gh. shafabakhsh

Construction resources in addition to identifying project's time and cost, sets up a unique spatial requirement in the project life cycle. The dynamic nature of construction resources leads to confined spatial conditions along with the project progresses. Incomprehension of the workspace condition and spatial-time conflicts lead to a considerable decrease in productivity and efficiency of working teams. Reduced efficiency of contractors alters temporal specifications such as critical path, duration and sequence of activities as well as budget planning of the entire project. Building Information Modeling (BIM) make a better understanding rather than activity-based scheduling techniques by covering the simultaneous cost, time and spatial information of activities. The Five-Dimensional Computer-Aided Design (5D CAD) model in this research, quantifies the number and the distance of spatial interference and also highlights space critical level of construction components and its corresponding resources on a daily basis. To conduct the research, an innovative three-phased modelling methodology was deigned: preparation of basis model, definition and allocation of workspaces, and finally simulation and detection of spatiotemporal conflicts. This investigation has started the initial stage of preparation information model through the generation and linking of two Product and Work Breakdown structures (PBS and WBS). The cost and Time required for creation of 5D CAD models are calculated by resource analysis and work-group efficiency rate. In proposed model, work execution space is simulated by BIM accessible software on a daily level and time-space conflicts are recorded automatically.This research by presentation of a novel methodology linked the graphical and non-graphical data together and made visual 5D workspace planning of construction resource and their interaction. The main purpose of this system is to contribute promoting planners' understanding about the potential spatial conflicts based on the overlapping duration and simulation of activities workspace according to the real situation. Proposed methodology was evaluated by implementing it in a real case (Micro-level project) and analyzing data obtained from it. Finally, the correlation coefficient between the variable such as number of activities, clashes number, and conflict distances in per day was assessed and an effective dependency in the same direction was perceived. The result of real case modelling demonstrates that the collisions of executive resource workspaces are occurred approximately in a quarter of project days with the average conflict distances of 2.3 meter.

Construction, maintenance and rehabilitation of highway infrastructures are the most essential issues for developing and improving a sustainable transportation industry. Besides high costs of these issues, improper performance of some aspects such as safety and security creates some problems which will threaten the reliability of a transportation system. Crash prediction models (CPMs), as safety analysis tools, have a specific role in analyzing the contributing factors in occurrence of traffic accidents. This study aimed to evaluate the effects of pavement distresses on the frequency of run-off-road accidents which are the most frequent and severe accidents in Iran and around the world. In doing so, about 150 km of Semnan-Tehran divided rural highway was examined and its pavement condition indice (PCI) was calculated. Then, using GENMOD and NLMIXED procedures in SAS software, two generalized linear models and 39 nonlinear negative binomial regression models were developed. After evaluating the goodness-of-fit of the models and estimating their error, comparison of these models leads to the most proper model. To estimate the goodness-of-fit of the models and select the best-fitted model, several criteria were implemented including standard deviation/scaled deviation, Pearson χ2, -2LL, AIC, AICc and BIC. Moreover, two criteria of mean absolute error and root mean squared error were used to estimate the validity of model results. In analyzing of residuals by standardized residuals method, the mean error was about -0.1048 and the variance was 0.9259, which are close to the assumptions of zero mean and variance equal to 1. CPMs showed that nonlinear negative binomial regression model fits significantly better with the available data, which demonstrates the association between PCI and frequency of run-off-road accidents. Sensitivity analysis of the model indicates that for nonlinear CPM, a unit increase in PCI results in around 1.93% reduction in frequency of run-off-road accidents.

Network of roads is a part of assets of each country that can be progressive in different fields and aspects. So, maintenance and repair of this property, is significant. Accordingly, pavement has a very specific effect due to its high costs. The More correct maintenance, lower costs appeared. Asphalt rutting is one of the flexible pavement deteriorations. Rutting has caused high maintenance and repairs costs and several problems for road users for years, so researches are concentrated on this problem. Investigations showed rutting has various origins, although, the most important factor is permanent deformation production due to lack of asphalt shear strength. Predicting asphalt pavement strength against permanent deformation while preparing laboratory mix design can prevent producing asphalt with high rutting potential. However, not only laboratory instruments for this purpose are expensive, but an experienced labor should be used for a considerable time to work with them. These factors made the researchers look for lower cost and time procedures for assessment of rutting in laboratory step. Although the most popular method of determining rutting potential, is loading wheel, but in lots of researches, dynamic creep test is used. Previous studies showed flow number of dynamic creep test is related to rutting potential inversely. In this research which covers a wide variety of aggregates, gradation, bitumen and filler used in Iran a simple model for predicting flow number is developed. Then it was appeared that gyratory shear stress curve which is the one of six output curves of gyratory compactor, can be represented as a logarithmic model. After modeling this curve for all specimens and determining the slope of density for them, a simple model was presented to estimate the flow number by SPSS software. The independent variables of this model were determined from output curves of gyratory compactors. This model was validated by using neural network and genetic algorithm. And its accuracy was confirmed by an appropriate percent of confidence. Flow number can be predicted while asphalt specimens are prepared in order to determine the 9 Developing a Model for Estimating … / Ziari, Divandari, Shafabakhsh and Fakhri optimal bitumen in laboratory by this model. So not only there is no need for expensive equipments to perform rutting and dynamic creep test, but also a reduction in mix design procedure time is achievable and producing of asphalt with high potential of rutting is prevented.

Rutting is one of the most important kinds of flexible pavements’ deformation which annually consumes significant amount of maintenance and repairing costs of asphalt pavements. On the other side, in level one of Superpave’s mix design method that has outstanding vantages compared to the Marshal method, there is lack of a simple test to determine the sample resistance against permanent deformation which is the main reason of rutting in asphalt. Available evaluation methods of rutting potential of asphalt mixtures are often expensive and time consuming. For these reasons, there is a need to present an accurate method to determine the samples’ rutting condition in a short time and with low costs. In this research, two different types of lime and silicon aggregate, two types of grading, two types of bitumen, two types of filler and three different bitumen contents have been used to provide samples. After the Gyratory Shear Stress Modeling, using the model’s parameters and gyratory compactness’ slope, two mathematical models have been developed for determination of sample’s permanent deformation. These models have been validated using Genetic Algorithm and Artificial Neural Network and can evaluate asphalt rutting potential simultaneously as the asphalt samples are provided to determine the optimum bitumen content. Therefore, there is no need to provide expensive rutting test’s apparatus and also the time to present an appropriate mix design will be reduced to