Contributions to rework prevention in construction projects

Literature usually suggests that construction organization can reduce the costs derived from rework implementing quality management systems. Most common challenges and obstacles that construction organizations face during the implementation process and use of quality management systems are related to "how" the information can be recorded in an effective way, and "what" can be done with the recorded information. The aim of this dissertation is to focus on improving the defects recording process in the construction industry, and to propose methods and tools to use defects recorded on-site to prevent and reduce rework in the construction industry. The dissertation starts with the development of a conceptual model used to characterize defects. The current model is based on previously existing models and their adaptation to the context of the Spanish residential building sector. The model is based on the enumeration of the parameters that allow characterizing defects. The final model includes 6 parameters, with a list of standardized words and their definitions. The pre-established vocabulary lists are based on existing classification systems proposed by recognised organisations, authors and research reports, but then adapted to the Spanish context. However, in terms of defects, no standardised list exists. For this reason a taxonomy of defects is further developed for the Spanish construction sector. The aforementioned taxonomy consists of 15 main categories and 19 subcategories. The dissertation continues with the development of a methodology to track defects in the construction industry and its implementation in an IT tool called MoBuild. The obtained tracking system is based on images and tags. The strengths the abovementioned tracking system is to record information in a structured way and enable further statistical analysis of the recorded information. The new approach implemented in the MoBuild application allows practitioners to reduce the time of the recording process, facilitating the implementation of quality management systems, such as ISO 9000 in construction organizations. Furthermore, research proposes a quantitative methodology for dealing with potential adverse quality risks during the pre-construction stages of residential buildings and other similar types of constructions. The strength of this methodology lies in the fact that it helps designers to explicitly consider on-site quality during the design process. Designers can compare several design alternatives during the design phase, and determine the corresponding overall quality risk levels of a construction project without their creative talents being restricted. The methodology is especially worthwhile for those less-experienced designers who lack the required skills and knowledge to recognize quality risks in developing optimal designs. The methodology also serves as an assessment tool for construction companies. It can be used to measure the potential quality risks of construction projects and its subsequent construction activities. The suggested methodology also allows construction companies to optimize their on-site performance in the quality domain during the planning and preparation stages. Finally, this dissertation analyses the quality perceived by the end users during the post-handover stage. Different statistical methods are used to demonstrate the usefulness of the recorded data for the construction organizations. The aim is to highlight the essential role that records play in the operation of a quality company, in particular by providing essential evidence of the operation of quality systems. The aforementioned statistical analysis determines the type of defects detected; the elements affected by defects; the areas where defects are detected; which subcontractors produce defects; the source of the detected defects; the origin of the detected defects and; the influence of the building type and its characteristics in the number of defects detected

High-Capacity Cells and Batteries for Electric Vehicles

The automotive sector is rapidly accelerating its transformation towards electric mobility, and electric vehicle (EV) sales have been increasing year after year since the beginning of the decade. Due to their overall performance, lithium-ion batteries currently dominate the electric vehicle market. Each year, car manufacturers launch new models, increasing the average capacity of electric vehicle batteries. This is achieved, in part, through making bigger batteries, which lead to an increase in the vehicle cost, weight and use of more critical raw materials. Although prices are lowering, Li-ion batteries still do not have enough energy density to substantially decrease the weight of vehicles, and EVs are around 50% heavier than common internal combustion engine vehicles (ICEV); thus, using high-energy cells is an intriguing possibility that we are being called on to explore. This Special Issue aims to evaluate several issues concerning high-capacity batteriesPeer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (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

U-value time series analyses: Evaluating the feasibility of in-situ short-lasting IRT tests for heavy multi-leaf walls

A gap in standardization of quantitative infrared thermography (IRT) directly leads to a lack of measurement pattern for determining in-situ U-values of heavy multi-leaf walls. Three groups of causal factors might influence the estimation of this build quality indicator: operating conditions, thermophysical properties and technical conditions. Focusing on the last one, previous studies underlined the difficulties of measuring below 3¿h. In contrast to active IRT, no algorithms have been found to process images, despite playing an important role in the effectiveness and robustness of IRT. The traditional approach involves analysing from 120 to 7200 thermograms with a data acquisition interval of 1¿min up to 1¿s respectively. The aim of this paper was to critically assess the test duration that is traditionally used. Six real heavy multi-leaf walls were tested under a stationary regime as a stochastic process of underlying data. For the first time, a research based on two U-value time series analyses (statistical tests and a signal modelling technique by MATLAB) demonstrated the feasibility of short-lasting IRT tests. Moreover, this research posed an innovative data management tool to automate this non-destructive testing (NDT) in mid-term, stopping IRT tests in real time once the right level of accuracy was achieved.Postprint (author's final draft

Implementation of predictive control in a commercial building energy management system using neural networks

Most existing commercial building energy management systems (BEMS) are reactive rule-based. This means that an action is produced when an event occurs. In consequence, these systems cannot predict future scenarios and anticipate events to optimize building operation. This paper presents the procedure of implementing a predictive control strategy in a commercial BEMS for boilers in buildings, and describes the results achieved. The proposed control is based on a neural network that turns on the boiler each day at the optimum time, according to the surrounding environment, to achieve thermal comfort levels at the beginning of the working day. The control strategy presented in this paper is compared with the current control strategy implemented in BEMS that is based on scheduled on/off control. The control strategy was tested during one heating season and a set of key performance indicators were used to assess the benefits of the proposed control strategy. The results showed that the implementation of predictive control in a BEMS for building boilers can reduce the energy required to heat the building by around 20% without compromising the user’s comfort.Peer ReviewedPostprint (author's final draft

Life Cycle Analysis of a Game-Based Solution for Domestic Energy Saving

ICT-based solutions are seen to be almost totally environmentally friendly, but game-based solutions for energy saving have not been explored yet. This paper describes a comprehensive analysis and an in-depth interpretation of the life cycle environmental impact of a game-based solution for domestic energy saving, developed and validated within the EU-funded Horizon 2020 project EnerGAware—Energy Game for Awareness of energy efficiency in social housing communities. Life cycle impacts were calculated with SimaPRO 8.5.2.0 using the ReCiPe 2016 v1.02 midpoint and endpoint methods and information contained within the Ecoinvent v3.4 database. Although the pre-competitive solution, directly arising from the research project, was found to have a relatively high environmental impact, its future exploitation, which mostly relies on existing infrastructure, was found to be highly competitive from an environmental perspective. The game will help reduce the life cycle impact of domestic energy consumption on damage to human health (3.68%), ecosystem quality (3.87%), and resource availability (4.81%). Most of the environmental impact of the market solution was found in the manufacturing phase (77.96–80.12%). Transport (8.86–7.57%), use (3.86–5.82%), and maintenance (7.24–7.54%) phases were found to contribute little to environmental impact. This research provides a useful reference for decision-making as it contributes to the environmental benchmarking of competing energy-saving strategies.This research was funded by the European Union’s Horizon 2020 research and innovation programme,grant number 649673.Peer ReviewedPostprint (published version

Experiencias con herramientas de gamificación en la docencia de proyectos en la enseñanza universitaria

Este artículo presenta una serie de experiencias con la herramienta de gamificación kahoot en sesiones de teoría de asignaturas del área de proyectos de ingeniería y analiza los resultados de su implementación. La primera experiencia presentada es el uso de la herramienta Kahoot para evaluar los ejercicios intermedios de una sesión de teoría. La segunda experiencia presentada es el uso de la herramienta para evaluar los conceptos de teoría explicados durante una clase de teoría. La última experiencia es el uso de la herramienta para repasar el temario hecho hasta el momento justo al inicio de una sesión de teoría. Los resultados demuestran la buena acogida que tienen estos tipos de actividades entre el alumnado. Además, la herramienta permite dinamizar las sesiones de teoría y conseguir una mayor atención de los estudiantes. De todos modos, el uso de este tipo de herramientas tiene sus riesgos. El hecho que Kahoot sea una herramienta competitiva provoca que los estudiantes se desconcentren y sea difícil que estos presten atención a las explicaciones del profesor. A nivel tecnológico no se reportaron problemas significativos ya que actualmente todos los estudiantes van a clase con un móvil, tableta u ordenador compatible con el sistema usado.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

Estimation of a room ventilation air change rate using a stochastic grey-box modelling approach

The measurement of ventilation air change rate is a difficult, expensive task in buildings. Usually, the tracer-gas mass balance equation is used to determine ventilation air change rates. This method uses an ordinary differential equation. Consequently, it cannot deal with disturbances that enter the system, such as the influence of unrecognized and unmodelled inputs or the measurement noise. The use of the stochastic grey-box modelling approach, which is less common in the ventilation field, can help to deal with disturbances that can affect the system. The objective of this paper is to assess the potential of using the stochastic grey-box modelling approach to estimate the ventilation air change rate. The modelling is based on the stochastic differential equation of tracer-gas mass balance. The results show that this approach produces robust estimations to determine the ventilation air change rate of a room.Peer ReviewedPostprint (author's final draft

Reduced-order modeling for energy performance contracting

Buildings subject to Energy Performance Contracts (EPCs) are usually quite complex public buildings, sometimes relatively old and usually barely documented from the technical standpoint. Gathering comprehensive and reliable technical information is a time consuming and expensive process that has to be carried out within the submission deadline. In these conditions, the standard approach to energy performance forecasting which uses detailed simulation is practically unfeasible. This paper proposes a reduced-order modeling approach that is tailored to the EPC tendering phase. The proposed methodology extends a third order building model, introducing explicit, albeit still abstract, representations of the heating/cooling system, of the weather influence and of the end-user gains. The extended parameter set reflects to a large degree the information that is readily available in practical on-site surveying, or that can be easily calculated from that information. As a consequence of the simplified physics, a knowledge driven, practical calibration procedure, which provides an effective way of reducing uncertainty, is proposed. The calibration procedure analyses the uncertainty present in the available knowledge and uses the constraints imposed by the implemented physics on the parameters’ dynamic to assess their value estimation. The modeling approach is exemplified through three case studies: the first one provides the comparison of the reduced-order model predictions with the outcomes of a detailed model of a small hospital, the second one is used to compare the reduced-order model predictions with the detailed measurements of energy consumption of a real building, and the third case study exemplifies the use in operational context with scarce information.Peer ReviewedPostprint (author's final draft