A knowledge model-based BIM framework for automatic code-compliant quantity take-off

The results of quantity take-off (QTO) based on building information modeling (BIM) technology rely heavily on the geometry and semantics of 3D objects that may vary among BIM model creation methods. Furthermore, conventional BIM models do not contain all the required information for automatic QTO and the results do not follow the descriptive rules in the standard method of measurement (SMM). This paper presents a new knowledge model-based framework that incorporates the semantic information and SMM rules in BIM for automatic code-compliant QTO. It begins with domain knowledge modeling, taking into consideration QTO-related information, semantic QTO entities and relationships, and SMM logic formulation. Subsequently, linguistic-based approaches are developed to automatically audit the BIM model integrity for QTO purposes, with QTO algorithms developed and used in a case study for demonstration. The results indicate that the proposed new framework automatically identifies the semantic errors in BIM models and obtains code-compliant quantities

Deconstruction waste management through 3d reconstruction and bim: a case study

The construction industry is responsible for 50% of the solid waste generated worldwide. Governments around the world formulate legislation and regulations concerning recycling and re-using building materials, aiming to reduce waste and environmental impact. Researchers have also been developing strategies and models of waste management for construction and demolition of buildings. The application of Building Information Modeling (BIM) is an example of this. BIM is emergent technology commonly used to maximize the efficiency of design, construction and maintenance throughout the entire lifecycle. The uses of BIM on deconstruction or demolition are not common; especially the fixtures and fittings of buildings are not considered in BIM models. The development of BIM is based on two-dimensional drawings or sketches, which may not be accurately converted to 3D BIM models. In addition, previous researches mainly focused on construction waste management. There are few studies about the deconstruction waste management focusing on demolition. To fill this gap, this paper aims to develop a framework using a reconstructed 3D model with BIM, for the purpose of improving BIM accuracy and thus developing a deconstruction waste management system to improve demolition efficiency, effective recycling and cost savings. In particular, the developed as-built BIM will be used to identify and measure recyclable materials, as well as to develop a plan for the recycling process

Construction and demolition waste - a shift toward Lean Construction and Building Information Model

Waste in the construction industry is a devastating dilemma, especially that construction and demolition activities are considered as the highest waste generator globally. Countries have developed regulations: policy-makers and professional associations have provided norms and policies to manage C&D waste. Previous studies, however, have revealed insufficiencies in the current regulations and norms in incentivizing the industry practices toward waste prevention, since its culture is characterized by the gap in technological use, insufficient knowledge, poor planning, and poor information flow. This research provides a literature review on the current research findings and trends in managing C&D waste. Then based on design theory and theory of production, an exploratory research consisting of BIM and Lean construction concepts is provided. Lean can maximize the value of construction by addressing waste within portfolios, projects, and operations; BIM offers an enhanced collaborative platform with improved design practice and information management throughout buildings’ life cycle. The proposed conceptual framework enables economic, environmental, and social benefits to allow practitioners collaborate, analyze, and minimize construction waste throughout buildings’ life cycle.(undefined

Immunogenic Salivary Proteins of Triatoma infestans: Development of a Recombinant Antigen for the Detection of Low-Level Infestation of Triatomines

Chagas disease, caused by Trypanosoma cruzi, is a neglected disease with 20 million people at risk in Latin America. The main control strategies are based on insecticide spraying to eliminate the domestic vectors, the most effective of which is Triatoma infestans. This approach has been very successful in some areas. However, there is a constant risk of recrudescence in once-endemic regions resulting from the re-establishment of T. infestans and the invasion of other triatomine species. To detect low-level infestations of triatomines after insecticide spraying, we have developed a new epidemiological tool based on host responses against salivary antigens of T. infestans. We identified and synthesized a highly immunogenic salivary protein. This protein was used successfully to detect differences in the infestation level of T. infestans of households in Bolivia and the exposure to other triatomine species. The development of such an exposure marker to detect low-level infestation may also be a useful tool for other disease vectors

A novel Data-Driven framework based on BIM and knowledge graph for automatic model auditing and Quantity Take-off

Model auditing is a critical step before conducting Building Information Modeling (BIM)-based Quantity Take-off (QTO) because these models may contain various human errors and mistakes, leading to insufficient semantic information and inconsistent modeling style in BIM models. The traditional object-oriented approach has difficulties in representing unstructured BIM data (e.g., interrelationships), while rule-based methods involve tremendous human efforts to develop rule sets, lacking flexibility for different requirements. Therefore, this study aims to establish a novel data-driven framework based on BIM and knowledge graph (KG) to represent unstructured BIM data for automatic inferences of auditing results of BIM model mistakes. It starts by establishing a BIM-KG data model via identifying required information for auditing purposes. Subsequently, BIM data is automatically transformed into the BIM-KG representations, the embeddings of which are trained using a knowledge graph embedding model. Automatic mechanisms are then developed to utilize the computable embeddings to effectively identify mistake BIM elements. The framework is validated using illustrative examples and the results show that 100% mistake elements can be identified successfully without human intervention