Classifying system for façades and anomalies

Façades play an important role in buildings’ energy demand, and their state of conservation obviously influences thermal performance. The energy performance gap in existing residential buildings due to façade conservation status has not been analyzed in depth. In order to facilitate the systematic analysis of this influence, a system for classifying façades and their corresponding anomalies was developed for the first time. The classification system includes 23 types of façades and eight types of anomalies. It was verified by a panel of experts, and a case study was carried out with a sample of 154 buildings. An analysis of the results showed that the classification system is useful for a future analysis of the energy performance gap in existing residential buildings.Peer ReviewedPostprint (author's final draft

Factors affecting rework costs in construction

Rework adversely impacts the performance of building projects. In this study, data were analyzed from 788 construction incidents in 40 Spanish building projects to determine the influence of project and managerial characteristics on rework costs. Finally, regression analysis was used to understand the relationship between the contributing factors, and to determine a model for rework prediction.Interestingly, the rework prediction model showed that only the original contract value (OCV) and the project location in relation to the company’s headquarters contribute to the regression model. The Project type, the Type of organization, the Type of contract and the original contract duration (OCD) which represents the magnitude and complexity of a project, were represented by the OCV. This model for rework prediction based on original project conditions enables strategies to be put in place prior to the start of construction, to minimize uncertainties and reduce the impact on project cost and schedule, and thus improve productivity.Peer ReviewedPostprint (author's final draft

Predicting fuel energy consumption during earthworks

This research contributes to the assessment of on-site fuel consumption and the resulting carbon dioxide emissions due to earthworks-related processes in residential building projects, prior to the start of the construction phase. Several studies have been carried out on this subject, and have demonstrated the considerable environmental impact of earthworks activities in terms of fuel consumption. However, no methods have been proposed to estimate on-site fuel consumption during the planning stage. This paper presents a quantitative method to predict fuel consumption before the construction phase. The calculations were based on information contained in construction project documents and the definition of equipment load factors. Load factors were characterized for the typical equipment that is used in earthworks in residential building projects (excavators, loaders and compactors), taking into considering the type of soil, the type of surface and the duration of use. We also analyzed transport fuel consumption, because of its high impact in terms of pollution. The proposed method was then applied to a case study that illustrated its practical use and benefits. The predictive method can be used as an assessment tool for residential construction projects, to measure the environmental impact in terms of on-site fuel consumption. Consequently, it provides a significant basis for future methods to compare construction projects.Peer ReviewedPostprint (author's final draft

Evaluación del rendimiento del control basado en redes neuronales para gestionar calderas mediante el modelo de edificio de orden reducido

There is a growing need to optimize the heating ventilation and air conditioning (HVAC) systems during building operations due to its high contribution to buildings' energy consumption and the willingness to meet the international energy and climate changes targets. Predictive and adaptive controls have arisen as proper tools to reduce the HVAC's energy consumption. They can predict future scenarios and determine the optimal strategy to manage HVAC systems. In this regard, control strategies based on neural networks (NN) to manage boilers and control the temperature setbacks are attracting significant attention. This study aims to use the reduced-order building descriptions as a benchmark model for building energy simulation to demonstrate an NN-based control's effectiveness in managing boilers in buildings. Reduced-order buildings will be simulated with different meteorological locations from various climate zones to determine if the proposed control system is more efficient than a schedule-based control or if certain zones have more potential to save energy. To carry out this analysis, a set of KPIs will be used to assess the performance of the proposed control and compare the results within the different scenarios and the baseline scenario, the scheduled-based control.Peer ReviewedObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats SosteniblesPostprint (published version

Facilitating the implementation of neural network-based predictive control to optimize building heating operation

Simple neural network (NN) architecture is a reliable tool to transform reactive rule-based systems into predictive systems. Thermal comfort is of utmost importance in office buildings, which need the activation of heating systems at an optimal time. A high-performance NN predictive system requires a large training dataset. This can limit system efficiency due to the lack of enough historical data derived from thermal controllers. To address this issue, we generated, trained and tested a dataset of eight sizes using a calibrated building model. A set of key performance indicators (KPIs) was improved by studying the output performance. The effect of normalization and standardization preprocessing techniques on NN prediction ability was studied. Learning curves showed that a minimum of 1–4 months of data are required to obtain enough accuracy. Two heating seasons provide the optimal data size to calibrate the NN properly with high prediction accuracy. The results also revealed that building data from =two years slightly improve NN performance. The most accurate results in KPIs 90%) were obtained with preprocessed data. The effect of preprocessing on large training patterns was less than that of training patterns <100. Finally, NN model performance was less accurate in cold climate zonesThe authors gratefully acknowledge the support by Catalan agency AGAUR through their research group support program (2017SGR00227)Peer ReviewedPostprint (published version

Post-handover housing defects: sources and origins

In Spain, the high levels of inexperienced workers and the long chains of subcontracting contribute to the poor quality of dwellings. Althoughthe Ley reguladora de la subcontratación en el Sector de la Construcción (subcontracting law) has established quality measures, the number of customer complaints is still increasing. In this paper, a total of 2,351 posthandover defects derived from four Spanish builders and seven residential developments are classi fi ed according to their source and origin. The research reveals that the most common defects identi fi ed by customers at posthandover were derived from bad workmanship and were related to construction errors and omissions. Typical defects were foundtoincludeincorrectinstallation,appearancedefects,andmissinganitemortaskmainlyrelatedto fi nishingandconsideredtobeminor.No defects were caused by poor design because they are mainly detected and resolved during construction or become apparent after some years of use. This study demonstrates the negative impact of redoing defective work during the fi nal stages of construction and provides knowledge to de fi ne measures to improve the quality of the fi nished buildings, such as understanding customer expectations and preferences, training programs for workers, specialization of subcontractors, and tightening external controls prior to handover.Postprint (author's final draft

The adoption of urban digital twins

The urban management industry has recently shown interest in implementing digital twins in cities to improve urban planning, optimize asset management and create secure, sustainable cities. Built on the knowledge gained with the development of smart cities and the implementation of digital twins in other industries, urban digital twins have experienced a significant expansion in just a few years. However, this rapid growth has led to a fragmented situation where the definition of the concept of urban digital twin is not clear and implementations share few similarities. For this reason, the main objective of this paper was to contribute to the conceptualization of the digital twin in urban management. To do so, existing initiatives were mapped in terms of applications, inputs, processing and outputs. Requirements were elicited and the basic structure of a city digital twin was defined. Benefits, open issues and key challenges were also identified. This paper will be useful for stakeholders within the urban management area as it establishes the basis for the future design, development and widespread adoption of urban digital twins.This work was supported by the Spanish Ministry of Science, Innovation and Universities via a doctoral grant to the first author (FPU19/ 04118), the Catalan authority AGAUR (2017SGR00227) and the University Service of the Terrassa City Council.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.3 - Per a 2030, duplicar la taxa mundial de millora de l’eficiència energèticaObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.b - Per a 2030, ampliar la infraestructura i millorar la tecnologia per tal d’oferir serveis d’energia moderns i sos­tenibles per a tots els països en desenvolupament, en particular els països menys avançats, els petits estats insulars en desenvolupament i els països en desenvolupament sense litoral, d’acord amb els programes de suport respectiusObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura::9.1 - Desenvolupar infraestructures fiables, sostenibles, resilients i de qualitat, incloent infraestructures regionals i transfrontereres, per tal de donar suport al desenvolupament econòmic i al benestar humà, amb especial atenció a l’accés assequible i equitatiu per a totes les personesObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura::9.4 - Per a 2030, modernitzar les infraestructures i reconvertir les indústries perquè siguin sostenibles, usant els recursos amb més eficàcia i promovent l’adopció de tecnologies i processos industrials nets i racionals ambiental­ment, i aconseguint que tots els països adoptin mesures d’acord amb les capacitats respectivesObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles::11.6 - Per a 2030, reduir l’impacte ambiental negatiu per capita de les ciutats, amb especial atenció a la qualitat de l’aire, així com a la gestió dels residus municipals i d’altre tipusObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats SosteniblesObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles::11.2 - Per a 2030, proporcionar accés a sistemes de transport segurs, assequibles, accessi­bles i sostenibles per a totes les persones, i millorar la seguretat viària, en particular mitjan­çant l’ampliació del transport públic, amb especial atenció a les necessitats de les persones en situació vulnerable, dones, nenes, nens, persones amb discapacitat i persones gransObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles::11.3 - Per a 2030, augmentar la urbanització inclusiva i sostenible, així com la capacitat de planificar i gestionar de manera participativa, integrada i sostenible els assentaments humans a tots els païsosObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles::11.4 - Redoblar els esforços per a protegir i salvaguardar el patrimoni cultural i natural del mónObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles::11.5 - Per a 2030, reduir de forma significativa el nombre de morts causades per desastres, inclosos els relacio­nats amb l’aigua, i de persones afectades per aquests, i reduir substancialment les pèrdues econòmiques directes causades per desastres relacionades amb el producte interior brut mundial, fent un èmfasi especial en la protecció de les persones pobres i de les persones en situacions de vulnerabilitatObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles::11.7 - Per a 2030, proporcionar accés universal a zones verdes i espais públics segurs, inclusius i accessibles, en particular per a les dones i els infants, les persones grans i les persones amb discapacitatObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles::11.a - Donar suport als vincles econòmics, socials i ambientals positius entre les zones urbanes, periurbanes i rurals enfortint la planificació del desenvolupament nacional i regionalObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles::11.b - Per a 2020, augmentar substancialment el nombre de ciutats i assentaments humans que adopten i posen en marxa polítiques i plans integrats per promoure la inclusió, l’ús eficient dels recursos, la mitigació del canvi climàtic i l’adaptació a aquest, així com la resiliència davant dels desastres, i desenvolupar i posar en pràctica una gestió integral dels riscos de desastre a tots els nivells, d’acord amb el Marc de Sendai per a la reducció del risc de desastres 2015.2030Postprint (published version

Review of criteria for determining HFM minimum test duration

The actual thermal behaviour of façades is important to identify suitable energy-saving measures and in- crease the energy performance of existing buildings. However, the accuracy of in situ measurements of façades’ U-values varies widely, mostly due to inadequate test durations. The aim of this paper was to evaluate the minimum duration of in situ experimental campaigns to measure the thermal transmittance of existing buildings’ façades using the heat flow meter method, and to analyse the thermal performance of the façade during the test. Minimum test duration was determined according to data quality criteria, variability of results criteria, and standardized criteria for different ranges of theoretical thermal trans- mittance and for the same range of average temperature difference. Then, the minimum test duration results were compared. The findings show that ISO criteria are more sensitive and provide more accurate results, requiring a longer test duration. However, when certification is not required, the duration of the test could be reduced by applying data quality and variability of results criteria. The minimum duration of experimental campaigns depends on the theoretical thermal transmittance and the stability of climatic conditions. Moreover, results are more accurate when the dynamic method is used.Peer ReviewedPostprint (author's final draft

A comparison of standardized calculation methods for in situ measurements of facades U-value

In recent years, a growing concern has been how to determine the actual thermal behaviour of façades in their operational stage, in order to establish appropriate energy-saving measures. This paper aims at comparing standardized methods for obtaining the actual thermal transmittance of existing buildings’ façades, specifically the average method and the dynamic method defined by ISO 9869-1:2014, to verify which best fits theoretical values. The paper also aims to promote the use of the dynamic method, and facilitate its implementation. Differences between the theoretical U-value and the measured U-value obtained using the average and dynamic methods were calculated in three case studies, and then compared. The results showed that differences between the theoretical and the measured U-value were lower when the dynamic method was used. Particularly, when testing conditions were not optimal, the use of the dynamic method significantly improved the fit with the theoretical value. Moreover, measurements of the U-value using the dynamic method with a sufficiently large dataset showed a better fit to the theoretical U-value than the results of other dynamic methods proposed by authors. Further research should consider the optimum size of the dataset to obtain a measured U-value that is correctly adjusted to the theoretical U-value.Peer ReviewedPreprin

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