فهرست مطالب hamed naseri

Understanding the genetic diversity and the relationships between species and plant ecotypes is an important step to preserve genetic resources. In this study, the genetic diversity of 15 Guava (Psidium guajava) genotypes was investigated using Rapid and ISSR markers in the molecular breeding laboratory of the Faculty of Agriculture, University of Birjand. Nine out of 36 RAPD primers and six out of seven ISSR primers showed polymorphism. In total, RAPD and ISSR primers amplified 80 and 35 bands, respectively, which 54 out of 80 (67.5%) and 25 out of 35 (71.42%) bands were amplified by RAPD and USSR primers, were polymorphic, respectively. The lowest and highest polymorphic information content was observed in RAPD marker in OPAB09 primer (0.13) and OPAB17 primer (0.40) and in ISSR primer in ISSR3 primer (0.19) and ISSR5 primer (0.37), respectively. According to the RAPD marker, genotypes M5 with M4 (0.83) had the highest and genotypes CH5 with R4 (0.19) had the least similarity, respectively. According to ISSR marker, genotypes R2 with R3 (0.86) had the highest and M1 with CH4 (0.12) had the least similarity, respectively. The Cluster analysis classified genotypes according to RAPD marker into five groups and according to ISSR marker into three groups, respectively. In total, the results showed that there is sufficient genetic diversity among Guava genotypes and RAPD and ISSR are useful markers in identifying polymorphic regions and estimating genetic distance and managing genetic resources of Guava.

International Roughness Index is one of the most important indices to assess the pavement quality, and it is generally employed in pavement maintenance planning. In this study, by virtue of differential evolutionary programming and Long Term Pavement Performance data, two prediction models are introduced to predict pavement deterioration function and treatments’ improvement. For pavement deterioration modeling, climate condition, traffic, and pavement structural features are taken into account. Besides, the most-applicable pavement treatments utilized in Iran are considered in this investigation. 520 and 336 data are applied to model pavement deterioration and treatments’ improvement. The predicted models have a very high accuracy and can be used in various fields. The pavement deterioration model and treatments’ improvement model reach the coefficient determination of 99% and 97% for both training and testing data, which indicates the high-level accuracy of the introduced models. The treatments’ improvement model provides the mean squared error of 0.2 approximately.

In this study, four concrete types, including ordinary Portland cement concrete, fly ash concrete, slag concrete, and slag-fly ash concrete, are taken into account in order to estimate their compressive strength by two novel machine learning methods (genetic algorithm and soccer league competition algorithm), and four types of regressions (linear, 2nd order polynomial, exponential, and logarithmic). Subsequently, the precision of prediction models are compared based on performance indicators, and the most accurate models are applied in the optimization problem modeling. Drawing on results, the most precise model to estimate the compressive strength of ordinary Portland cement concrete is the genetic algorithm, and the soccer league competition is the most accurate model to estimate the strength of other concrete types. Afterward, a model is developed so as to design mixture proportions of 40MPa concretes. Fly ash concrete, slag-fly ash concrete, and slag concrete reduce the unit cost by 35.2%, 29.9%, and 23.1%, respectively, compared with ordinary Portland cement concrete. Fly ash concrete, slag-fly ash concrete, slag concrete, and ordinary Portland cement concrete require 217.25 kg, 150.47 kg, 102 kg, and 414.64 kg cement to be manufactured. Furthermore, the slag concrete can reduce the amount of cement in the mixture proportion by 75.4%, and it is the most eco-friendly concrete.

Travel time prediction as an essential issue has been scrutinized in recent decades. To this end, various techniques are applied to estimate travel duration in the dynamic networks and intelligent transportation systems. Accordingly, in this investigation, prediction of travel time is considered by machine learning techniques. Initially, the experimental test is planned, and the travel time effective parameters are spotted. Subsequently, with the assistance of the floating car method, and Mytacks application, the data are collected in six elected roads. After data preparation, stop delay, grades, and the number of the lane are determined as the most effective travel time criteria. In this study, a novel machine learning technique based on the coyote optimization algorithm is introduced, and its precision is compared with five conventional regression models. Drawing on results, the accuracy of the coyote optimization algorithm-based machine learning technique is more than that of other prediction methods. The coefficient of determination of the introduced machine learning technique for training and testing data is equal to 0.746 and 0.724, respectively. Furthermore, coyote optimization algorithm-based machine learning estimates 73% of testing data with an error of fewer than 20 seconds.

Choosing an appropriate strategy to maintain pavements has become a significant concern. Recently, pavement agencies tackle large-scale networks, which makes the problem complicated. To prevail in this complexity, utilizing metaheuristic algorithms can be an ideal approach. By increasing the dimension of the problem, the competency of metaheuristic algorithms is by far enhanced. To this end, the water cycle algorithm is applied to solve the problem. In this investigation, two models, including single objective optimization and multi-objective optimization, are taken into consideration. In the single-objective model, the minimization of the network International Roughness Index (IRI) is considered as the objective function. In multi-objective optimization modeling, minimization of the network International Roughness Index and embodied CO2 emission are taken into account simultaneously. Furthermore, a new constraint is considered in the model, which leads to restricting the budget fluctuation in different years of the analysis period. A network, including 79 segments, is the case study of this investigation. The results reveal that the water cycle algorithm is highly qualified to solve the pavement maintenance and rehabilitation problem. According to the results, multi-objective optimization reduces the CO2 emission by 47.2% compared with single-objective modeling. Furthermore, the variation of minimum and maximum costs is less than 20% in the planning horizon.

Maintenance and rehabilitation planning plays a pivotal role in the implementation of efficient pavement management system. The variables are generally considered deterministic to solve the problem. Nevertheless, this problem tackles with high level of uncertainty. For instance, the budget, as one of the essential criteria, is fluctuated owing to resource limitation, and policy alteration. If the budget is taken into account as deterministic, the result of problem may be considerably different from the absolute optimal solution to the problem. This investigation aims to solve maintenance and rehabilitation problem by consideration of a novel and powerful uncertainty approach. To this end, a multi-stage integer linear uncertainty model is introduced in order to find a solution, which is feasible and optimal in all of the uncertainty modes. The case study of this paper is a network including six pavements. The outcomes indicate that the proposed model is competent to consider budget fluctuation, and it introduces a solution that is optimal for all uncertainty scenarios. The comparison of deterministic and uncertainty models reveals that the number of preventative maintenance selected by uncertainty model is more than that of deterministic model. The number of preventative maintenance is increased from 36.67% to 40.91% if uncertainty is considered in the problem, and it can be postulated that the uncertainty model tries to allocate budget to more segments so as to reduce the likely negative impacts of budget fluctuation on the project.

Maintenance and rehabilitation scheduling optimization is one of the most critical parts of the pavement management system. Pavement agencies usually face large-scale pavement networks. The complexity of network management is exponentially increased in the circumstances that the dimension of the network is extended. Hence, meta-heuristic algorithms and decomposition techniques are usually applied to solve these problems. In this investigation, energy consumption optimization is taken into account as an essential environmental criterion. Furthermore, a recently developed meta-heuristic algorithm called Soccer league competition algorithm is utilized to solve the problem. This robust algorithm is inspired by the competitions of players and teams in soccer leagues. A real network containing 84 segments is the case study of this investigation. According to the results, the Soccer league competition algorithm is competent to solve a large-scale network problem in a short time. Moreover, the comparison of different strategies outcomes indicates that considering energy consumption as the second objective function reduces the energy consumption and total cost 13% and 10%, respectively, and increases the average international roughness index by 9%.

The mixing process performs a critical role in the concrete microstructure, which defines the final product’s quality. Optimizing mixing time and mixing speed during the mixing process can reduce the unit price and energy consumption without impacting product quality. Furthermore, the energy consumption to manufacture self-consolidating concretes (SCC) is more than that of conventional concretes due to their compositions and structures. Due to the fact that rheological and mechanical properties assess the performance of concrete, the effects of mixing energy on the mentioned properties of self-consolidating concretes are taken into consideration simultaneously in the current investigation. Accordingly, different mixtures contained polysaccharide-based viscosity modifying agent and limestone powder are made under different mixing speeds and mixing times. Drawing on the results of mixing time, the optimum time for mixing is 8 min in this study in order to improve the workability and to provide ideal levels of rheological features such as minimum yield stresses (static and dynamic). In the aspect of mixing speed, double increasing from 20 to 40 rpm rises the slump flow by 5%. Besides, accelerating mixing speed increases dynamic yield stress by 37% for 11 min mixed mixtures, which is more than that of other mixing times mixtures’ dynamic yield stress increment. In the aspect of mechanical properties, mixing time increment increases the concrete’s compressive strength. Nevertheless, other mechanical characteristic criteria do not significantly depend on mixing energy.