User-Centric BIM-Based Framework for HVAC Root-Cause Detection

In the building operation phase, the Heating, Ventilation, and Air-Conditioning (HVAC) equipment are the main contributors to excessive energy consumption unless proper design and maintenance is carried out. Moreover, HVAC problems might have an impact on occupants’ discomfort in thermal comfort. Hence, the identification of the root cause of HVAC problems is imperative for facility managers to plan preventive and corrective maintenance actions. However, due to the complex interaction between various equipment and the lack of data integration among Facility Management (FM) systems, they fail to provide necessary information to identify the root cause of HVAC problems. Building Information Modelling (BIM) is a potential solution for maintenance activities to address the challenges of information reliability and interoperability. Therefore, this paper presents a novel conceptual model and user-centric framework to determine the causes of HVAC problems implemented in BIM for its visualization. CMMS and BMS data were integrated into BIM and utilized by the framework to analyze the root cause of HVAC problems. A case study in a university building was used to demonstrate the applicability of the approach. This framework assists the FM team to determine the most probable cause of an HVAC problem, reducing the time to detect equipment faults, and providing potential actions to solve them.This research received funding from the Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR) from Generalitat de Catalunya (2019 FI_B00064).Peer ReviewedPostprint (published version

Facility managers’ perceptions on building performance assessment

During the operational phase, building performance may decrease in various areas, so that the end users’ requirements are no longer met. Consequently, indicators are useful to assessand improve the performance of existing buildings. In this study, we carried out a literature review and organized a focus group with facility management experts to gather and analyze facility managers’ perceptions on operational indicators that could be used to assess the performance of buildings. The results revealed that the core indicators used to measure a building’s operational performance are related to safety and assets working properly, health and comfort, space functionality, and energy performance. The findings also revealed that these indicators can be obtained from three sources: a) facility managers/operators, who carry out corrective maintenance and perform technical inspections, b) regular users, who report complaints and fill-in satisfaction questionnaires, and c) sporadic users, who also fill-in satisfaction questionnaires. These indicators and their sources can contribute to a better analysis of building performance and the definition of measures to improve performance during the operational phase of a building.Peer ReviewedPostprint (published version

Analysis of building maintenance requests using a text mining approach: building services evaluation

End users’ maintenance requests gathered from computerized maintenance management systems (CMMS) configure a rich source of information to evaluate the occupants’ satisfaction and the building systems. Nevertheless, the non-standardized data gathered from CMMS makes it difficult to carry out analytics. This paper presents a text mining approach to extract information from end users’ maintenance requests and an analysis of 6,830 maintenance requests derived from 46 buildings including offices, academic buildings and laboratories over two and a half years. The research results reveal that the most common maintenance requests during building operation and maintenance are related to problems in electrical and HVAC fixtures. Although the year of construction is not related to the occupants’ maintenance requests, the type of building use and building property do influence them. The implementation of control and preventive strategies based on these results may increase facility managers’ productivity and building systems’ performance.Peer ReviewedPostprint (author's final draft

BIM-based decision support for building condition assessment

Building condition assessment requires the integration of various types of data such as building characteristics, the properties of elements/systems and maintenance records. Previous research has focused on identifying these data and developing a building condition risk assessment model based on Bayesian networks (BN). However, due to interoperability issues, the process of transferring the data is performed manually, which requires considerable time and effort. To address this issue, this paper presents a data model to integrate the building condition risk assessment model into BIM. The proposed data model is implemented in existing software as a case study and tested and evaluated on three scenarios. Addressing interoperability will leverage the BIM tool as a data re- pository to automate the data transfer process and improve its consistency and reliability. It will also enable BIM to be a more effective tool for building condition and causality analysis visualization.This work was supported by Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) from Generalitat de Catalunya under Grant 2019 FI_B00064Postprint (published version

Assessment of the energy implications adopting adaptive thermal comfort models during the cooling season: a case study for mediterranean nursing homes

The growing demand in the use of cooling in buildings for the effects of climate change and the thermal comfort conditions requires the adoption of energy conservation measures. Implementing adaptive thermal comfort models can result in a significant decrease in energy consumption, especially in buildings where the users are groups of vulnerable people. However, no study has proposed a prediction of energy consumption from a comfort-based approach for nursing homes. This article presents the development of adaptive consumption models to assess the energy implications of HVAC systems for the cooling season by measuring real data on energy consumption and environmental conditions. The adaptive consumption models are implemented in eight nursing homes located in two different climates (Mediterranean and Continental-Mediterranean). The findings reveal that adaptive thermal comfort control methods result in important energy savings in comparison to a fixed set point temperature. The study demonstrates a potential average energy savings of up to 9.9 % (8.1 % in Mediterranean climate and 11.7 % in the Continental-Mediterranean climate) for the analysed nursing homes. The prediction of energy consumption from an adaptive comfort-based approach in nursing homes will enhance their energy efficiency ensuring the well-being of their vulnerable residents by maintaining optimal thermal comfort. These findings hold significant value for the effective energy management of buildings in future climate change scenarios and warrant careful consideration by nursing home facility managers.This research was supported by the Spanish Ministry of Economy, Industry and Competitiveness under R&D project Thecoelen, reference no. PID2019-106777RB-C21. Additionally, thanks to Sanitas Mayores and especially the Eng. Marc Vallet and Eng. Albert Ayala for providing their nursing homes and helping to collect all the required data. The authors also would like to extend our appreciation to all the caregivers, maintenance staff and older people who participated in this project. This work was also supported by the Catalan agency AGAUR through its research group support program (2021 SGR 00341).Peer ReviewedObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats SosteniblesPostprint (published version

¿Porqué PERT & GANTT no funciona en proyectos de edificación?

Process engineering applied to building projects has not developed its own methodology for project management, so it has been using generic project management tools, based on PERT and GANTT, under the paradigms Critical Path Method or Critical Chain. Both PERT and GANTT do not consider some variables that are relevant in the specific environment of a construction site, such as: location, repetitive activities, productivity, continuity of work for external crews, material transformation, space clashes, etc. This article analyzes: 1) The theoretical foundations of generic project management tools based on activities management, 2) The significant variables in both production and project management paradigms, 3) The new paradigm and conceptual tools developed over the last 20 years in the Lean Construction community of knowledge. The findings of this research will help construction managers and superintendents, explaining why the standard tools they are using are not enough to manage their regular work, and proposing a more adequate set of tools and systems to plan and control production activities on site.Postprint (published version

Modelling indoor air carbon dioxide concentration using grey-box models

Predictive control is the strategy that has the greatest reported benefits when it is implemented in a building energy management system. Predictive control requires low-order models to assess different scenarios and determine which strategy should be implemented to achieve a good compromise between comfort, energy consumption and energy cost. Usually, a deterministic approach is used to create low-order models to estimate the indoor CO2 concentration using the differential equation of the tracer-gas mass balance. However, the use of stochastic differential equations based on the tracer-gas mass balance is not common. The objective of this paper is to assess the potential of creating predictive models for a specific room using for the first time a stochastic grey-box modelling approach to estimate future CO2 concentrations. First of all, a set of stochastic differential equations are defined. Then, the model parameters are estimated using a maximum likelihood method. Different models are defined, and tested using a set of statistical methods. The approach used combines physical knowledge and information embedded in the monitored data to identify a suitable parametrization for a simple model that is more accurate than commonly used deterministic approaches. As a consequence, predictive control can be easily implemented in energy management systems.Peer ReviewedPostprint (author's final draft

Communication key performance indicators for selecting construction project bidders

It is vital to select the right project bidders, as this affects the success of a project. Although there are numerous methods for assessing bidders, communication is rarely taken into account. This paper discusses the results of a survey on communication key performance indicators (KPIs) and the success of construction projects. Data were collected from 390 construction partners in Spain. The results indicate that the most significant communication KPI is the quality of information: basically, its accuracy and timeliness. In addition, experienced respondents placed less importance on communication flow structures and communication management than did inexperienced respondents. Experienced respondents distrusted new trends and/or management theories and mainly relied on experience. The findings also reveal that the communication flow structure, the communication and information management plan, and the channels of communication are relevant aspects for the success of a project. The results of this research can be used to assess bidders' communication abilities and systems.Peer ReviewedPostprint (author's final draft