Mammadov, Ahmad
The main drive upon which this study relies is to introduce a prescriptive accident prevention model to avoid work related accidents by predicting outcomes of work-related accidents in pipeline construction with using machine learning algorithms. In depth study on construction accidents is crucial due to construction being one of the most hazardous industry and being temporary in nature. To come up with a prescriptive accident prevention model, all incident reports from a pipeline project were analysed and a data set was prepared for 1,184 cases with attributes. These attributes consist of twenty-four immediate causes, eighteen root causes and three consequences that are nearmiss, asset or property damage and injury. A machine learning tool, RapidMiner, is used to predict outcomes of the cases for different data subsets by using eleven different ML algorithms. One of the machine learning algorithms, Deep Learning, was selected due to performing better in predicting outcome of complex data sets and in majority of twelve data sets. Model performance was attempted to be optimized with parameter optimization. It was concluded that predictive models with optimized parameters can predict accident outcomes better and a prescriptive accident prevention model can be presented thanks to these predictions. With a prescriptive model, it may be possible to provide a foresight about the root cause, immediate cause, or date and time of potential accidents. The causes of accidents can be eliminated; hence accidents can be prevented with these predictions having statistical basis.


Identification of linear handling models for road vehicles
ARIKAN, KUTLUK BİLGE; Ünlüsoy, Yavuz Samim; Korkmaz, I.; Celebi, A. O. (2008-01-01)
This study reports the identification of linear handling models for road vehicles starting from structural identifiability analysis, continuing with the experiments to acquire data on a vehicle equipped with a sensor set and data acquisition system, and ending with the estimation of parameters using the collected data. The model structure originates from the well-known linear bicycle model that is frequently used in handling analysis of road vehicles. Physical parameters of the bicycle model structure are s...
Prediction of Nonlinear Drift Demands for Buildings with Recurrent Neural Networks
Kocamaz, Korhan; Binici, Barış; Tuncay, Kağan (2021-09-08)
Application of deep learning algorithms to the problems of structural engineering is an emerging research field. Inthis study, a deep learning algorithm, namely recurrent neural network (RNN), is applied to tackle a problemrelated to the assessment of reinforced concrete buildings. Inter-storey drift ratio profile of a structure is a quiteimportant parameter while conducting assessment procedures. In general, procedures require a series of timeconsuming nonlinear dynamic analysis. In this study, an extensiv...
Development of of a safety performance index assessment tool by using a fuzzy structural equation model for construction sites
Gunduz, Murat; Birgönül, Mustafa Talat; Ozdemir, Mustafa (Elsevier BV, 2018-01-01)
The main goal of this study is to propose a safety performance index assessment tool to improve the construction safety. Formulation of the safety performance index of construction sites is achieved upon a validated multidimensional safety performance model. The contribution of this study could be summarized as incorporation of fuzzy set theory into structural equation modeling to develop a safety performance index assessment software tool. Case studies were conducted at 11 international construction sites ...
Application of design for verification with concurrency controllers to air traffic control software
Betin Can, Aysu; Lindvall, Mikael; Lux, Benjamin; Topp, Stefan (2005-11-11)
We present an experimental study which demonstrates that model checking techniques can be effective in finding synchronization errors in safety critical software when they are combined with a design for verification approach. We apply the concurrency controller design pattern to the implementation of the synchronization operations in Java programs. This pattern enables a modular verification strategy by decoupling the behaviors of the concurrency controllers from the behaviors of the threads that use them u...
Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments
Dogulu, N.; López López, P.; Solomatine, D. P.; Weerts, A. H.; Shrestha, D. L. (Copernicus GmbH, 2015-7-23)
In operational hydrology, estimation of the predictive uncertainty of hydrological models used for flood modelling is essential for risk-based decision making for flood warning and emergency management. In the literature, there exists a variety of methods analysing and predicting uncertainty. However, studies devoted to comparing the performance of the methods in predicting uncertainty are limited. This paper focuses on the methods predicting model residual uncertainty that differ in methodological complexi...
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