poorya rashvand

the present study, the laboratory examination of compressive and flexural strengths of concrete samples made with recycled materials, steel fibers and PVA was done at different ages.the presence of recycled aggregates instead of natural aggregates on the compressive and flexural strengths of non-fiber concrete at the ages of 3, 7, 28, 56, and 90 days was examined.Then, by adding different percentages of steel fibers ,PVA including 0.5, 1, 1.5،, and 2 to concrete with natural and recycled materials, we compared their compressive and flexural strengths in the mentioned ages.The results revealed that the compressive and flexural strengths of concrete samples made with natural materials are always higher than recycled materials.Also, comparing the compressive strength of concrete samples with the percentage of different fibers revealed that the highest compressive strength was related to the samples made with natural materials with 0.5% and the lowest compressive strength was related to the concrete samples made with recycled materials with 2% PVA fibers.Additionally,the flexural strength of the samples with PVA fibers is far less than the steel fibers of the same age and the same fiber percentages.By increasing the percentage of steel fibers and PVA from 0.5 to 2%, the flexural strength of the samples also increases.In all ages of concrete, the highest flexural strength is related to concrete samples made with natural materials with 2% steel fibers and the lowest compressive strength is also related to samples made with recycled materials with 0.5% PVA fibers.higher percentage of steel fibers and PVA in concrete leads to a reduction in the stone materials, and thus, a reduction in the compressive strength of concrete, which is more significant in steel fibers due to the higher density of steel fibers compared to PVA fibers.Also, due to the increase in the elasticity of concrete, its flexural strength increases.

One of the destructive consequences of the earthquake was the failure of the infrastructure structures and facilities of each region, such as the fuel transmission system. In general, the fuel transmission system in cities consists of pipes buried in the ground for fuel transfer and various tanks on the ground for the storage and storage of petroleum materials. With the occurrence of earthquakes and the movement of the earth's tectonic plates, we have witnessed two main destructive phenomena: 1- failure of fuel transfer pipes 2- creating cracks in the pipes (leakage of oil materials). Based on the position of the fuel networks system, which consists of pipes buried in the soil, it is expected that after the pipe breaks and leaks, the contents inside the pipes of the fuel transmission system will move in the soil as a pollutant, and in addition to soil pollution based on the coefficient hydraulic transmission, has entered the groundwater resources and caused the pollution of these valuable resources. Considering the nature of the earthquake, which is classified as a random phenomenon, an accurate prediction of its time, place, and intensity cannot be provided, and also based on the numerous parameters involved in the performance of the fuel transmission system under seismic loading, which has wide uncertainties. There is a need to carry out extensive studies (vulnerability analysis, risk analysis) on the fuel network under different earthquake scenarios and consider getting the conditions of uncertainty assessment on the pollution of groundwater resources by the leakage of oil pollutants resulting from the failure of the fuel transmission system should be implemented.

In this research, a comprehensive model has been presented to evaluate the pollution of underground water resources based on the vulnerability of the fuel transmission pipeline under the conditions of an earthquake. In the research model, the seismic vulnerability of the buried pipeline has been developed by considering the conditions of uncertainty and using the machine learning method. The results of the validation of the developed numerical models have shown acceptable performance and high accuracy in predicting the seismic vulnerability of the pipe. Also, the aquifer pollution assessment using the DRASTIC model has been implemented. In this research, 5 main machine learning algorithms, each of which has different computational branches, have been used to develop a model for predicting the seismic vulnerability of a buried pipeline.In order to consider the conditions of uncertainty in the input data of stress prediction models and the possibility of seismic damage that directly affects the results, the conditions of uncertainty in the input data of the model have been taken into account regarding the main goal of assessing the probability of seismic vulnerability in the line The buried pipe is under different earthquake scenarios, taking into account the uncertainty in the input data values of the model, the most critical conditions for the possibility of pipe vulnerability are extracted. Based on this, 4 main parameters: 1- Depth of working, 2- Modulus of elasticity, 3- Soil density, 4- Magnitude of earthquake in numerical models based on the conditions of uncertainty in the initial input values and using the probability distribution function and the coefficient of variation. The Monte Carlo method is used for simulating critical conditions (the highest probability of pipe seismic failure) have been extracted. In this research, the number of Monte Carlo simulation repetitions is considered equivalent to 100,000 repetitions.

The results of the performance of the machine learning model based on different algorithms indicate the amount of error in predicting the stress on the buried pipe compared to the exact values based on the initial data set. Based on the results, it is clear that the GPR models have a better performance than other computational models of machine learning. Based on the results, it is clear that among the algorithms of the GPR method, the Exponential GPR algorithm has the minimum error rate, so the machine learning model developed based on this algorithm has an MSE error equal to 0.00329 and the correlation R equal to 0.999. The results of the performance of the machine learning model for predicting seismic vulnerability based on different algorithms showed that GPR models (Gaussian Process Regression Models) had better performance than other computational models of machine learning and had a minimum error rate, so the machine learning model developed based on this algorithm has an MSE error equal to 0.00329 and a correlation value R equal to 0.999. Based on the results of the drastic model, the pollution of the Tehran aquifer based on the first seismic scenario (earthquake event with magnitude Mw = 5) showed that the pollution zoning of the aquifer has changed and the area of the area with moderate pollution has increased by about 9% based on the ML prediction model. According to the results of the forecasting models, which indicate the number of failures 2 and the number of leaks 15 under the first seismic scenario in the pipeline, the amount of 10% changes in the medium vulnerability level can be investigated.Based on the results of the implementation of the second seismic scenario (earthquake with a magnitude of 6 on the Richter scale) based on the seismic damage prediction model, the ML prediction model showed an increase in the level of moderate pollution by 35% and an increase in the level of high pollution by 5%. The zoning of Tehran aquifer pollution based on the third seismic scenario (earthquake event with magnitude Mw = 7) showed that the underground water sources of Tehran city have been heavily polluted due to the leakage of fuel products from the fuel pipeline in such a way that according to the model ML, the area of low pollution level is 18%, medium pollution level is 45% and high pollution level is 37%. Based on the results obtained in the event of an earthquake with a magnitude of 7 on the Richter scale, the amount of aquifer pollution based on the ML prediction model indicates an increase of 25% in the medium pollution level and 19% in the level of high pollution. There has been a lot of pollution.

Groundwater is one of the main sources of sustainable development in human societies, and due to the supply of water needed by the drinking, agriculture, and industry sectors, their pollution will have destructive effects. In addition, the fuel transmission network is of great importance due to the storage and transportation of petroleum products. The importance of this system increases in various aspects during events such as earthquakes. Pollution of underground water sources due to leakage from the fuel transmission system is one of the secondary effects of the earthquake and leaves an adverse effect on the environment and human health. This research is focused on evaluating the vulnerability of groundwater due to the failure of the urban fuel transmission network against the occurrence of an earthquake, which was conducted in the form of case studies on the fuel transmission network of Tehran city and the Tehran-Karaj plain aquifer.

In this research, a unified model for seismic damage analysis and risk assessment under conditions of uncertainty in the fuel transmission network of Tehran city has been defined and implemented. In this model, the consequences of damage to the fuel transmission network under three earthquake scenarios (magnitude 5, 6, 7 on the Richter scale) have been evaluated on the pollution of underground water in Tehran. In addition, strategic strategies have been presented in order to reduce the effects of groundwater pollution caused by earthquakes. The proposed model uses a multi-layer perception artificial neural network (ANN). Also, the DRASTIC model has been used to evaluate groundwater pollution based on fuel leakage from the damaged fuel transmission network. In these studies, strategic planning has been done based on the use of robust decision-making techniques and the degree of robustness in order to reduce the probable effects of groundwater pollution by using the theory of minimum-maximum regret.

Results:

 The results of this research showed that the developed artificial neural network model has a high ability to assess damage(failure-leakage) in the pipeline of the fuel transmission network so that the root-mean-square error (RMSE) and the correlation coefficient R are equal to 0.029 and 0.98 respectively. Based on the results, the amount of damage to the pipeline in the first scenario (Mw=5) is equal to 15 leaks and 2 failures, in the second scenario (Mw=6), it is equal to 25 leaks and 7 failures and in the third scenario (Mw=7) the number of leaks was 27 and 9 failures. According to the results obtained from aquifer pollution under three earthquake scenarios, it is clear that in the scenario of an earthquake with Mw=5, 30% of the aquifer has medium pollution potential and 55% of the potential of low pollution, in the scenario of an earthquake with Mw=6, 45% The aquifer has medium pollution potential and 18% has low pollution potential, and in the earthquake scenario with Mw=7, 55% of the aquifer has medium pollution potential and 22% has low pollution potential. In line with the strategic planning of aquifer pollution reduction, different strategies were evaluated against different scenarios with the minimum-maximum regret criterion. Finally, three strategies were presented to reduce the pollution of underground water resources. According to the results, the use of the strategy (insulation of the environment around the pipeline) led to a 70% reduction in groundwater pollution, and the use of the strategy (implementation of an intelligent seismic system to cut off the fluid flow in the event of an earthquake), which was known as a robust strategy, led to aquifer pollution has been reduced by 75%.

Based on obtained results from the performance evaluation of the model developed in this research, it was found that the presented model had an acceptable performance in predicting the seismic vulnerability of the fuel transmission pipeline and assessing the pollution of the aquifer. This model has the ability to be implemented in different urban areas and to evaluate the performance of the fuel transmission system under different earthquake scenarios, as well as to evaluate groundwater pollution. Also, based on the results of the strategic management of groundwater pollution control, implementing an intelligent seismic system to cut off the fluid flow in the event of an earthquake can be used as a comprehensive solution to reduce environmental damage to groundwater resources specifically in the seismic regions.

Due to the importance role of the behavioural characteristics of the project manager in construction projects and the need for effective and useful communication with other stakeholders of the project, studies have not comprehensively examined the behavioural competencies of the project manager and the lack of a practical model in the Iranian construction industry to select a capable project manager feels tangible. This research is on a more systematic approach to address the behavioural capabilities of the project manager by considering the factors related to his behavioural variables to evaluate the behavioural capabilities of the project manager. Using a multi-stage research process, including extensive literature review, analysis and a combination of important factors in the behavioural competencies of project managers, the development of an analytical network model (AHP, TOPSIS) was considered. This model would be a decision support tool for the employer organization to compare project managers according to their behavioural competencies by creating a database. This protects construction companies from the effective challenges posed by the performance of project managers and helps them to evaluate the performance of their project managers. It also enables project managers to focus on their efforts to address behavioural practices to improve their project performance.

Behavior of buildings against past earthquakes indicates their susceptibility. In order to reduce vulnerability and ensure safety of buildings against earthquakes and, consequently, undertake crisis management, it is specifically important to use qualified constructors in their construction. The executive bylaw for Article 33 of Building Engineering System Act requires owners to use qualified constructors. However, the formal nature of qualified constructors and lack of practical use of them cause the upcoming problems. In this work, the success rate of qualified constructors use plan in the construction of urban buildings is investigated and, then, weaknesses of the plan are identified and the important duties of constructors from the viewpoint of employers are priorities. Using the questionnaire and obtaining the needed information from Qazvin Province Engineering System Organization, success rate and weaknesses of the plan are examined from the viewpoints of employers, supervisors and real and legal qualified constructors. Moreover, based on the view of authorities and experts, important duties of qualified constructors are collected and their indices are determined. Using the Likert model-based quationnaire, the determined indices are weighed and priorities. The research findings showed that use of qualified constructors in the construction of urban buildings was very low and factors such as type of contract, qualification level of constructors considering the construction level per city are effective in the success rate. Weight of the determined indices for the importance of duties of contructors demonstrated all the indices for the application of qualified constructors are effective and of special significance.

Nowadays, all managers in all organizations want to make optimal use of facilities and capacities in various departments. Therefore, the existence of a model to provide feedback on destructive factors in bridges with the aim of improving the management of concrete bridges and obtaining a tool to satisfy the needs of managers, seems very necessary and logical. In line with the above necessity, concrete road bridges under the supervision of the Highway and Road Transportation Organization of the country will be selected as a statistical sample and the performance of concrete bridges will be compared with each other using data envelopment analysis (DEA) method. In order to evaluate the performance of bridges, efficient and inefficient bridges are determined and the effective factors in their inefficiency are determined. In order to evaluate the efficiency of inputs such as weather and traffic conditions, and the outputs including structural failure factor and serviceability were examined using the input-axis CCR model through DEA-Master software. The results showed that 35 bridges out of 384 concrete road bridges measured in the main axes of Zanjan province have strong performance and the average efficiency of these 384 bridges is 0.98 out of 1.Also, the 8 km bridge of Zanjan-Qazvin freeway is the most efficient bridge in terms of threat of reference times.