Cost-effective energy and carbon emission optimization in building renovation – A case-study in a low income neighbourhood

Construction sector is one of the major responsible for energy consumption and carbon emissions and renovation of existing buildings plays an important role in the actions to mitigate climate changes. Present work is based on the methodology developed in IEA Annex 56, allowing identifying cost optimal and cost effective renovation scenarios improving the energy performance. The analysed case study is a residential neighbourhood of the municipality of Gaia in Portugal. The analysis compares a reference renovation scenario (without improving the energy performance of the building) with a series of alternative renovation scenarios, including the one that is being implemented.

IEA EBC Annex56 vision for cost effective energy and carbon emissions optimization in building renovation

Building sector has become an important target for carbon emissions reduction, energy consumption and resources depletion. Due to low rates of replacement of the existing buildings, their low energy performances are a major concern. Most of the current regulations are focused on new buildings and do not account with the several technical, functional and economic constraints that have to be faced in the renovation of existing buildings. Thus, a new methodology is proposed to be used in the decision making process for energy related building renovation, allowing finding a cost-effective balance between energy consumption, carbon emissions and overall added value.

Project scheduling and cost minimization with multiple availability constrained resources under stochastic conditions

The authors propose a mathematical model to minimize the project total cost where there are multiple resources constrained by maximum availability. They assume the resources as renewable and the activities can use any subset of resources requiring any quantity from a limited real interval. The stochastic nature is inferred by means of a stochastic work content defined per resource within an activity and following a known distribution and the total cost is the sum of the resource allocation cost with the tardiness cost or earliness bonus in case the project finishes after or before the due date, respectively. The model was computationally implemented relying upon an interchange of two global optimization metaheuristics – the electromagnetism-like mechanism and the evolutionary strategies. Two experiments were conducted testing the implementation to projects with single and multiple resources, and with or without maximum availability constraints. The set of collected results shows good behavior in general and provide a tool to further assist project manager decision making in the planning phase.

Cost-efficient one-part alkali-activated mortars with low global warming potential for floor heating systems applications

Increasing building energy efficiency is one the most cost-effective ways to reduce emissions. The use of thermal insulation materials mitigates heat loss in buildings, therefore minimising heat energy needs. In recent years, several papers were published on the subject of foam alkali-activated cements with enhanced thermal conductivity. However, on those papers cost analysis was strangely avoided. This paper presents experimental results on one-part alkali-activated cements. It also includes global warming potential assessment and cost analysis. Foam one-part alkali-activated cements cost simulations considering two carbon dioxide social costs scenarios are also included. The results show that one-part alkali-activated cements mixtures based on 26%OPC + 58.3%FA + 8%CS + 7.7%CH and 3.5% hydrogen peroxide constitute a promising cost-efficient (67 euro/m3), thermal insulation solution for floor heating systems. This mixture presents a low global warming potential of 443 KgCO2eq/m3. The results confirm that in both carbon dioxide social cost scenarios the mixture 26 OPC + 58.3 FA + 8 CS + 7.7 CH with 3.5% hydrogen peroxide foaming agent is still the most cost efficient.

Poker learner: modelação de jogadores através de data mining

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Cheese whey: a cost-effective alternative for hyaluronic acid production by Streptococcus zooepidemicus

This study focuses on the optimization of cheese whey formulated media for the production of hyaluronic acid HA by Streptococcus zooepidemicus. Culture media containing whey (W; 2.1 g/L) or whey hydrolysate (WH; 2.4 g/L) gave the highest HA productions. Both W and WH produced high yields on protein consumed, suggesting cheese whey is a good nitrogen source for S. zooepidemicus production of HA. Polysaccharide concentrations of 4.0 g/L and 3.2 g/L were produced in W and WH in a further scale-up to 5 L bioreactors, confirming the suitability of the low-cost nitrogen source. Cheese whey culture media provided high molecular weight (> 3000 kDa) HA products. This study revealed replacing the commercial peptone by the low-cost alternative could reduce HA production costs by up to a 70%compared to synthetic media.

Modelling tyre-road noise with data mining techniques

The research aimed to establish tyre-road noise models by using a Data Mining approach that allowed to build a predictive model and assess the importance of the tested input variables. The data modelling took into account three learning algorithms and three metrics to define the best predictive model. The variables tested included basic properties of pavement surfaces, macrotexture, megatexture, and uneven- ness and, for the first time, damping. Also, the importance of those variables was measured by using a sensitivity analysis procedure. Two types of models were set: one with basic variables and another with complex variables, such as megatexture and damping, all as a function of vehicles speed. More detailed models were additionally set by the speed level. As a result, several models with very good tyre-road noise predictive capacity were achieved. The most relevant variables were Speed, Temperature, Aggregate size, Mean Profile Depth, and Damping, which had the highest importance, even though influenced by speed. Megatexture and IRI had the lowest importance. The applicability of the models developed in this work is relevant for trucks tyre-noise prediction, represented by the AVON V4 test tyre, at the early stage of road pavements use. Therefore, the obtained models are highly useful for the design of pavements and for noise prediction by road authorities and contractors.

COST Action TU1406: eBook of the 1st Workshop Meeting

COST Action TU1406 aims to address the European economic and societal needs by standardizing the condition assessment and maintenance level of roadway bridges. Currently, bridge quality control plans vary from country to country and, in some cases, within the same country. This therefore urges the establishment of a European guideline to surpass the lack of a standard methodology to assess bridge condition and to define quality control plans for roadway bridges. Such a guideline will comprise specific recommendations for assessing performance indicators as well as for the definition of performance goals, bringing together different stakeholders (e.g. universities, institutes, operators, consultants and owners) from various scientific disciplines (e.g. on-site testing, visual inspection, structural engineering, sustainability, etc.) in order to establish a common transnational language. COST Action TU1406 Workshops aim to facilitate the exchange of ideas and experiences between active researchers and practitioners as well as to stimulate discussions on new and emerging issues in line with the conference topics. This first Workshop essentially focuses on WG1 issues, namely, intends to address performance indicators, how these are assessed (e.g. visual inspection, nondestructive tests and monitoring systems), with what frequency and what values are generally obtained. The main outcomes, given in this eBook, were really important, not only for WG1 developments, but also for all the other WGs and for the Action itself.

The Data Mining applied for the prediction of highway roughness due to overloaded trucks

Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and verified using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks

Silicon nanowire arrays coupled with cobalt phosphide spheres as low-cost photocathodes for efficient solar hydrogen evolution

We demonstrate the first example of silicon nanowire array photocathodes coupled with hollow spheres of the emerging earth-abundant cobalt phosphide catalysts. Compared to bare silicon nanowire arrays, the hybrid electrodes exhibit significantly improved photoelectrochemical performance toward the solar-driven H2 evolution reaction.

Cost efficiency and resistance to chemical attack of a fly ash geopolymeric mortar versus epoxy resin and acrylic paint coatings

This article presents results of an experimental investigation on the resistance to chemical attack (with sulphuric, hydrochloric and nitric acid) of several materials: OPC concrete, high-performance concrete, epoxy resin, acrylic painting and a fly ash-based geopolymeric mortar). Three types of acids with three high concentrations (10, 20 and 30%) were used to simulate long-term degradation. A cost analysis was also performed. The results show that the epoxy resin has the best resistance to chemical attack independently of the acid type and the acid concentration. However, the cost analysis shows that the epoxy resin-based solution is the least cost-efficient solution being 70% above the cost efficiency of the fly ash-based geopolymeric mortar.

Text mining na análise de sentimentos em contextos de big data

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Pervasive data mining engine

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Development of an intelligent earthwork optimization system

Tese de Doutoramento em Engenharia Civil.

Estimativa dos parâmetros de compactação de solos usando técnicas de data mining

Dissertação de mestrado integrado em Engenharia Civil