Machine learning methods in BIM-based applications : a review

This paper presents a survey of machine learning (ML) methods used in applications dedicated to the building and construction industry. A building information modeling (BIM) model, being a database system for civil engineering data, is presented. A representative selection of methods and applications is described. The aim of this paper is to facilitate the continuation of research efforts and to encourage bigger participation of database system researchers in the field of civil engineering

A Bibliometric Analysis of Generative Design, Algorithmic Design, and Parametric Design in Architecture

This research aims to display, compare, and analyze the keywords related to parametric design, generative design, and algorithmic design. Digital design has become increasingly inseparable from architects; thus, 3D modeling software has become a necessity for architects. The digital workflow has put computational design—generative design, algorithmic design, and parametric design—into importance. There are emerging trends for the past decade, and a bibliometric analysis can display information about trends in the literature. Literature trends may provide insight into the direction of computational design development. This study uses a bibliometric analysis with VOSviewer and data from Lens to identify the trends from 2011 to 2021. The result indicates several trends: artificial intelligence, computation, machine learning, visualization, and internet technology. The trend analysis needs to be continued in other computational design categories to find continuity in the findings

Parameterized IFC-based graph generation for user-oriented path search

This paper deals with the problem of transforming the data obtained from the IFC file of a given building into a weighted graph, which is used for searching shortest routes accessible for different types of users including people and mobile objects. This graph contains information about the topology and accessibility between building spaces. It is created using the parameter specifying the permissible distance from the center of a moving object to a wall. Edge weights are calculated based on the Euclidean distance between nodes representing doors or internal points of rooms with concave shapes. On the basis of information encoded in the graph the application calculates the shortest path between designated rooms and creates its visualization. The presented approach is illustrated on examples of searching shortest routs between spaces of the building extracted from the IFC file belonging to the free IFC model database

Machine Learning Recognition Models in Construction: A Systematic Review

Due to its growing acceptance and success in many sectors, there is a rapidly rising adoption and application of machine learning recognition models within construction. As a result of this adoption and usage surge, there is copious knowledge residing in different repositories. This surge makes it a daunting task for researchers and other stakeholders to access concise and summarised evidence of existing research showing the usage and adoption of different recognition models in construction. As a result, a systematic review of machine learning recognition models with their different applications in construction is inevitable. We leveraged PRISMA protocol and PICOC technique to retrieve 819 construction-related studies from SCOPUS. We grouped recognition models into Image Recognition, Pattern Recognition, Voice Recognition, and Natural Language Processing (NLP). Our thorough analysis and approach show that 53% of existing studies use Pattern Recognition, 42% Image Recognition, and 2% Voice Recognition. We identified that 45% of the studies focused on buildings, 31% on worker's health and safety, while 24% was on equipment detection, efficiency, and usage. We recommend that future studies leverage the textual and voice data generated from construction-related activities and studies. This will build more voice and NLP recognition models for training robots and other assistive technologies that can support construction workers to improve their safety and productivity. This study will guide researchers and other stakeholders in this field to widen their horizons on trends in recognition model application to construction, making informed decisions, and establish gaps in the literature while suggesting lasting contributions

Building Information Modelling, Artificial Intelligence and Construction Tech

Development and adoption of digital information tools in the construction sector provides fertile ground for the birth and growth of companies that specialize in applications of technologies to design and construction. While some of the technologies are themselves new, the majority are based on ideas that have proliferated in construction research for decades but could not be implemented without a sound digital building information foundation. Building Information Modelling (BIM) itself can be traced to a landmark paper from 1975; ideas for artificially intelligent design and code checking tools date from the mid-1980s; and construction robots have laboured in research labs for decades. Yet it is only within the past five years that venture capital has actively sought startup companies in the ‘Construction Tech’ sector. We follow a set of digital construction innovations through their known past and their uncertain present, and we review their increasingly optimistic future, all through the lens of their dependence on digital information. The review identifies new challenges, yielding a set of research topics with the potential to unlock a range of future applications that make extensive use of artificial intelligence.Centre for Digital Built Britai

Stacked Denoising Autoencoders and Transfer Learning for Immunogold Particles Detection and Recognition

In this paper we present a system for the detection of immunogold particles and a Transfer Learning (TL) framework for the recognition of these immunogold particles. Immunogold particles are part of a high-magnification method for the selective localization of biological molecules at the subcellular level only visible through Electron Microscopy. The number of immunogold particles in the cell walls allows the assessment of the differences in their compositions providing a tool to analise the quality of different plants. For its quantization one requires a laborious manual labeling (or annotation) of images containing hundreds of particles. The system that is proposed in this paper can leverage significantly the burden of this manual task. For particle detection we use a LoG filter coupled with a SDA. In order to improve the recognition, we also study the applicability of TL settings for immunogold recognition. TL reuses the learning model of a source problem on other datasets (target problems) containing particles of different sizes. The proposed system was developed to solve a particular problem on maize cells, namely to determine the composition of cell wall ingrowths in endosperm transfer cells. This novel dataset as well as the code for reproducing our experiments is made publicly available. We determined that the LoG detector alone attained more than 84\% of accuracy with the F-measure. Developing immunogold recognition with TL also provided superior performance when compared with the baseline models augmenting the accuracy rates by 10\%