Purpose: The paper clarifies research gaps and future directions in building information modeling (BIM) research by analyzing research trends and publication patterns. It aims to (1) systematically categorize the vast array of BIM literature into coherent main topics, (2) identify the most and least explored areas and (3) propose directions for future research based on identified research gaps. Design/methodology/approach: This study uses the Latent Dirichlet Allocation (LDA) method to manage large datasets and uncover hidden patterns in academic journals and conference articles. To clarify the scholarly focus, the main topics in BIM research are categorized into three groups: (1) primary areas of focus, (2) moderately explored topics and (3) least investigated topics. Findings: The findings revealed 10 main topics (MTs) and 57 subtopics (STs), identifying key areas such as project design and management (20%), innovative construction technology (14%) and sustainable construction/life cycle management (14%). Conversely, it also highlighted underexplored areas like Facility/safety management and urban data development, suitable for future research. Research limitations/implications: While this work provides a structured overview of the BIM domain, it reveals opportunities for further exploring the complexity of the interrelation among interdisciplinary topics. Originality/value: The novelty of this study is its extensive scope, analyzing over fifteen thousand BIM articles from 2013 to 2023, which significantly expands the literature scale previously reviewed. This comprehensive approach maps BIM research trends and gaps and also shows the hierarchical trend line of publications in each main topic, setting a benchmark for future studies.

Cost estimation for tendering is one of the leading causes of legal disputes in the architecture, engineering, construction, and facilities management (AEC/FM) industry. To date, there are no standardized procedures for verifying cost estimation accuracy. The association between cost data and geometric model objects is currently done manually, leading to time losses and inaccuracies in cost estimates. This study defines a new architecture for cost items within the Industry Foundation Classes (IFC) data model to ensure structured cost data, which is currently expressed in unstructured natural language. It encompasses the definition of a cost domain in IFC, potential relationships it may have with an IFC geometric information model, and the validation of these relationships through semi-automated verification procedures of structured data (geometric-cost). A specific case study, focused on a structural IFC model, is examined to assess current and future applications. Additionally, rules for BIM information requirements will be defined through the Information Delivery Specification (IDS) to ensure clear understanding for both humans and computers. This will specify which data must be included in the geometric model to ensure validation and verification of the uniqueness of associated cost data. The results demonstrate the feasibility of defining structured cost elements in IFC and verifying their association with geometric data to ensure coherence and uniqueness in cost estimation.

Cost estimation is one of the most critical tasks in the architecture, engineering, construction, and facilities management industry (AEC/FM). The lack of a standardized support procedure for verifying the association of the cost to geometric objects cause a waste of time, inaccuracy and human errors in the cost estimates.This research investigates how to identify a cost ontology within the Industry Foundation Classes (IFC) database.The results show that we can consider the cost element no longer as a natural language text string, detached from the digital model, but as a structured class in the IFC data model (IfcCostItem).

The increasing integration of digital technologies in the construction sector is transforming the processes of buildings design, management, and evaluation throughout their life cycle. Life Cycle Costing (LCC), Building Information Modeling (BIM), and openBIM standards play a key role in promoting economic and environmental sustainability. More recently, Artificial Intelligence (AI) has unlocked novel possibilities for data-driven decision-making and cost optimization. However, the integration of LCC, BIM, and AI is insufficiently explored in the current literature. This study presents a systematic literature review (SLR) aimed at analyzing two distinct lines of research, LCC–BIM and LCC–AI, and identifying underexplored opportunities for their future convergence. A dual-stream approach was adopted to analyze scientific contributions based on LCC–BIM and LCC–AI separately, using bibliometric analysis and the systematic screening of peer-reviewed articles from 2015 to 2025. The findings reveal that while LCC–BIM integration shows growing methodological maturity, AI-based applications are still in an early stage, with limited implementation in construction-specific contexts. The review identifies key challenges, including data fragmentation, a lack of interoperability, and limited standardization, as significant impediments to integrated digital workflows. By highlighting these gaps and proposing actionable future directions, the paper outlines future research directions focused on open data models, AI-enhanced cost estimation, and the development of interoperable frameworks to support sustainable and intelligent cost management in the Architecture, Engineering, and Construction (AEC) sector.

The AECO (Architecture, Engineering, Construction, and Operation) sector is highly complex, involving multidisciplinary collaboration, extensive data management, and significant financial investments. Decisions in early phases significantly impact operational and maintenance costs, as well as the environmental and economic sustainability of a project over its lifecycle. Cost efficiency and sustainability are critical and interconnected goals across the sector, spanning all phases of a building’s lifecycle. Ontologies, as formal and structured representations of knowledge within a particular domain, have the potential to enhance cost efficiency by improving decision-making, reducing redundancies, and optimizing resource allocation. Despite their relevance, cost ontologies are still lacking in the AECO sector. This paper addresses this gap by presenting both a methodological and conceptual contribution: it outlines a structured and iterative methodology for developing a cost ontology, and it defines the core concepts required to semantically represent construction cost information. The methodology emphasizes stakeholder engagement and refinement cycles, while the ontological structure ensures machine-readability and interoperability. The approach involves a preliminary analysis of the necessary cost parameters for defining the ontology and a subsequent validation of a practical case study. The results show the development of a heterogeneous and standardized data structure designed to define a cost ontology, aimed at improving the updatability, transparency, and sustainability-oriented interpretation of construction cost data by both humans and machines.

Achieving cost-effective energy performance while meeting sustainability goals is a challenge in retrofitting decisions within the construction industry. To enhance the decision-making process, this study introduces an IFC-based approach that integrates cost estimation and energy analysis directly within BIM. This approach supports more structured and data-informed retrofit planning by structuring cost and energy data within a semi-automated IFC-based workflow. The methodology follows a structured approach that includes three phases. The first focuses on developing a BIM model that captures the physical and semantic attributes of an existing building. This is followed by parametric energy simulations to evaluate retrofit scenarios, with cost data integrated and energy analysis reports linked to IFC elements. The final phase involves a post-retrofit cost assessment to identify the optimal scenario based on total cost, with potential for extension to other performance indicators. The framework was applied in a residential case study to evaluate the model’s functionality. The results show that IFC-based integration improves transparency, interoperability, and reliability in cost–energy assessments. By structuring data as linked IFC entities, the approach enhances BIM’s role as a decision-support tool for sustainable and economically efficient retrofitting.