing of a rebar. An image segmentation method based on pixel statistics was proposed to store the geometric and non-geometric information of the detail drawing of the rebar separately. Next, the bending angle was extracted by line thinning and corner detection, and the bending direction of the steel bar was determined based on the mathematical characteristics of the vector product. Finally, the non-geometric information was extracted by combining the morphological algorithm and the Optical Character Recognition (OCR) engine. According to the characteristics of the information sequence, an information mapping method was proposed to realize the integration of geometric and non-geometric information. The applicability and accuracy of this method for extracting the steel bar’s information were tested by experiments, and it was shown that the method also provides a theoretical basis for realizing the intelligentization and informatization of steel bar processing.

A novel graph neural networks approach for 3D product model retrieval

ABSTRACT With the quick development of intelligent manufacturing technology, increasing of 3D product models data resource raises the need for product models retrieval method that can fully exploit model information, whereas the existing methods only concern geometric information with little or no manufacturing information. Model-Based Definition (MBD) 3D engineering has gained wide popularity in recent times which enables smart manufacturing by attaching product manufacturing information. Its tip orientation to the effective reuse of MBD for reducing development time and creating an efficient digital thread. In this paper, we proposed the MBD-based attribute adjacency graph neural networks (MAAGNN) with attention mechanism. Firstly, the MBD-based attribute adjacency graph (MAAG) is established to fully express the product semantic information, which includes geometric and non-geometric information of the MBD model. Then graph neural network (GNN) is employed to embed the MAAG into continuous vector spaces, and attention mechanism is introduced to adaptively recognize more important features information, which can solve the ambiguity problem caused by blend features such as chamfer. Finally, the model is trained to achieve fast and accurate retrieval of the MBD models. Experiments demonstrate its potential advantages to meet the requirements of engineering applications.

HBIM Application in Historic Timber Structures: A Systematic Review

ABSTRACT Despite the recent significant increase on the use of Building Information Modelling (BIM) in the cultural heritage field, its application on heritage timber structures aiming at their conservation and assessment has not yet been fully established. Comparing with other construction materials, timber presents singular features that must be addressed in order to carry out a proper condition assessment. For this reason, this review summarizes existing works on historical timber structures using Historical BIM (HBIM), focusing not only on various geometric surveying and 3D modelling methods, but also on nongeometric information included in the model which are especially related with conservation, testing, and monitoring. In addition, this work illustrates the effectiveness increase given by a structural analysis, as to assess structural heath, after being implemented within a HBIM-based framework. To that aim, a global framework is proposed where the development and implementation level of different analysis stages are described.

Virtual Gas Turbines Part II: An Automated Whole-Engine Secondary Air System Model Generation

Abstract The design and analysis of the secondary air system (SAS) of gas turbine engine is a complex and time-consuming process because of the complicated topology and iterative nature of SAS design. The conventional SAS design-analysis model generation process is quite tedious and inefficient. It is still largely dependent on human expertise and thus incurs high time-cost. This paper presents an automated, whole-engine SAS flow network model generation methodology. This method accesses a prebuilt feature-based whole-engine geometry model and transforms the geometry features into the features suitable for SAS flow network analysis. The proposed method extracts both the geometric and non-geometric information from the engine geometry model such as rotational frames, materials, and boundary conditions. Apart from ensuring geometric consistency, this methodology also establishes a bidirectional information exchange protocol between the engine geometry model and the SAS flow network model, which enables to make engine geometry modifications based on SAS analysis results. The application of this feature mapping methodology is demonstrated by generating the SAS flow network model of a modern three-shaft gas turbine engine. This flow network model is generated within a few minutes, without any human intervention, which significantly reduces the SAS design-analysis time cost. The proposed methodology seamlessly links the geometry and the air system modelers of Virtual Gas Turbines simulation framework and thus allows performing a large number of whole-engine SAS simulations, design optimizations and fast redesign activities.

A node2vec-based graph embedding approach for unified assembly process information modeling and workstep execution time prediction

The trend of customized production results in the demand for higher level of automation, in which artificial intelligence decision-making dominates. As the core of smart manufacturing systems, intelligent services stand a significant role in the analysis, prediction, and adjustment of production process, which is inseparable from the effective semantic modeling of the procedures and elements involved. However, there is an absence of unified modeling of assembly process, including both geometric and non-geometric information, leading to the incomprehensiveness when providing data support for intelligent services. To fill this gap, a generic node2vec-based parameterized representation of geometric elements and assembly constraints approach is proposed. Firstly, the information structure of assembly process is established, in which the geometric elements and topological relationships of the product are abstracted into a network. Secondly, node2vec is adopted for the graph embedding to generate preset dimension vectors corresponding to the nodes in the geometric network. As the edges in the network, the vectors corresponding to the assembly constraints, which are regarded as the parameterized representations, can be obtained through node vector calculation. Moreover, an assembly workstep execution time prediction method based on historical data is introduced with the parameterized representations of assembly constraints as the carriers of geometric topological information. At last, an industrial case study is illustrated to show the entire process of constraint parameterized representations and workstep execution time prediction, indicating the feasibility and availability of the method proposed.

An improved memory networks based product model classification method

ABSTRACT Existing CAD model classification methods are usually performed by extracting the geometric and topological information of the models. This is not suitable for the classification of Model-based Definition (MBD) models because of the lack of non-geometric semantic information. They cannot solve the problem, as the geometry and topology are similar but the product manufacturing information (PMI) is completely different. Firstly, this paper proposed a multi-granularity classification model based on MBD attribute adjacency graph (MAAG). And then an improved Long Short-Term Memory (LSTM) neural network with a new loss function and adaptive training times is put forward. This neural network model is trained to achieve fast and accurate classification of the MBD models. Finally, the experimental results show that the proposed method not only improves the accuracy of classification, but also effectively reduces the training time cost of the model.

Semantic enrichment of building and city information models: A ten-year review

Building Information Models (BIMs) and City Information Models (CIMs) have flourished in building and urban studies independently over the past decade. Semantic enrichment is an indispensable process that adds new semantics such as geometric, non-geometric, and topological information into existing BIMs or CIMs to enable multidisciplinary applications in fields such as construction management, geoinformatics, and urban planning. These two paths are now coming to a juncture for integration and juxtaposition. However, a critical review of the semantic enrichment of BIM and CIM is missing in the literature. This research aims to probe into semantic enrichment by comparing its similarities and differences between BIM and CIM over a ten-year time span. The research methods include establishing a uniform conceptual model, and sourcing and analyzing 44 pertinent cases in the literature. The findings plot the terminologies, methods, scopes, and trends for the semantic enrichment approaches in the two domains. With the increasing availability of data sources, algorithms, and computing power, they cross the border to enter each other’s domain. Future research will likely gain new momentums from the demands of value-added applications, development of remote sensing devices, intelligent data processing algorithms, interoperability between BIM and CIM software platforms, and emerging technologies such as big data analytics.

CAD Integration of Mechanical Numerical Control Board Parts Based on Machining Features

The development and application of computer-aided design (CAD) technology has led to rapid improvements in product design automation, crafting process automation and numerical control programming automation. Machining feature refers to basic configuration units that constitute part shapes and the collection of non-geometric information with engineering semantics attached to it. The integration of mechanical numerical control parts is the integration of part design features and machining features, and each feature corresponds to a set of processing methods. Based on the summaries and analyses of previous research works, this paper expounded the current status and significance of mechanical numerical control board part integration, elaborated the development background, current status and future challenges of machining features and CAD technology, introduced a data transfer method of CAD integration and machining features-based part integration system, analyzed the design and machining features of CAD integration of board parts, constructed the graphics processing model and information reorganization model for CAD integration of board parts; conducted the feature description and modeling analysis of CAD integration of plate parts; discussed the crafting information similarity of mechanical numerical control plate part integration; explored the feature information and expression of feature library for plate parts integration.

Assembly Information Model Based on Knowledge Graph

There are heterogeneous problems between the CAD model and the assembly process document. In the planning stage of assembly process, these heterogeneous problems can decrease the efficiency of information interaction. Based on knowledge graph, this paper proposes an assembly information model (KGAM) to integrate geometric information from CAD model, non-geometric information and semantic information from assembly process document. KGAM describes the integrated assembly process information as a knowledge graph in the form of “entity-relationship-entity” and “entity-attribute-value”, which can improve the efficiency of information interaction. Taking the trial assembly stage of a certain type of aero-engine compressor rotor component as an example, KGAM is used to get its assembly process knowledge graph. The trial data show the query and update rate of assembly attribute information is improved by more than once. And the query and update rate of assembly semantic information is improved by more than twice. In conclusion, KGAM can solve the heterogeneous problems between the CAD model and the assembly process document and improve the information interaction efficiency.

A Deep Learning-Based Method to Detect Components from Scanned Structural Drawings for Reconstructing 3D Models

Among various building information model (BIM) reconstruction methods for existing building, image-based method can identify building components from scanned as-built drawings and has won great attention due to its lower cost, less professional operators and better reconstruction performance. However, this kind of method will cost a great deal of time to design and extract features. Moreover, the manually extracted features have poor robustness and contain less non-geometric information. In order to solve this problem, this paper proposes a deep learning-based method to detect building components from scanned 2D drawings. Taking structural drawings as an example, in this article, 1500 images of structural drawings were firstly collected and preprocessed to guarantee the quality of data. After that, the neural network model—You Only Look Once (YOLO) was trained, verified and tested. In addition, a series of metrics were utilized to evaluate the performance of recognition. The results of test experiments show that the components in structural drawings (e.g., grid reference, column and beam) can be successfully detected, while the average detection accuracy of the whole image is over 80% and the average detection time for each image is 0.71 s. The experimental results demonstrate that the proposed method is robust and timesaving, which provides a good basis for the reconstruction of BIM from 2D drawings.

Persistent Cohomology for Data With Multicomponent Heterogeneous Information.

Persistent homology is a powerful tool for characterizing the topology of a data set at various geometric scales. When applied to the description of molecular structures, persistent homology can capture the multiscale geometric features and reveal certain interaction patterns in terms of topological invariants. However, in addition to the geometric information, there is a wide variety of nongeometric information of molecular structures, such as element types, atomic partial charges, atomic pairwise interactions, and electrostatic potential functions, that is not described by persistent homology. Although element-specific homology and electrostatic persistent homology can encode some nongeometric information into geometry based topological invariants, it is desirable to have a mathematical paradigm to systematically embed both geometric and nongeometric information, i.e., multicomponent heterogeneous information, into unified topological representations. To this end, we propose a persistent cohomology based framework for the enriched representation of data. In our framework, nongeometric information can either be distributed globally or reside locally on the datasets in the geometric sense and can be properly defined on topological spaces, i.e., simplicial complexes. Using the proposed persistent cohomology based framework, enriched barcodes are extracted from datasets to represent heterogeneous information. We consider a variety of datasets to validate the present formulation and illustrate the usefulness of the proposed method based on persistent cohomology. It is found that the proposed framework outperforms or at least matches the state-of-the-art methods in the protein-ligand binding affinity prediction from massive biomolecular datasets without resorting to any deep learning formulation.

The Comparison of Diffeomorphic Images Based on the Construction of Persistent Homology

An object shape analysis is a problem that is related to such areas as geometry, topology, image processing and machine learning. For analyzing the form, the deformation between the source and terminal form of the object is estimated. The most used form analysis model is the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. The LDDMM model can be supplemented with functional non-geometric information about objects (volume, color, formation time). The paper considers algorithms for constructing sets of barcodes for comparing diffeomorphic images, which are real values taken by persistent homology. A distinctive feature of the use of persistent homology with respect to methods of algebraic topology is to obtain more information about the shape of the object. An important direction of the application of persistent homology is the study invariants of big data. A method based on persistent cohomology is proposed that combines persistent homology technologies with embedded non-geometric information presented as functions of simplicial complexes. The proposed structure of extended barcodes using cohomology increases the effectiveness of persistent homology methods. A modification of the Wasserstein method for finding the distance between images by introducing non-geometric information was proposed. The possibility of the formation of barcodes of images invariant to transformations of rotation, shift and similarity is considered.

Comparison of Objects’ Images based on Computational Topology Methods

The paper considers methods for comparison of objects’ images represented by sets of points using computational topology methods. The algorithms for construction of sets of real barcodes for comparison of objects’ images are proposed. The determination of barcodes of object forms allows us to study continuous and discrete structures, making it useful in computational topology. A distinctive feature of the use of the proposed comparison methods versus the methods of algebraic topology is obtaining more information about objects’ form. An important area of application of real-valued barcodes is studying invariants of big data. Proposed method combines the technology of barcodes construction with embedded non-geometrical information (color, time of formation, pen pressure), represented as functions of simplicial complexes. To do this, barcodes are expanded with functions from simplexes to represent heterogeneous information. The proposed structure of extended barcodes increases the effectiveness of persistent homology methods when comparing images and pattern recognition. A modification of the Wasserstein method is proposed for finding the distance between images by introducing non-geometric information about the distances between images, due to inequalities of the functions of the source and terminal images of the corresponding simplexes. The geometric characteristics of an object can change with diffeomorphic deformations; the proposed algorithms for the formation of expanded image barcodes are invariant to rotation and translation transformations. We considered a method for determining the distance between sets of points representing the curves, taking into account an orientation of curves’ segments. The article is intended for a reader who is familiar with basic concepts of algebraic and computational topology, the theory of Lie groups, and diffeomorphic transformations.