http://ir.lib.ruh.ac.lk/handle/iruor/20790 2026-04-26T15:57:15Z http://ir.lib.ruh.ac.lk/handle/iruor/20794 Identification of accident black spots to improve the safety on highways. Karunarathna, M.D.S.M.; Edirisinghe, A.G.H.J.; Pathirana, W.P.A.M. Road accidents remain a critical global concern, necessitating efficient transportation system management. Identifying high-risk areas, known as "Black Spots (BS)," is crucial for road safety. The research objectives involve conducting a comprehensive analysis of past accident records, comparing the outcomes produced by various statistical methods, and evaluating how road geometry contributes to improve safety at identified BS locations. Four methods, Accident Point Weightage, Accident Rate Screening, Empirical Bayesian, and Spatial Autocorrelation (Moran’s I) Method, coupled with the Getis-Ord Gi function were applied to five years of traffic accident data (2018-2022) from Padeniya - Anuradhapura Road (0 km to 54.4 km) in Sri Lanka. The analysis reveals a 19% fatality rate, with rear-end collisions (26%), angle collisions (18%), and pedestrian accidents (17%) being prevalent. Forty-nine road segments were identified as BS locations by at least one method, showing consistency among APW, ARS, and EB methods. Spatial Autocorrelation method results differed but still identified high-risk areas. This suggests that these methods can be favorably applied to roads with similar characteristics as those selected for this study. Considerably, each BS method yielded both concordant and disparate BS locations, with enhanced accuracy observed for all methods. 2025-10-01T00:00:00Z http://ir.lib.ruh.ac.lk/handle/iruor/20793 Comparative analysis of Multinomial logit (MNL) and Nested logit (NL) modeling approaches for mode choice decisions. Vidurangana, R.L.N.; Vindyani, H.A.R.N.; Dharmarathna, W.R.S.S. Number of methods have been used globally for predicting the travel mode choice, including Multinomial Logit (MNL), Nested Logit (NL), Multinomial Probit (MNP), Generalized Extreme Value (GEV), Mixed Multinomial Logit (MMNL) and Artificial Neural Network (ANN) modelling approaches. Among them, MNL and NL approaches are predominant. However, the assumptions in MNL regarding the independence error components might not hold in developing countries like Sri Lanka. NL partially relaxes this assumption. The study gathered trip-specific, socio-economic, and household data via online and face-to-face questionary surveys. After meticulous parameter selection and cross-sectional analysis, both MNL and NL models were developed and compared. Buses (46%), motorbikes (19%), trains (12%), and cars (11%) were the dominant modes. Notably, total travel time significantly influenced mode choice, with NL model exhibiting superior accuracy over MNL. Recognizing the weight of this attribute informs urban planning, policy formulation, and transportation system optimization. 2025-10-01T00:00:00Z http://ir.lib.ruh.ac.lk/handle/iruor/20792 A general numerical modelling approach to assess the performance of a building subjected to blast loads. Shamiranga, K.L.L.; Fernando, P.L.N. Accidental and unintentional explosion events have become a common occurrence in the present world context, hence, presenting a challenge for structural engineers tasked with designing potentially blast-resistant structures. Hence, understanding the structural response to blast loads is crucial for accurate design, often studied using Numerical modelling, which often involves Finite Element Modelling (FEM). However, current FEM methods are complex and costly, limiting their accessibility for day-to-day design works. This study aims to develop a general numerical modelling technique for simulating the behaviour of multi-storey buildings under blast loads. Key blast load parameters for different blast loading scenarios were identified, and reinforced concrete buildings - of varying heights (9 m to 60 m) - consisting of beams, columns, slabs, and shear walls were simulated and analysed using the commercially available Midas Gen software. Blast loads were modelled as dynamic nodal loads on the exposed face of the building. Top floor displacement was taken as the main output of the model. It was observed that the proposed approach is more accurate for far-field blasts than for near-field blasts which resulted in localised effects. The findings of the study yield several practical outputs which can be used in general structural engineering practices involving blast-resistant structures. 2025-10-01T00:00:00Z http://ir.lib.ruh.ac.lk/handle/iruor/20791 Effect of Bolt Pre-loading on the Rotational Stiffness of Bolted Connections. Dharmawansha, D.A.S.T.; Herath, H.M.S.T. This study explores the influence of bolt preloading on the rotational stiffness of stainless-steel end-plate connections, specifically focusing on extended end-plate connections with six bolts. In this work, a validated numerical model was used to investigate the impact of varying levels of bolt preloading on rotational stiffness. A snug tight connection serves as the baseline for comparison. The investigation reveals how bolt preload affects the rotational behaviour of the connection. The validated numerical model provides a comprehensive understanding of the relationship between bolt preloading and rotational stiffness in stainless steel end-plate connections. For example, it is shown that the rotational stiffness increases by 134% with ultimate preloading. This research contributes to advancing the knowledge base in structural engineering by clarifying the effect of connection element geometric properties on rotational stiffness. The findings not only improve understanding of rotational behaviour but also can help optimise the design and performance of stainless-steel end-plate connections in various structural applications. 2025-10-01T00:00:00Z