Alberto Pavan

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.

The integrative and transformative potential of Building Information Modeling (BIM) has significantly expanded employment opportunities in the construction sector. This study investigates the consistency between BIM-related job market demands and the UNI 11337-7 role definitions in Italy, contributing to the broader dialogue on the alignment between employment, policy, and education. A dataset of 261 BIM-related job advertisements collected from LinkedIn in the Italian context was analyzed using a term frequency-based methodology. Reference keyword sets derived from the UNI 11337-7 standard were established for the BIM Specialist, BIM Coordinator, BIM Manager, and CDE Manager roles. The analysis reveals a significant misalignment: only 30% of the advertisements clearly reflect the competencies defined by the standard, while 70% appear vague or inconsistent. These findings suggest limited awareness or uneven application of BIM role definitions within the industry. This study offers a quantitative assessment of the alignment between employment demand and professional standards and proposes a replicable method for benchmarking job market data. It also highlights the need for clearer communication and stronger implementation of standardized professional roles to effectively support the digital transformation of the construction 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.

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.

Building Information Modelling (BIM) revolutionizes the construction industry by digitally simulating real-world entities through a defined and shared semantic structure. However, graphical information included in BIM models often contains more detailed data compared to the corresponding semantic or computable data. This inconsistency creates an asymmetry, where valuable details present in the graphical renderings are absent from the semantic description of the model. Such an issue limits the accuracy and comprehensiveness of BIM models, constraining their full utilization for efficient decision-making and collaboration in the construction process. To tackle this challenge, this paper presents a novel approach that utilizes Machine Learning (ML) to mediate the disparity between graphical and semantic information. The proposed methodology operates by automatically extracting relevant details from graphical information and transforming them into semantically meaningful and computable data. A comprehensive empirical evaluation shows that the presented approach effectively bridges the gap between graphical and computable information with an accuracy of over 80% on average, unlocking the potential for a more accurate representation of information within BIM models and enhancing decision-making and collaboration/utility in construction processes.

Effective cost estimation for tendering plays a critical role in the building construction process, enabling efficient investment management and ensuring successful execution of the construction phase. Traditional cost estimation procedure involves manual information processing to extract and match technical data from textual description construction resources. This activity requires practitioner deep experience and manual effort, often resulting in errors and, in the worst scenario, judicial disputes. In response to the increasing demand for structured information and automated processes, this study addresses the need for Public Administrations to achieve better control over the data contained in public tendering documents provided to practitioners. To fulfill this objective, a framework is proposed to automatically retrieve information from these documents, serving as a support tool to map items within the documents, highlight missing data, and critical semantic ambiguity. The designed framework aims to develop a tool for automatically identifying similarities between work items and their corresponding elementary resource items in Price List tendering documents. By leveraging the information retrieval NLP technique of cosine similarity through TF-IDF, a methodology was developed to support and facilitate practitioners' activities. Finally, the framework was tested on four case studies extracted from Lombardy Regional Italian price list documents showing that the resulting support tool is able to automate the analysis process and efficiently reveal inconsistency. The model successfully extracted and correctly matched the elementary resource to the corresponding work query in 75% of the cases where the elementary resource was present in the list. Additionally, the model proved to be a valuable tool in helping practitioners identify missing resources