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Bridge Scour is the localized loss of the geomaterials around the foundation of a bridge as a result of the movement of water around it. Scour is a great risk to the stability of a bridge’s foundation, thus leading to collapse, loss of lives and setback in a nation’s socio-economic life. Artificial Neural Networks (ANN) are collections of simple, highly connected processing elements that learn according to sets of input parameters and use that to simulate the networks of nerve cells of humans or animal central nervous system. The Asa Dam Bridge, one of the longest bridges in Ilorin, Kwara State, Nigeria, has five (5) spans of 20m each. The bridge connects Ilorin to the Ogbomosho Express way (leading to the western part of the country) and the Eyenkorin-Jebba road (leading to the north). Thus, the bridge has a high economic value. In this research, factors such as flow depth, average flow velocity of the river and median sediment size were investigated to show how they affect the depth of scour around the bridge pile foundation. Data were taken for a period of 48 weeks and ANN was applied to predict and generate a model that shows how these factors relate to the scour depth of the riverbed. The model revealed that the hydraulic parameters and soil grading around the pile cap of Asa River Bridge bears significant influence on the scour depth of its foundation. The model was compared with five (5) other established scour equations.

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Background: Random Forest (RF) is a technique that optimises predictive accuracy by fitting an ensemble of trees to stabilise model estimates. The RF techniques were adapted into survival analysis to model the survival of patients with liver disease in order to identify biomarkers that are highly influential in patient prognostics. Methods: The methodology of this study begins by applying the classical Cox proportional hazard (Cox-PH) model and three parametric survival models (exponential, Weibull and lognormal) to the published dataset. The study further applied the supervised learning methods of Tuning Random Survival Forest (TRSF) parameters and the conditional inference Forest (Cforest) to optimally predict patient survival probabilities. Results: The efficiency of these models was compared using the Akaike information criteria (AIC) and integrated Brier score (IBS). The results revealed that the Cox-PH model (AIC = 185.7233) outperforms the three classical models. We further analysed these data to observe the functional relationships that exist between the patient survival function and the covariates using TRSF. Conclusion: The IBS result of the TRFS demonstrated satisfactory performance over other methods. Ultimately, it was observed from the TRSF results that some of the covariates contributed positively and negatively to patient survival prognostics.

It is very common in medical studies for a patient to experience more than one event rather than one of interest. This led to exposing an individual to multiple risks and medical practitioners need to account for these risks concerning some prognostic factors.  There are many methods of dealing with multiple events in survival data classically, however, these methods break down when considering the top-down effect of the prognostic factors concurrently and when the risks of events are correlated (competing risks). This study aimed to develop a decision tree using a within-node homogeneity procedure in survival analysis with multiple events to classify individual risks for the competing risks. The study considers the use of Deviance and Modified Cox-Snell residuals as a measure of impurity in the Classification Regression Tree (CART). The flexibility and predictive accuracy of our learning algorithm would then be compared with other existing methods through simulation and real-life data. The results of the simulation revealed that: using Deviance and Cox-Snell residuals as a response within the node homogeneity classification tree performs better than using other residuals irrespective of performance indices. Results from empirical studies of the two real-life data that the proposed model with Cox-Snell residual (Deviance=16.6498) performs better than both the Martingale residual (deviance=160.3592) and Deviance residual (Deviance=556.8822). Conclusively, using Cox-Snell residual (Mean Square Error (MSE)=0.01783563) as a measure of impurity in CART revealed improved performance than using any other residual methods (MSE=0.1853148, 0.8043366).