Automatic building information model query generation

Energy efficient building design and construction calls for extensive collaboration between different subfields of the Architecture, Engineering and Construction (AEC) community. Performing building design and construction engineering raises challenges on data integration and software interoperability. Using Building Information Modeling (BIM) data hub to host and integrate building models is a promising solution to address those challenges, which can ease building design information management. However, the partial model query mechanism of current BIM data hub collaboration model has several limitations, which prevents designers and engineers to take advantage of BIM. To address this problem, we propose a general and effective approach to generate query code based on a Model View Definition (MVD). This approach is demonstrated through a software prototype called QueryGenerator. By demonstrating a case study using multi-zone air flow analysis, we show how our approach and tool can help domain experts to use BIM to drive building design with less labour and lower overhead cost.published_or_final_versio

Comparative analysis of the applicability of BIM query languages for energy analysis

Paper no. 036A range of query languages have been used or developed to query partial information from Building Information Model (BIM)-based databases and files in recent decades. This paper aims to investigate the applicability of existing BIM query languages to extract necessary information from BIMs for energy analysis. A total of 16 query languages categorized into two groups, namely programming or generic query language, and domain specific query language, are summarized through extensive literature review. The key requirements of BIM data query for energy analysis are also developed, which include MVD based query support, custom query support, and easiness to construct queries. Taking these requirements as the criteria, the applicability of the 16 query languages is compared and analyzed. This paper then proposes some suggestions for developing effective and efficient building information query mechanisms for energy analysis.postprin

Building Information Modeling (BIM) And Building Energy Modeling (BEM): Assessment of BIM-BEM Workflows And Energy Simulation Tools

Interoperability and Integration of the Building Information Modeling (BIM) and Building Energy Modeling (BEM) tools are major challenges for the Architecture, Engineering, and Construction (AEC) industry. The goals of this dissertation were to (i) investigate various BEM tools to evaluate their workflow and integration with BIM and (ii) assess BEM tools’ accuracy in predicting/simulating whole building energy performance from a broader lens to a more specific one (i.e., EUI to end-use data, respectively). To conduct the research study, case study buildings were selected from various categories, representing different building types (i.e., academic, administrative, and recreational). The case study buildings were located on the campus of the University of Massachusetts Amherst (UMass Amherst), and the main criterion in their selection was the accessibility of their construction document and measured energy data. BEM tools from three categories of BIM-integrated, BIM-interoperable, and BIM-separated BEM tools were selected, aiming to evaluate design-analysis workflows. To comparatively analyze the simulated energy data against each other and measured data, case study buildings’ actual energy data was collected and used. The research findings suggest potential developments that are essential to accomplish a streamlined BIM to BEM workflow, making it less tedious and time-consuming

Semantics-based approach for generating partial views from linked life-cycle highway project data

The purpose of this dissertation is to develop methods that can assist data integration and extraction from heterogeneous sources generated throughout the life-cycle of a highway project. In the era of computerized technologies, project data is largely available in digital format. Due to the fragmented nature of the civil infrastructure sector, digital data are created and managed separately by different project actors in proprietary data warehouses. The differences in the data structure and semantics greatly hinder the exchange and fully reuse of digital project data. In order to address those issues, this dissertation carries out the following three individual studies. The first study aims to develop a framework for interconnecting heterogeneous life cycle project data into an unified and linked data space. This is an ontology-based framework that consists of two phases: (1) translating proprietary datasets into homogeneous RDF data graphs; and (2) connecting separate data networks to each other. Three domain ontologies for design, construction, and asset condition survey phases are developed to support data transformation. A merged ontology that integrates the domain ontologies is constructed to provide guidance on how to connect data nodes from domain graphs. The second study is to deal with the terminology inconsistency between data sources. An automated method is developed that employs Natural Language Processing (NLP) and machine learning techniques to support constructing a domain specific lexicon from design manuals. The method utilizes pattern rules to extract technical terms from texts and learns their representation vectors using a neural network based word embedding approach. The study also includes the development of an integrated method of minimal-supervised machine learning, clustering analysis, and word vectors, for computing the term semantics and classifying the relations between terms in the target lexicon. In the last study, a data retrieval technique for extracting subsets of an XML civil data schema is designed and tested. The algorithm takes a keyword input of the end user and returns a ranked list of the most relevant XML branches. This study utilizes a lexicon of the highway domain generated from the second study to analyze the semantics of the end user keywords. A context-based similarity measure is introduced to evaluate the relevance between a certain branch in the source schema and the user query. The methods and algorithms resulting from this research were tested using case studies and empirical experiments. The results indicate that the study successfully address the heterogeneity in the structure and terminology of data and enable a fast extraction of sub-models of data. The study is expected to enhance the efficiency in reusing digital data generated throughout the project life-cycle, and contribute to the success in transitioning from paper-based to digital project delivery for civil infrastructure projects

Natural Language Processing using Deep Learning for Classifying Water Infrastructure Procurement Records and Calculating Unit Costs

This thesis introduces a novel ontology-based deep learning classification model specifically tailored for civil engineering applications, focusing on automating the extraction and classification of water infrastructure capital works tenders and progress certificates. Utilizing ontology for standardizing tender-bid data and employing Named Entity Recognition (NERC) for item categorization, the model adeptly addresses the challenges posed by the diversity in document styles and formats. Incorporating Long Short-Term Memory (LSTM) structures within the model enables the learning of both linear and non-linear dependencies between words. This aspect is particularly significant in tackling the unique language constructs present in tender-bid document records. The model's effectiveness is underscored by its impressive classification accuracy, achieving 92.6% for testing data and 98.7% for training data, thereby marking a significant advancement in the field. The practical application of this model through a web server highlights its adaptability and efficiency in real-world scenarios. The model's role in tasks such as unit cost calculation establishes a new benchmark in the industry, showcasing the thesis's innovative contributions in areas like ontology-based data structuring and LSTM-driven automated unit cost computation. Looking beyond its current scope, this research holds potential for broader applications and adaptations in different civil engineering domains. It lays the groundwork for future enhancements, including exploring multilingual extensions and specialized approaches aligned with evolving industry trends. This thesis amalgamates data preprocessing, deep learning, and engineering expertise to boost efficiency and accuracy significantly. The unique methodology fosters continuous improvement and broad applicability across different regions. The practical integration of this technology in civil engineering tasks, demonstrated through the web server, opens avenues for further development to encompass a wider array of applications. Future research directions include refining the framework to cater to the dynamic needs of various civil engineering domains and extending the web server's capabilities for real-time data processing and analysis. Investigating the applicability of this methodology in other engineering or interdisciplinary contexts could also provide valuable insights, extending the utility of this research. This thesis lays a solid foundation for ongoing enhancements in capital work planning and tender contract assessment within the civil engineering industry