Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks

Prediction the effects of ZnO2 nanoparticles on splitting tensile strength and water absorption of high strength concrete

“…However, as mentioned by the other authors ANNs [2,3] and GEP [6,7] are suitable soft computing tools for prediction the properties of concrete specimens. The results obtained from this work also indicate these findings.…”

“…NNs are a family of massively parallel architectures that solve difficult problems via the cooperation of highly interconnected but simple computing elements (or artificial neurons). Basically, the processing elements of a neural network are analogous to the neurons in the brain, which consist of many simple computational elements arranged in several layers 3 . The concrete properties could be calculated using the models built with NNs.…”

Estimation of concrete compressive strength using artificial neural network

“…Despite the fact that previous conventional models give reasonable prediction accuracy for engineering purposes in reference to concrete compressive strength, development of new concrete mixtures, with different types and percentage of additives, increase the number of concrete constituents, thus, making harder to obtain reliable results among various concrete components. Therefore, in recent years, artificial neural networks (ANN) have been used for the purpose of modelling different properties of concrete, such as drying shrinkage [5], concrete durability [6], compressive strength of normal concrete and high performance concrete [7][8][9][10][11][12], workability of concrete with metakaolin and fly ash [13][14], mechanical behaviour of concrete at high temperatures [15] and long term effect of fly ash and silica fume on compressive strength [16]. The main advantage of ANN approach over the standard conventional predictors [1][2][3][4] lies in the possibility to examine the compressive strength of large number of concrete specimens with different w/c ratio, including the effect of exposure to various freeze/thaw cycles.…”

“…Uprkos injenici da ovi konvencionalni modeli daju ocenu pritisne vrsto e betona sa zadovoljavaju om ta noš u za potrebe inženjerske prakse, razvoj novih smeša betona, s razli itim tipovima i koli inom aditiva, pove ava broj sastavnih elemenata betona, što otežava uspostavljanje jasnih veza izme u razli itih komponenata. Iz tog razloga, tokom poslednjih godina, sve eš a je primena vešta kih neuronskih mreža za potrebe modelovanja razli itih svojstava betona, poput skupljanja pri isušivanju [5], trajnosti betona [6], vrsto e normalnog betona i betona visoke vrsto e pri pritisku [7][8][9][10][11][12], konsistencije betona s metakaolinom i lete im pepelom [13][14], mehani kog ponašanja betona na visokim temperaturama [15], kao i dugotrajnog efekta lete eg pepela i silikatne prašine na vrsto u betona pri pritisku [16]. Glavna prednost primene vešta kih neuronskih mreža u odnosu na standardne konvencionalne prediktore [1][2][3][4], leži u mogu nosti analize vrsto e velikog broja uzoraka betona s razli itim vodocementnim faktorom, uklju uju i i efekat izlaganja dejstvu mraza.…”

Modeling the Fresh and Hardened Stage Properties of Self-Compacting Concrete using Random Kitchen Sink Algorithm

“…The MAPE of the model were 3.13 and 8.42% for high and normal strength concrete in training while it was 3.75 and 9.69% for test data which is better compared to the other models. Other works in the area of modeling of hardened properties of mortar and concrete like compressive strength, flexural strength and torsional strength using different soft computing methods are also reported (Yeh 1998;Lee 2003;Tang 2006;Hossain et al 2006;Tang et al 2007;Pala et al 2007;Topcu and Saridemir 2008;Altun et al 2008;Prasad et al 2009;Khatibinia et al 2016). Mohebbi et al (2011) proposed an ANN model on the effect chemical and mineral admixtures on the flow properties of self consolidating cement paste based on 200 training data.…”