This paper aims to explore the challenges of fire hazard management in high-rise buildings by extending the application of BIM as a collaborative and automated tool covering deficiencies of fire ha...

Application of building information modelling for fire hazard management in high-rise buildings: an investigation in Sri Lanka

Highway Routine Maintenance Cost Estimation for Nevada by Monika Hagood Hualiang (Harry) Teng, Ph.D., Examination Committee Chair Associate Professor of Civil and Environmental Engineering University of Nevada, Las Vegas State highway agencies are obligated to maintain existing roads for the highway systems to work efficiently and with greater longevity. Every year NDOT is responsible for approximately 13,150 lane miles of existing infrastructure. With that in mind, resources need to be provided to maintain the highway system. The purpose of this research was to estimate annual routine maintenance cost for several typical treatment methods of highways. Five prioritization categories of highways used by NDOT were considered. Linear regression models were developed that present the relationship between costs including total maintenance cost and five maintenance cost components: labor, equipment, materials, manpower and stockpile, and the influencing factors: traffic load, road geometry, pavement structure, and climate. It was expected that the cost model depends on various roadway factors including elevation, number of lanes, age of the pavement, last year of pavement construction work, average daily traffic

/pdf/highway-routine-maintenance-cost-estimation-for-nevada-2ig7ieqch3.pdf

Highway Routine Maintenance Cost Estimation for Nevada

Maintenance costs of all types of buildings are most often ignored since they are incurred in the future. Potential investors are interested in capital costs—construction costs—while maintenance costs are considered as unimportant in the life cycle of a building. If there were a larger number of maintenance cost estimation models, it would be possible to estimate these costs and present them to potential investors more easily, thus making it easier and more effective to apply life cycle cost methods. A study on the characteristics and costs of the maintenance of sewer systems in the Republic of Croatia was conducted, wherein questionnaires were sent to companies operating public sewer systems. The data requested in the questionnaires were general data on enterprises, maintenance, data on sewer systems, quantities of sewer discharge, sewer pipes and data on maintenance costs of sewer systems. It was established that it is possible to use linear regression when creating a model for estimating the maintenance costs of a sewer system.

/pdf/estimating-maintenance-costs-of-sewer-system-1spu54j5.pdf

Estimating Maintenance Costs of Sewer System

Evaluating the cost of detecting bridge defects is a difficult task, but one that is vital to the lifecycle cost analysis of bridges. In this study, a detection cost sample database was established based on practical engineering data, and a bridge defect detection cost prediction model and software were developed using machine learning. First, the random forest method was adopted to evaluate the importance of the seven main factors affecting the detection cost. The most important indicators were selected, and the recent GDP growth rate was employed to account for the impact of social and economic developments on the detection cost. Combining a genetic algorithm with a multilayer neural network, a detection cost prediction model was established. The predictions given by this model were found to have an average relative error of 3.41%. Finally, an intelligent prediction software for bridge defect detection costs was established, providing a reliable reference for bridge lifecycle cost analysis and the evaluation of defect detection costs during the operation period.

A Machine Learning Framework for Predicting Bridge Defect Detection Cost

Concrete face rockfill dams are widely used in hydropower engineering because of their many advantages. As important structures, if the performance of the concrete face slab and the impervious curt...

Numerical simulation of concrete face rockfill dam seepage: case study of Miaojiaba dam, China

In order to make informed decisions on routine maintenance of bridges of expressways, the hierarchical regression analysis method was used to quantify factors influencing routine maintenance cost. Two calculation models for routine maintenance cost based on linear regression and time-series analysis were proposed. The results indicate that the logarithm of the historical routine maintenance cost is the dependent variable and the bridge age is the independent variable. The linear regression analysis was used to obtain a cost prediction model for routine maintenance of a beam bridge, which was combined with the quantity and price, and verified by a physical engineering example. In order to cope with the cost changes and future demands brought about by the emergence of new maintenance technologies, the time-series analysis method was used to obtain a model to predict the engineering quantities for the routine maintenance of a bridge based on standardized minor repair engineering quantities. Taking into account the actual cost of the minor repair project as well as the time-series analysis’ sample size demands, the annual engineering quantity was randomly decomposed into four quarterly data quantities, and the time-series analysis result was verified by physical engineering. These results can improve the calculation accuracy of the routine maintenance costs of reinforced concrete beam bridges. Furthermore, it can have a certain application value for improving the cost measurement module of bridge maintenance management systems.

/pdf/prediction-methods-for-routine-maintenance-costs-of-a-1eomkunp74.pdf

Prediction Methods for Routine Maintenance Costs of a Reinforced Concrete Beam Bridge Based on Panel Data

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Prediction Methods for Routine Maintenance Costs of a Reinforced Concrete Beam Bridge Based on Panel Data