This study investigated the reliability of three timber species from the Niger Delta—Mansonia (Mansonia altissima), Ububa Red (Berlinia grandiflora), and Angala (Rhizophora racemosa) as bridge beams to Eurocode 5 design rules. The strength classes for Mansonia, Ububa Red and Angala timber species were established in accordance with the provisions of EN 338 (2009). The study classified Mansonia as D50, Ububa Red as D60 and Angala as D70 respectively. The limit state functions were developed considering the failure of the beams in bending and shear respectively. Reliability analysis was used to assess the structural performance of each timber species, and sensitivity analyses were conducted by varying design parameters to observe their effect on reliability indices. The reliability indices were computed using custom MATLAB programs based on the First Order Reliability Method. It was shown that the reliability indices generally decreased with increasing beam span and live load on beams for both bending and shear failure modes respectively. The timber beams showed the capacity to support live loads of 11.5 KN/m, 14 KN/m, and 16.5 KN/m over a 50-year reference period, meeting the target reliability index of 3.8 recommended by Eurocode 0 (1978). It is also found that the reliability of the beam increased with increase in depth and width of the beams. For this period, required beam depths were identified as 350 mm for Mansonia, 325 mm for Ububa Red, and 300 mm for Angala timber beam respectively. It is also found that Mansonia, Ububa Red and Angala timber beams are very safe for a span of 6.8m, 7.5m and 8m respectively. The Mansonia, Ububa Red and Angala timber beams are generally very safe in bending and shear.

This study investigates the application of Artificial Neural Networks (ANNs) for rainfall-river modelling in the Oramiriukwa River, located in Owerri, Imo State, Nigeria. The study utilizes daily streamflow data from the Ulakwo station (1978–1988) alongside corresponding rainfall and temperature data for Imo State, obtained from the Nigerian Meteorological Agency (NiMet). A series of Feedforward Multi-Layer Perceptron (MLP) models were developed and tested using MATLAB, with performance evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). Among the models, Model 4 ([15]) delivered the best results, achieving an R² of 0.9158, MSE of 0.1294, and RMSE of 0.3597, demonstrating its effectiveness for streamflow prediction in the Oramiriukwa River. Model 2 ([30, 15, 5]) also showed good performance (R² = 0.9029), but its increased complexity suggested a potential risk of overfitting. Model 1 ([20, 10]) yielded lower predictive accuracy, highlighting the need for more complex architectures or additional input features to improve ANN performance for hydrological applications. These results confirm the effectiveness of ANNs in modelling nonlinear hydrological processes and suggest their potential for improving streamflow prediction in similar river basins. This study contributes to the growing use of data-driven methods in hydrological modelling in Nigeria and offers a foundation for future work aimed at enhancing the accuracy and robustness of ANN models.