Text mining (TM) and natural language processing (NLP) have stirred interest within the construction field, as they offer enhanced capabilities for managing and analyzing text-based information. This highlights the need for a systematic review to identify the status quo, gaps, and future directions from the perspective of construction management. A review was conducted by aligning the objectives of 205 publications with the specific domains, areas, tasks, and processes outlined in construction management practices. This review reveals multiple facets of the construction sector empowered by TM/NLP approaches and highlights essential voids demanding consideration for automation possibilities and minimizing manual tasks. Ultimately, following identified obstacles, the review results indicate potential research opportunities: (1) strengthening overlooked construction aspects, (2) coupling diverse data formats, and (3) leveraging pre-trained language models and reinforcement learning. The findings will provide vital insights, fostering further progress in TM/NLP research and its applications in academia and industry. 

Text mining and natural language processing in construction

Comprehensive digital twin for infrastructure: A novel ontology and graph-based modelling paradigm

Building Information Modelling (BIM) has been increasingly integrated with Artificial Intelligence (AI) solutions to automate building construction processes. However, the methods for effectively transforming data from BIM formats, such as Industry Foundation Classes (IFC), into formats suitable for AI applications still need to be explored. This paper conducts a Systematic Literature Review (SLR) following the PRISMA guidelines to analyse current data preparation approaches in BIM applications. The goal is to identify the most suitable methods for AI integration by reviewing current data preparation practices in BIM applications. The review included a total of 93 articles from SCOPUS and WoS. The results include eight common data types, two data management frameworks, and four primary data conversion methods. Further analysis identified three barriers: first, the IFC format’s lack of support for time-series data; second, limitations in extracting geometric information from BIM models; and third, the absence of established toolchains to convert IFC files into usable formats. Based on the evidence, the data readiness is at an intermediate level. This research may serve as a guideline for future studies to address the limitations in data preparation within BIM for AI integration. 

BIM and IFC Data Readiness for AI Integration in the Construction Industry: A Review Approach

The built environment is subject to various defects as it ages. A well-maintained built environment depends on surveying activities to inspect, document, and rehabilitate the defects that occurred. The advancement of digital technologies paves the pathway towards (1) comprehensive defect inspection by systematic mapping, (2) their consistent documentation by digital modeling, and (3) timely retrofitting by proactive management. However, the three steps of defect &nbsp;map- ping, modeling, and management (D3M) remain largely fragmented and have yet to be synergized. Exploiting the pivotal role of building information modeling(BIM) in built asset management, this paper puts forward a cohesive framework for integrated D3M. It leverages the rich geometric-semantic information in BIM to assist defect mapping and enriches the BIM by industry foundation classes (IFCs)-represented defect information. The defect-enriched BIM facilitates defect management in a data-driven manner. The framework was applied in multiple real-life infrastructure and civil works projects. It demonstrates how the BIM-based D3M framework can enhance the maintenance of those that have been built, and ultimately contribute to a safe and sustainable built &nbsp;envi- ronment. Future studies are called for to substantiate each of the 3Ms by leveraging BIM as both an enabler and a beneficiary. 

Built environment defect mapping, modeling, and management(D3M): A BIM-based integrated framework

The assembly of prefabricated components is a critical process in prefabricated building construction, influencing both progress and accuracy. However, the assembly sequence planning and optimization (ASPO) of prefabricated components have yet to receive sufficient attention from researchers, and current research has displayed limited automation and poor generalization capabilities. Therefore, this paper proposes a framework for intelligently generating assembly sequences for prefabricated components based on graph databases and matrices. The framework utilizes an adjacency matrix and interference matrix-based modeling method to comprehensively describe the connections and constraint relationships between components, enabling better evaluation of assembly difficulty during optimization. The graph database serves as the central hub for data exchange, facilitating component information storage, automatic querying, and summarization. The obtained assembly sequence and progress plan are fed back into the graph database. To accomplish assembly sequence optimization, a genetic algorithm based on the double-elite strategy is employed. Furthermore, the effectiveness of the proposed framework is validated through an actual engineering case. The results demonstrate that the framework can effectively find an optimal assembly sequence to mitigate the assembly challenge of a prefabricated building. 

Graph Database and Matrix-Based Intelligent Generation of the Assembly Sequence of Prefabricated Building Components

Element property is a crucial aspect of building information modeling (BIM) for almost all BIM‐based engineering tasks. Since there are limited properties predefined in Industry Foundation Classes (IFC) specifications, a vast number of property concepts were customized and stored in BIM models, which lack labor‐intensive data modeling and alignment for effective information management and reuse. To tackle the challenge, this study presents a natural language understanding (NLU)‐based method for the automatic ontological knowledge modeling of project‐specific property concepts from BIM models. A soft pattern matching model was used to acquire contextual definitions of concepts from a domain corpus before applying deep NLU models to transform the concept names and definitions into dense vector representations. These outputs were then fed into two stacking ensemble learning models to carry out two tasks: (a) classifying whether an unseen concept overlaps with the IFC ontology, and (b) aligning the repetitive concepts with the most relevant concepts in the ontology. Finally, all fresh properties were appended to an IFC ontology, either as new objects or new synonyms. The performance was evaluated based on 327 property concepts from real‐life BIM models. The results show that the proposed approach incorporating reading comprehension of definitions outperforms the existing name similarity‐based methods. Finally, a case study on a renovation project demonstrates the effectiveness of this study in automatic ontology modeling of property concepts.

A deep natural language processing‐based method for ontology learning of project‐specific properties from building information models

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A deep natural language processing‐based method for ontology learning of project‐specific properties from building information models