Musa Adamu

Non-destructive evaluation of nano silica-modified roller-compacted rubbercrete using combined SonReb and response surface methodology

Roller-compacted concrete (RCC) is being widely used in highway construction industry (for pavement applications) due to its enormous advantages over conventional concrete rigid pavement. However, the major problems related to RCC pavement are the rigidity and relative tendency to crack due to low tensile strength. To address this problem, crumb rubber (CR) can be added as partial replacement of fine aggregate. High elastic and deformation properties of CR will increase the ductility of RCC pavement to absorb the deformation and strain energy caused by traffic loads. However, incorporating CR to RCC pavement leads to a reduction in mechanical properties which needs to be addressed for proper utilisation. Therefore, in this study, roller-compacted rubbercrete (RCR) was produced by partially replacing fine aggregate with CR. Nano silica was used as an additive to cement to mitigate the loss of mechanical properties in RCR caused by incorporation of CR. The non-destructive tests, that is, rebound hammer test and ultrasonic pulse velocity (UPV) were used to evaluate the performance of RCR. Response surface methodology was then used to develop models for predicting the 28 days UPV and rebound number (RN) of RCR. Combined UPV–RN (SonReb) models for predicting the 28 days strength of RCR based on combining UPV and RN were developed using multivariable regression (double power, bilinear, and double exponential models). From the combined SonReb models formulated, it is concluded that the double exponential model has better accuracy for predicting the 28 days compressive strength of RCR compared to the double power models recommended by RILEM 43-CND for conventional concrete.

Effect of crumb rubber and nano silica on the fatigue performance of roller compacted concrete pavement

Abstract Roller compacted concrete (RCC) pavement is subjected to continues traffic loading from vehicular activities which can results to fatigue cracking. Fatigue is one of the commonest defects affecting pavement which affect the cost of maintenance, and shortens pavement design life. To carter for these factors, higher deformation resistant pavements with longer design life need to be designed. Therefore, in this study, crumb rubber was used as a partial replacement to fine aggregate in RCC pavement to improve its fatigue life. Five mixtures were considered; one control mixture, two mixtures with fine aggregate replaced using crumb rubber at 10 and 20% by volume; one mixture containing 20% crumb rubber as partial replacement to fine aggregate 1% nano silica added by weight of cementitious materials. Lastly, one high volumefly ash (HVFA) RCC pavement mixture where 50% cement was replaced with fly ash, and 20% fine aggregate replaced with crumb rubber. The results showed that both crumb rubber and nano silica increases the bending resistance and fatigue life of RCC pavement. While HVFA decreases both flexural strength and fatigue performance of RCC pavement. The double logarithmic-equation can best be used to determine the stress level–number of cycles (S–N) fatigue behavior and relation for RCC pavement mixtures.

Abrasion Resistance of Nano Silica Modified Roller Compacted Rubbercrete: Cantabro Loss Method and Response Surface Methodology Approach

Roller compacted concrete (RCC) when used for pavement is subjected to skidding/rubbing by wheels of moving vehicles, this causes pavement surface to wear out and abrade. Therefore, abrasion resistance is one of the most important properties of concern for RCC pavement. In this study, response surface methodology was used to design, evaluate and analyze the effect of partial replacement of fine aggregate with crumb rubber, and addition of nano silica on the abrasion resistance of roller compacted rubbercrete (RCR). RCR is the terminology used for RCC pavement where crumb rubber was used as partial replacement to fine aggregate. The Box-Behnken design method was used to develop the mixtures combinations using 10, 20, and 30 crumb rubber with 0, 1, and 2 nano silica. The Cantabro loss method was used to measure the abrasion resistance. The results showed that the abrasion resistance of RCR decreases with increase in crumb rubber content, and increases with increase in addition of nano silica. The analysis of variance shows that the model developed using response surface methodology (RSM) has a very good degree of correlation, and can be used to predict the abrasion resistance of RCR with a percentage error of 5.44. The combination of 10.76 crumb rubber and 1.59 nano silica yielded the best combinations of RCR in terms of abrasion resistance of RCR. © 2018 Published under licence by IOP Publishing Ltd.

Skid Resistance of nano silica modified roller compacted rubbercrete for pavement applications: Experimental methods and response surface methodology

Abstract Roller compacted concrete (RCC) pavement usage is limited to low-speed roads due to its low skid resistance, caused by the smooth surface texture that makes it unsuitable for use in high-speed traffic pavements. If used in high-speed roadways will lead to increased skid related accidents. In this study, crumb rubber was used as partially replaced with fine aggregate at levels 10, 20, and 30% by volumeto produce roller compacted rubbercrete (RCR) so as to improve the skid resistance of RCC pavement, and nano silica was added at 0, 1, 2 and 3% by weight of cementitious materials to mitigate loss of strength with incorporation of crumb rubber. The British Pendulum Number (BPN) was used to measure the skid resistance. Incorporating up to 20% crumb rubber and 2% nano silica increases both skid and impact resistance. The findings showed that RCR with 20% CR and 2% NS can be used for motorways, trunk, and class 1 roads. Finally, response surface methodology was used to develop the relationship between BPN in wet/dry conditions and crumb rubber/nano-silica. The analysis of variance for response surface methodology analysis shows that the quadratic models developed has a very good degree of correlation, and can be used to predict the skid resistance of RCR. The results of multi-objective optimization showed that an optimum mixture can be achieved with a 17.20% volumereplacement of fine aggregate and 1.87% nano silica addition by weight of cementitious materials to have the highest skid resistance RCR for pavement applications.

An Artificial neural networks (ANN) model for evaluating construction project performance based on coordination factors

Abstract Construction projects are delivered in a multidisciplinary environment, which need continues coordination. The aim of this paper is to develop an ANN model to evaluate the influence of coordination factors on construction projects performance. For this purpose, the most effective 16 coordination factors impacting the construction projects performance have been identified. After that, through a questionnaire survey, the extent of coordination factors application and the corresponding project’s performance were collected. Three multilayer feed-forward networks with Back-Propagation and Elman-Propagation algorithms were adopted to train, validate, and test the cost, time and quality, as performance evaluation indicators. Consequently, the training process continues unit it reaches the pre-defined error or up to 1000 epochs. The results of Mean Square Error (MSE) confirmed the accuracy of the networks with an average value of 0.0231. Furthermore, the determination coefficient (R2) for the three networks of cost, time, and quality were obtained to be 0.77, 0.76 and 0.75, respectively.