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GEP has been employed in this work to model the compressive strength of different types of geopolymers through six different schemes. The differences between the models were in their linking functions, number of genes, chromosomes and head sizes. The curing time, Ca(OH)2 content, the amount of superplasticizer, NaOH concentration, mold type, aluminosilicate source and H2O/Na2O molar ratio were the seven input parameters considered in the construction of the models to evaluate the compressive strength of geopolymers. A total number of 399 input-target pairs were collected from the literature, randomly divided into 299 and 100 sets and were trained and tested, respectively. The best performance model had 6 genes, 14 head size, 40 chromosomes and multiplication as linking function. This was shown by the absolute fraction of variance, the absolute percentage error and the root mean square error. These were of 0.9556, 2.4601 and 3.4716 for training phase, respectively and 0.9483, 2.8456 and 3.7959 for testing phase, respectively. However, another model with 7 genes, 12 head size, 30 chromosomes and addition as linking function showed suitable results with the absolute fraction of variance, the absolute percentage error and the root mean square of 0.9547, 2.5665 and 3.4360 for training phase, respectively and 0.9466, 2.8020 and 3.8047 for testing phase, respectively. These models showed that gene expression programming has a strong potential for predicting the compressive strength of different types of geopolymers in the considered range.

In the present study, six different models based on artificial neural networks have been developed to predict the compressive strength of different types of geopolymers. The differences between the models were in the number of neurons in hidden layers and in the method of finalizing the models. Seven independent input parameters that cover the curing time, Ca(OH)2 content, the amount of superplasticizer, NaOH concentration, mold type, geopolymer type and H2O/Na2O molar ratio were considered. For each set of these input variables, the compressive strength of geopolymers was obtained. A total number of 399 input-target pairs were collected from the literature, randomly divided into 279, 60 and 60 data and were trained, validated and tested, respectively. The best performance model was obtained through a network with two hidden layers and absolute fraction of variance of 0.9916, the absolute percentage error of 2.2102 and the root mean square error of 1.4867 in training phase. Additionally, the entire trained, validated and tested network showed a strong potential for predicting the compressive strength of geopolymers with a reasonable performance in the considered range.

Carbonation is a major cause of concrete structures deterioration leading to expensive maintenance and conservation operations. The eco-efficient construction agenda favours the increase of the use of supplementary cementing materials (SCMs) to reduce Portland cement’s consumption and also the use of recycled aggregates concrete (RAC) in order to reduce the consumption of primary aggregates and to avoid landfill disposal of concrete waste. There is a wide range of literature published on the field of concrete carbonation related to the use of SCMs and/or RCA. However, the different conditions used by different authors limit comparison and in some cases contradictory findings are noticed. Besides, since most investigations are based on the use of the phenolphthalein indicator, which provides a poor estimate of the real concrete carbonation depth, there is a high probability that past researches could have underestimate the corrosion potential associated to concrete carbonation. This paper reviews current knowledge on concrete carbonation addressing carbonation depth’s measurement, the use of SCMs and or RAC.

This chapter starts with an overview of the unsustainable energy consumption which is due to fast population growth and related greenhouse gas emissions. The case of energy efficiency building is introduced. A short analysis of the ambitious European nearly zero-energy building (NZEB) target is presented. Shortcomings of current materials concerning energy building efficiency are reviewed. Examples of promising nano- and biotech-based materials for energy building efficiency are briefly covered. A book outline is presented., (undefined)

Published online: 16 Oct 2013, Looking back to less than three centuries of industrialization, responsible for alarming levels of pollution and consumption of non-renewable resources that has led to the exhaustion of the earth’s capacity, the humankind only now begins to grasp the overwhelming potential of natural systems. During almost 40 million centuries, Nature has developed materials and processes with optimal performance which are totally biodegradable. Analysis of bioinspired materials requires the knowledge of both biological and engineering principles which are being a part of a large research area termed biotechnology. This hot area is one of the six strategic Key Enabling Technologies that will be funded under the EU Framework Programme Horizon 2020. This paper reviews the current knowledge on the potential of this emerging field, particularly in the development of materials and technologies for the construction industry. It covers the use of bacteria for enhancing concrete durability and for soil stabilization. It also covers bioinspired tough composite materials, bioinspired adhesives and coatings, and self-cleaning materials. Incorporation of biology basics in the civil engineering curriculum would ease the communication between biologists and civil engineers, helping to foster research on biotechnologies and bioinspired materials for the construction industry.

Neuro-fuzzy approach has been successfully applied to a wide range of civil engineering problems so far. However, this is limited for geopolymeric specimens. In the present study, compressive strength of different types of geopolymers has been modeled by adaptive neuro-fuzzy interfacial systems (ANFIS). The model was constructed by 395 experimental data collected from the literature and divided into 80% and 20% for training and testing phases, respectively. Curing time, Ca(OH) 2 content, NaOH concentration, mold type, aluminosilicate source and H 2 O/Na 2 O molar ratio were independent input parameters in the proposed model. Absolute fraction of variance, absolute percentage error and root mean square error of 0.94, 11.52 and 14.48, respectively in training phase and 0.92, 15.89 and 23.69, respectively in testing phase of the model were achieved showing the relatively high accuracy of the proposed ANFIS model. By the obtained results, a comparative study was performed to show the interaction of some selected factors on the compressive strength of the considered geopolymers. The discussions findings were in accordance to the experimental studies and those results presented in the literature.

The volume of polymeric wastes such as tyre rubber and polyethylene terephthalate (PET) bottles is increasing at a fast rate. An estimated 1000 million tyres reach the end of their useful lives every year and 5000 million more are expected to be discarded in a regular basis by the year 2030. Only a small part is currently recycled and millions of tyres are just stockpiled, landfilled or buried. As for PET bottles annual consumption is over 300 000 million units. The majority is just landfilled. This chapter reviews research published on the performance of concrete containing tyre rubber and PET wastes. Furthermore it discusses the effect of waste treatments, the size of waste particles and the waste replacement volume on the fresh and hardened properties of concrete.