An investigation into the use of manufactured sand as a 100% replacement for fine aggregate in concrete

Manufactured sand differs from natural sea and river dredged sand in its physical and mineralogical properties. These can be both beneficial and detrimental to the fresh and hardened properties of concrete. This paper presents the results of a laboratory study in which manufactured sand produced in an industry sized crushing plant was characterised with respect to its physical and mineralogical properties. The influence of these characteristics on concrete workability and strength, when manufactured sand completely replaced natural sand in concrete, was investigated and modelled using artificial neural networks (ANN). The results show that the manufactured sand concrete made in this study generally requires a higher water/cement (w/c) ratio for workability equal to that of natural sand concrete due to the higher angularity of the manufactured sand particles. Water reducing admixtures can be used to compensate for this if the manufactured sand does not contain clay particles. At the same w/c ratio, the compressive and flexural strength of manufactured sand concrete exceeds that of natural sand concrete. ANN proved a valuable and reliable method of predicting concrete strength and workability based on the properties of the fine aggregate (FA) and the concrete mix composition

Predicting The Strength Properties of Self Healing Concrete Using Artificial Neural Network

An extensive simulation program is used in this study to discover the best ANN model for predicting the compressive strength of concrete with respect to the percentage of mineral admixture and percentage of crystalline admixture. To accomplish this, an experimental database of 100 samples is compiled from the literature and utilized to find the best ANN architecture. The main aim of this paper was to predict the strength properties of self-healing concrete (SHC) with crystalline admixture and different mineral admixtures using an artificial neural network (ANN). The samples, 100 in Number, with different mixes, were analyzed after 28 days of curing of the samples. ANN was fed with the experimental data containing four input parameters: mineral admixture (MA), percentage of mineral admixture (PMA), Percentage of crystalline admixture (PCA), and type of exposure (TE). Correspondingly, strength (Fc) was the output parameter. The experimental data showed a good correlation with the values predicted by ANN. In conclusion, ANN could be used to accurately evaluate SHC strength characteristics

Comparison of artificial intelligence methods for predicting compressive strength of concrete

Tlačna čvrstoća betona je značajan parametar u projektiranju betona. Točnim predviđanjem tlačne čvrstoće betona mogu se smanjiti troškovi i ostvariti uštede u vremenu. U ovom radu se na temelju šest raznih međunarodnih nizova podataka uspoređuje uspješnost predviđanja vrijednosti tlačne čvrstoće betona primjenom nekoliko metoda baziranih na umjetnoj inteligenciji (prilagodljivi neuroneizraziti sustav, algoritam slučajnih šuma, linearna regresija, klasifikacijsko i regresijsko stablo, regresija potpornih vektora, metoda najbližih susjeda i stroj za ekstremno učenje). Učinak tih metoda procjenjuje se pomoću koeficijenta korelacije, korijena srednje kvadratne pogreške, srednje apsolutne pogreške i srednje apsolutne postotne pogreške. Usporedni rezultati pokazuju da je prilagodljivi neuroneizraziti sustav uspješniji od ostalih u svim nizovima podataka.Compressive strength of concrete is an important parameter in concrete design. Accurate prediction of compressive strength of concrete can lower costs and save time. Therefore, thecompressive strength of concrete prediction performance of artificial intelligence methods (adaptive neuro fuzzy inference system, random forest, linear regression, classification and regression tree, support vector regression, k-nearest neighbour and extreme learning machine) are compared in this study using six different multinational datasets. The performance of these methods is evaluated using the correlation coefficient, root mean square error, mean absolute error, and mean absolute percentage error criteria. Comparative results show that the adaptive neuro fuzzy inference system (ANFIS) is more successful in all datasets

Image Analysis of Surface Porosity Mortar Containing Processed Spent Bleaching Earth

Image analysis techniques are gaining popularity in the studies of civil engineering materials. However, the current established image analysis methods often require advanced machinery and strict image acquisition procedures which may be challenging in actual construction practices. In this study, we develop a simplified image analysis technique that uses images with only a digital camera and does not have a strict image acquisition regime. Mortar with 10%, 20%, 30%, and 40% pozzolanic material as cement replacement are prepared for the study. The properties of mortar are evaluated with flow table test, compressive strength test, water absorption test, and surface porosity based on the proposed image analysis technique. The experimental results show that mortar specimens with 20% processed spent bleaching earth (PSBE) achieve the highest 28-day compressive strength and lowest water absorption. The quantified image analysis results show accurate representation of mortar quality with 20% PSBE mortar having the lowest porosity. The regression analysis found strong correlations between all experimental data and the compressive strength. Hence, the developed technique is verified to be feasible as supplementary mortar properties for the study of mortar with pozzolanic material

Estimation of bremsstrahlung photon fluence from aluminum by artificial neural network

Background: As bremsstrahlung photon beam fluence is important parameter to be known in a photonuclear reaction experiment as the number of produced particle is strongly depends on photon fluence. Materials and Methods: Photon production yield from different thickness of aluminum target has been estimated using artificial neural network (ANN) model. Target thickness and incoming electron energy has been used as input in ANN model and the photon fluence was output. Results: The results were estimated using ANN model for three different thickness and compared with the results obtained by EGS (Electron Gamma Shower) simulation. Conclusion: It can be concluded from this work that the bremsstrahlung photon fluence can be obtained using ANN model

Hardened concrete properties and durability assessment of high volume fly ash concrete

Concrete is produced more than any other material in the world. Sustainable construction is extremely important in today\u27s industry and fly ash is the leading material for sustainable concrete design. The addition of fly ash improves many fresh and hardened concrete properties. However, the slow hydration process associated with fly ash makes the use of the material in large amounts undesirable in conventional construction. This study evaluated the hardened concrete and durability performance of several high-volume fly ash (HVFA) concrete mixes. The various HVFA concrete mixes evaluated within this study consisted of 70 percent replacement of portland cement by weight of cementitious material and water-to-cementitious ratios (w/cm) ranging from 0.30 to 0.45. Studies were conducted on hardened properties including: compressive strength, flexural strength, splitting tensile strength, and modulus of rupture. A shrinkage analysis was also performed to evaluate drying and free shrinkage. The durability performance of the HVFA concrete was also evaluated. Results obtained from the tests revealed that compressive strengths of HVFA concrete are comparable to portland cement concrete with a reduced w/cm. Also, a reduction in concrete shrinkage was observed for HVFA concrete. The durability testing showed HVFA concrete increased the corrosion resistance and decreased the chloride penetration. Finally, existing relationships for hardened material properties and durability of conventional concretes are applicable to HVFA concretes --Abstract, page iii

The Optimization of Concrete Mixtures for Use in Highway Applications

Portland cement concrete is most used commodity in the world after water. Major part of civil and transportation infrastructure including bridges, roadway pavements, dams, and buildings is made of concrete. In addition to this, concrete durability is often of major concerns. In 2013 American Society of Civil Engineers (ASCE) estimated that an annual investment of $170 billion on roads and$20.5 billion for bridges is needed on an annual basis to substantially improve the condition of infrastructure. Same article reports that one-third of America’s major roads are in poor or mediocre condition [1]. However, portland cement production is recognized with approximately one cubic meter of carbon dioxide emission. Indeed, the proper and systematic design of concrete mixtures for highway applications is essential as concrete pavements represent up to 60% of interstate highway systems with heavier traffic loads. Combined principles of material science and engineering can provide adequate methods and tools to facilitate the concrete design and improve the existing specifications. In the same manner, the durability must be addressed in the design and enhancement of long-term performance. Concrete used for highway pavement applications has low cement content and can be placed at low slump. However, further reduction of cement content (e.g., versus current specifications of Wisconsin Department of Transportation to 315-338 kg/m3 (530-570 lb/yd3) for mainstream concrete pavements and 335 kg/m3 (565 lb/yd3) for bridge substructure and superstructures) requires delicate design of the mixture to maintain the expected workability, overall performance, and long-term durability in the field. The design includes, but not limited to optimization of aggregates, supplementary cementitious materials (SCMs), chemical and air-entraining admixtures. This research investigated various theoretical and experimental methods of aggregate optimization applicable for the reduction of cement content. Conducted research enabled further reduction of cement contents to 250 kg/m3 (420 lb/yd3) as required for the design of sustainable concrete pavements. This research demonstrated that aggregate packing can be used in multiple ways as a tool to optimize the aggregates assemblies and achieve the optimal particle size distribution of aggregate blends. The SCMs, and air-entraining admixtures were selected to comply with existing WisDOT performance requirements and chemical admixtures were selected using the separate optimization study excluded from this thesis. The performance of different concrete mixtures was evaluated for fresh properties, strength development, and compressive and flexural strength ranging from 1 to 360 days. The methods and tools discussed in this research are applicable, but not limited to concrete pavement applications. The current concrete proportioning standards such as ACI 211 or current WisDOT roadway standard specifications (Part 5: Structures, Section 501: Concrete) for concrete have limited or no recommendations, methods or guidelines on aggregate optimization, the use of ternary aggregate blends (e.g., such as those used in asphalt industry), the optimization of SCMs (e.g., class F and C fly ash, slag, metakaolin, silica fume), modern superplasticizers (such as polycarboxylate ether, PCE) and air-entraining admixtures. This research has demonstrated that the optimization of concrete mixture proportions can be achieved by the use and proper selection of optimal aggregate blends and result in 12% to 35% reduction of cement content and also more than 50% enhancement of performance. To prove the proposed concrete proportioning method the following steps were performed: • The experimental aggregate packing was investigated using northern and southern source of aggregates from Wisconsin; • The theoretical aggregate packing models were utilized and results were compared with experiments; • Multiple aggregate optimization methods (e.g., optimal grading, coarseness chart) were studied and compared to aggregate packing results and performance of experimented concrete mixtures; • Optimal aggregate blends were selected and used for concrete mixtures; • The optimal dosage of admixtures were selected for three types of plasticizing and superplasticizing admixtures based on a separately conducted study; • The SCM dosages were selected based on current WisDOT specifications; • The optimal air-entraining admixture dosage was investigated based on performance of preliminary concrete mixtures; • Finally, optimal concrete mixtures were tested for fresh properties, compressive strength development, modulus of rupture, at early ages (1day) and ultimate ages (360 days). • Durability performance indicators for optimal concrete mixtures were also tested for resistance of concrete to rapid chloride permeability (RCP) at 30 days and 90 days and resistance to rapid freezing and thawing at 56 days