Data-Driven Process Mining Framework for Risk Management in Construction Projects

Construction Projects are exposed to numerous risks due to their complex and uncertain nature, threatening the realization of the project objectives. However, Risk Management (RM) is a less efficient realm in the industry than other knowledge areas given the manual and time-consuming nature of its processes and reliance on experience-based subjective judgments. This research proposes a Process Mining-based framework for detecting, monitoring, and analysing risks, improving the RM processes using evidence-based event logs, such as Risk Registers and Change-Logs within previous projects' documents. Process Mining (PM) is a data- driven methodology, well established in other industries, that benefits from Artificial Intelligence(AI) to identify trends and complex patterns among event logs. It performs well while intaking large amounts of data and predicting future outputs based on historical data. Therefore, this research proposes a Bayesian Network (BN)-based Process Mining framework for graphical representation of the RM processes, intaking the conditional dependence structure between Risk variables, and continuous and automated risk identification and management. A systematic literature review on RM, PM, and AI forms the framework theoretical basis and delineates the integration areas for practical implementation. The proposed framework is applied to a small database of 20 projects as the case study, the scope of which can be tailored to the enterprise requirements. It contributes to creating a holistic theoretical foundation and practical workflow applicable to construction projects and filling the knowledge gap in inefficient and discrete conventional RM methods, which ignore the interdependencies between risk variables and assess each risk isolated

GeoBIM for built environment condition assessment supporting asset management decision making

The digital transformation in management of the built environment is more and more evident. While the benefits of location data, from Building Information Modelling or Geographical Information Systems, have been explored separately, their combination - GeoBIM - in asset management has never been explored. Data collection for condition assessment is challenging due to quantity, types, frequency and quality of data. We first describe the opportunities and challenges of GeoBIM for condition assessment. The theoretical approach is then validated developing an integrated GeoBIM model of the digital built environment, for a neighbourhood in Milan, Italy. Data are collected, linked, processed and analysed, through multiple software platforms, providing relevant information for asset management decision making. Good results are achieved in rapid massive data collection, improved visualisation, and analysis. While further testing and development is required, the case study outcomes demonstrated the innovation and the mid-term service-oriented potential of the proposed approach

Data-drive decision support system for selecting building retrofit strategies

The building sector in EU countries is primarily comprised of outdated and inefficient structures, which are of high energy consumption and seismic vulnerability. As a result, building retrofit is being stressed as a viable option for addressing existing energy and seismic issues in the construction industry, particularly in residential properties. For this purpose, strategic decisions should be made about the retrofit strategies, which require great time, effort, resources, and expertise. While traditional case-based retrofit scenarios fail to provide rapid and objective solutions, data-driven methods such as Artificial Intelligence (AI) technologies can serve as an effective and efficient decision support system for selecting retrofit strategies. This research offers a clustering of residential properties in the CENED database (Lombardia 2007)(comprising over 1 million energy labels of residential properties), based on the construction year and U-value. These clusters are associated with the type of material and building technique using the National scientific report on the TABULA activities (Corrado, Ballarini, and Corgnati 2012), and the probability distribution of EHP values. Therefore considering a given U-value and an energy class, the most optimum retrofit strategy is suggested to obtain a particular energy label. This research benefits from AI technologies to enhance strategic decision-making for building retrofit by connecting the current dispersed databases. It also helps increase energy-saving on an urban level

BIM AND GIS INTEGRATION FOR INFRASTRUCTURE ASSET MANAGEMENT: A BIBLIOMETRIC ANALYSIS

The integration of Building Information Modelling (BIM) and Geographical Information Systems (GIS) is gaining momentum in digital built Asset Management (AM), and has the potential to improve information management operations and provide advantages in process control and delivery of quality AM services, along with underlying data management benefits through entire life cycle of an asset. Work has been carried out relating GeoBIM/AM to buildings as well as infrastructure assets, where the potential financial savings are extensive. While information form BIM maybe be sufficient for building-AM; for infrastructure AM a combination of GIS and BIM is required. Scientific literature relating to this topic has been growing in recent years and has now reached a point where a systematic analysis of current and potential uses of GeoBIM in AM for Infrastructure is possible. Three specific areas form part of the analysis – a review of BIM and Infrastructure AM and GIS and Infrastructure AM leads to a better understanding of current practice. Combining the two, a review of GeoBIM and Infrastructure AM allows the benefits of, and issues relating to, GeoBIM to be clearly identified, both at technical and operational levels. A set of 54 journal articles was selected for in-depth contents analysis according to the AM function addressed and the managed asset class. The analysis enabled the identification of three categories of issues and opportunities: data management, interoperability and integration and AM process and service management. The identified knowledge gaps, in turn, underpin problem definition for the next phases of research into GeoBIM for infrastructure AM

La scheda interattiva di INNOVance per prodotti in laterizio

Anche per i laterizi la scheda tecnica di prodotto, sviluppata nell’ambito del pro getto INNOVance, assume un ruolo fondamentale a supporto della progettazione e della gestione delle informazioni nelle diverse fasi del processo edilizio. L’insieme di dati contenuti in ciascuna scheda sarà associato alla rappresentazione di oggetti innovativi di tipo BIM 'Building Information Model

Application of Artificial Neutral Network and Geographic Information System to Evaluate Retrofit Potential in Public School Buildings

School buildings in Italy are old, in critical maintenance conditions and they often perform below acceptable service levels. Nevertheless, data to guide renovation policies are missing or very expensive to retrieve. This paper presents a methodology for evaluating building’s energy savings potential, using the Certificazione Energetica degli Edifici (CENED) database, concerning energy performance labelling. Data are first clustered to identify most common thermo-physical properties. Three retrofit scenarios are then defined and energy savings potential, for each of the three, is evaluated through eight neural networks. Ultimately, data are geocoded and further processed to guide the definition of the retrofit strategy in most critical areas in Lombardy region. The results of the three scenarios proved that the highest energy savings can be obtained through retrofit interventions on around 50% of buildings. In conclusion, further insights on retrofit costs analysis and future development of the research are discussed

Thermal and Economic Efficiency of Progressive Retrofit Strategies for School Buildings by a Statistical Analysis based Tool

Design alternatives in air conditioned buildings may be easily compared just by summing the hourly consumption of primary energy, while quantitative approachs for bioclimatic design strategies are difficult to be assessed and compared. A actively heated and passively cooled school building is considered as an application field of a novel methodology to promote an informed choice about the retrofit strategies to be adopted for buildings, defined as the Gained Comfort Cost (GCC). A functional and significant unit (i.e. a classroom), is used to test different energy retrofit solutions and their performances were compared with a baseline, in terms of the capacity to reduce the indoor air temperature variation. The novel methodology is a visual tool allowing to understand the “distance” of indoor conditions from comfort; the retrofit strategies are promoted to reduce this distance considering however the associated costs (LCC) to deal with actual feasibility