Il risparmio delle risorse alla scala abitativa e di insediamento. Tecnologie non convenzionali e valutazione ambientale attraverso metodologia LCA.

Nel corso del mio lavoro di ricerca mi sono occupata di identificare strategie che permettano il risparmio delle risorse a livello edilizio e di approfondire un metodo per la valutazione ambientale di tali strategie. La convinzione di fondo è che bisogna uscire da una visione antropocentrica in cui tutto ciò che ci circonda è merce e materiale a disposizione dell’uomo, per entrare in una nuova era di equilibrio tra le risorse della terra e le attività che l’uomo esercita sul pianeta. Ho quindi affrontato il tema dell’edilizia responsabile approfondendo l’ambito delle costruzioni in balle di paglia e terra. Sono convinta che l’edilizia industriale abbia un futuro molto breve davanti a sé e lascerà inevitabilmente spazio a tecniche non convenzionali che coinvolgono materiali di semplice reperimento e posa in opera. Sono altresì convinta che il solo utilizzo di materiali naturali non sia garanzia di danni ridotti sull’ecosistema. Allo stesso tempo ritengo che una mera certificazione energetica non sia sinonimo di sostenibilità. Per questo motivo ho valutato le tecnologie non convenzionali con approccio LCA (Life Cycle Assessment), approfondendo gli impatti legati alla produzione, ai trasporti degli stessi, alla tipologia di messa in opera, e ai loro possibili scenari di fine vita. Inoltre ho approfondito il metodo di calcolo dei danni IMPACT, identificando una carenza nel sistema, che non prevede una categoria di danno legata alle modifiche delle condizioni idrogeologiche del terreno. La ricerca si è svolta attraverso attività pratiche e sperimentali in cantieri di edilizia non convenzionale e attività di ricerca e studio sull’LCA presso l’Enea di Bologna (Ing. Paolo Neri).

Applicazione della metodologia LCA ai cereali: Il caso della cooperativa agricola Capa Cologna

L’agricoltura e la trasformazione dei prodotti agro-alimentari hanno un forte impatto sull’ambiente. Su questo aspetto converge l’attenzione sia delle politiche nazionali ed internazionali sia del singolo consumatore. E’ quindi sempre più necessario valutare questo impatto lungo tutta la filiera dei prodotti agro-alimentari per capire dove e come intervenire per aumentarne le prestazioni ambientali. La presente tesi, svolta in collaborazione con la società di ingegneria ambientale E&NGI srl, si propone quindi di analizzare, attraverso la metodologia di Life Cycle Assessment, gli impatti del ciclo di vita di grano e mais, prodotti, trasportati e trattati dalla cooperativa agricola Capa Cologna, in provincia di Ferrara. I cereali sono stati seguiti dalla produzione fino ai cancelli dell’azienda e i dati relativi a tutti i flussi uscenti ed entranti dal processo produttivo sono stati raccolti in campo o ottenuti dall’applicazione di modelli previsionali o, quando necessario, ricavati da banche dati esistenti. Questi flussi hanno costituito un inventario implementato nel software Gabi 6. Successivamente i flussi sono stati convertiti in impatti potenziali utilizzando due metodi (CML 2001 e USEtox) e selezionando sette categorie d’impatto potenziale: esaurimento delle risorse abiotiche, acidificazione, eutrofizzazione, effetto serra, assottigliamento della fascia di ozono stratosferico, smog fotochimico ed ecotossicità acquatica. Dall’analisi è emerso che per entrambi i cereali la fase del ciclo di vita maggiormente impattante è quella di coltivazione. Ciò è dovuto, soprattutto, alla produzione dei fertilizzanti chimici, dei fitofarmaci e alle loro emissioni in ambiente. Sui metodi per la stima di queste emissioni è stata svolta un’analisi di sensitività. Infine, non essendo ipotizzabile intervenire nella fase agricola, in quanto la cooperativa deve seguire un rigido disciplinare, si sono proposte azioni di miglioramento sull’impianto di Capa Cologna. In particolare, si è proposto uno scenario alternativo in cui l’impianto è alimentato ad energia solare fotovoltaica.

Applicazione delle metodologie LCA ed LCC per la valutazione ambientale ed economica della produzione del pellet realizzato tramite scarti agricoli di potature degli uliveti

Nel seguente lavoro è stata sviluppata una analisi ambientale ed economica del ciclo di vita del pellet, realizzato con scarti agricoli dalle potature degli uliveti. L’obiettivo di tale lavoro è dimostrare se effettivamente l’utilizzo del pellet apporti vantaggi sia dal punto di vista ambientale sia da quello economico. In tale progetto si sviluppano quindi un LCA, Life cycle analysis, e un LCC, Life Cycle Cost, secondo gli steps standard suggeriti da tali metodologie. Per effettuare l’analisi del ciclo di vita è stato utilizzato il software Simapro che ha permesso di valutare gli impatti ambientali sulle varie categorie di impatto incluse. In particolare sono stati considerati due metodologie, una midpoint ed una endpoint, ossia l’Ecoindicator 99 e il CML2 baseline 2000. Per le valutazioni finali è stata poi utilizzata la normativa spagnola sugli impatti ambientali, BOE 21/2013 del 9 dicembre, che ci ha permesso di caraterizzare le varie categorie d’impatto facendo emergere quelle più impattate e quelle meno impattate. I risultati finali hanno mostrato che la maggior parte degli impatti sono di tipo compatibile e moderato; pochi, invece, sono gli impatti severi e compatibili, che si riscontrano soprattutto nella categoria d’impatto “Fossil Fuels”. Per quanto riguarda invece l’analisi economica, si è proceduto effettuando una valutazione iniziale fatta su tutto il processo produttivo considerato, poi una valutazione dal punto di vista del produttore attraverso una valutazione dell’investimento ed infine, una valutazione dal punto di vista del cliente finale. Da queste valutazioni è emerso che ciò risulta conveniente dal punto di vista economico non solo per il produttore ma anche per l’utente finale. Per il primo perché dopo i primi due anni di esercizio recupera l’investimento iniziale iniziando ad avere un guadagno; e per il secondo, poiché il prezzo del pellet è inferiore a quello del metano. Quindi, in conclusione, salvo cambiamenti in ambito normativo ed economico, l’utilizzo del pellet realizzato da scarti di potature di uliveti risulta essere una buona soluzione per realizzare energia termica sia dal punto di vista ambientale, essendo il pellet una biomassa il cui ciclo produttivo non impatta severamente sull’ambiente; sia dal punto di vista economico permettendo al produttore introiti nell’arco del breve tempo e favorendo al cliente finale un risparmio di denaro sulla bolletta.

Non-invasive quantitative estimation of bone density in rats throughout the life cycle and in arthritic osteopenia: preliminary results

A longitudinal bone survey was conducted in 86 female Wistar rats in order to assess mineral density kinetics from young age (5 weeks: 115 g) till late adulthood (64 weeks: 586 g). In vivo quantitative radiographic scanning was performed on the caudal vertebrae, taking trabecular mass as the parameter. Measurements were expressed as Relative Optical Density (ROD) units by means of a high resolution densitometric device. Results showed a progressive increase in mineral density throughout the life cycle, with a tendency to level in the higher weight range, indicating that progressive mineral aposition occurs in rats in dependency of age. This phenomenon, however, should be always considered within the context of continuous skeletal growth and related changes typical of this species. Twelve different animals were also examined following induction of articular inflammation with Freund's adjuvant in six of them. Bone survey conducted 12 to 18 days after inoculation revealed a significant (P less than 0.01) reduction in trabecular bone mass of scanned vertebrae in comparison with the weight-matched untreated controls. It is concluded that the in vivo quantitative assessment of bone density illustrated in this report represents a sensitive and useful tool for the long-term survey of naturally occurring or experimentally induced bone changes. Scanning of the same part of the skeleton can be repeated, thereby avoiding sacrifice of the animal and time-consuming preparation of post-mortem material.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Improving the management of building life cycle : a data mining approach

The construction industry has adapted information technology in its processes in terms of computer aided design and drafting, construction documentation and maintenance. The data generated within the construction industry has become increasingly overwhelming. Data mining is a sophisticated data search capability that uses classification algorithms to discover patterns and correlations within a large volume of data. This paper presents the selection and application of data mining techniques on maintenance data of buildings. The results of applying such techniques and potential benefits of utilising their results to identify useful patterns of knowledge and correlations to support decision making of improving the management of building life cycle are presented and discussed.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Using data mining to improve building life cycle

The building life cycle process is complex and prone to fragmentation as it moves through its various stages. The number of participants, and the diversity, specialisation and isolation both in space and time of their activities, have dramatically increased over time. The data generated within the construction industry has become increasingly overwhelming. Most currently available computer tools for the building industry have offered productivity improvement in the transmission of graphical drawings and textual specifications, without addressing more fundamental changes in building life cycle management. Facility managers and building owners are primarily concerned with highlighting areas of existing or potential maintenance problems in order to be able to improve the building performance, satisfying occupants and minimising turnover especially the operational cost of maintenance. In doing so, they collect large amounts of data that is stored in the building’s maintenance database. The work described in this paper is targeted at adding value to the design and maintenance of buildings by turning maintenance data into information and knowledge. Data mining technology presents an opportunity to increase significantly the rate at which the volumes of data generated through the maintenance process can be turned into useful information. This can be done using classification algorithms to discover patterns and correlations within a large volume of data. This paper presents how and what data mining techniques can be applied on maintenance data of buildings to identify the impediments to better performance of building assets. It demonstrates what sorts of knowledge can be found in maintenance records. The benefits to the construction industry lie in turning passive data in databases into knowledge that can improve the efficiency of the maintenance process and of future designs that incorporate that maintenance knowledge.

International review of environmental assessment tools and databases

Existing widely known environmental assessment models, primarily those for Life Cycle Assessment of manufactured products and buildings, were reviewed to grasp their characteristics, since the past several years have seen a significant increase in interest and research activity in the development of building environmental assessment methods. Each method or tool was assessed under the headings of description, data requirement, end-use, assessment criteria (scale of assessment and scoring/ weighting system)and present status

Whole of life cycle cost analysis in bridge rehabilitation

The report presents a methodology for whole of life cycle cost analysis of alternative treatment options for bridge structures, which require rehabilitation. The methodology has been developed after a review of current methods and establishing that a life cycle analysis based on a probabilistic risk approach has many advantages including the essential ability to consider variability of input parameters. The input parameters for the analysis are identified as initial cost, maintenance, monitoring and repair cost, user cost and failure cost. The methodology utilizes the advanced simulation technique of Monte Carlo simulation to combine a number of probability distributions to establish the distribution of whole of life cycle cost. In performing the simulation, the need for a powerful software package, which would work with spreadsheet program, has been identified. After exploring several products on the market, @RISK software has been selected for the simulation. In conclusion, the report presents a typical decision making scenario considering two alternative treatment options.

Strategies for minimising the whole of life cycle cost of reinforced concrete bridge exposed to aggressive environments

n design of bridge structures, it is common to adopt a 100 year design life. However, analysis of a number of case study bridges in Australia has indicated that the actual design life can be significantly reduced due to premature deterioration resulting from exposure to aggressive environments. A closer analysis of the cost of rehabilitation of these structures has raised some interesting questions. What would be the real service life of a bridge exposed to certain aggressive environments? What is the strategy of conducting bridge rehabilitation? And what are the life cycle costs associated with rehabilitation? A research project funded by the CRC for Construction Innovation in Australia is aimed at addressing these issues. This paper presents a concept map for assisting decision makers to appropriately choose the best treatment for bridge rehabilitation affected by premature deterioration through exposure to aggressive environments in Australia. The decision analysis is referred to a whole of life cycle cost analysis by considering appropriate elements of bridge rehabilitation costs. In addition, the results of bridges inspections in Queensland are presented