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In this article, an efficient methodology is presented to optimize the topology of structural systems under transient loads. Equivalent static loads concept is used to deal with transient loads and to solve an alternate quasi-static optimization problem. The maximum strain energy of the structure under the transient load during the loading interval is used as objective function. The objective function is calculated in each iteration and then the dynamic optimization problem is replaced by a static optimization problem, which is subsequently solved by a convex linearization approach combining linear and reciprocal approximation functions. The optimal layout of a deep beam subjected to transient loads is considered as a case study to verify the effectiveness of the presented methodology. Results indicate that the optimal layout is dependant of the loading interval.

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An excavation damage zone (EDZ) can be defined as a rock zone where the rock properties and conditions have been changed due to the processes related to an excavation. This zone affects the behavior of rock mass surrounding the construction that reduces the stability and safety factor and increase probability of failure of the structure. This paper presents an approach to build a model for the identification and classification of the EDZ. The Support vector machine (SVM) is a new machine learning method based on statistical learning theory, which can solve the classification problem with small sampling, non-linearity and high dimension. However, the practicability of the SVM is influenced by the difficulty of selecting appropriate SVM parameters. In this study, the proposed hybrid Harmony search (HS) with the SVM was applied for identification and classification of damaged zone, in which HS was used to determine the optimized free parameters of the SVM. For identification and classification of the EDZ, based upon the modulus of the deformation modulus and using the hybrid of HS with the SVM a model for the identification and classification of the EDZ was built. To illustrate the capability of the HS-SVM model defined, field data from a test gallery of the Gotvand dam, Iran were used. The results obtained indicate that the HS-SVM model can be used successfully for identification and classification of damaged zone around underground spaces.

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The excavation damaged zone (EDZ) can be defined as a rock zone where the rock properties and conditions have been changed due to the processes related to an excavation. This zone affects the behavior of rock mass surrounding the construction that reduces the stability and safety factor and increase probability of failure of the structure. In this paper, a methodology was examined for computing the creation probability of damaged zone by Latin hypercube sampling based on a feed-forward artificial neural network (ANN) optimized by hybrid particle swarm optimization and genetic algorithm (HPSOGA). The HPSOGA was carried out to decide the initial weights of the neural network. A case study in a test gallery of the Gotvand dam, Iran was carried out and creation probabilities of 0.191 for highly damaged zone (HDZ) and 0.502 for EDZ were obtained.

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The development of an excavation damaged zone (EDZ) around an underground excavation can change the physical, mechanical and hydraulic behaviors of the rock mass near an underground space. This might result in endangering safety, achievement of costs and excavation planed. This paper presents an approach to build a prediction model for the assessment of EDZ, based upon rock mass characteristics changed. Rock engineering systems (RES) was used as an appropriate method for choosing the best parameter that expresses the occurrence of EDZ. Modulus of deformation with the highest weight in the system was selected as the most effective changed parameter. The adaptive network-based fuzzy inference system (ANFIS) with modulus of deformation as input was used to build a prediction model for the assessment of EDZ. Three ANFIS models were implemented, grid partitioning (GP), subtractive clustering method (SCM) and fuzzy c-means clustering method (FCM). A comparison was made between these three models and the results show the superiority of the ANFIS-SCM model. Furthermore, a case study in a test gallery of the Gotvand dam, Iran was carried out to illustrate the capability of the ANFIS model defined.

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Structural  design  optimization  usually  deals  with  multiple  conflicting  objectives  to  obtain the minimum construction cost, minimum weight, and maximum safety of the final design. Therefore, finding the optimum design is hard and time-consuming for  such problems.  In this paper, we borrow the basic concept of multi-criterion decision-making and combine it with  Particle  Swarm  Optimization  (PSO)  to  develop  an  algorithm  for  accelerating convergence  toward  the  optimum  solution  in  structural  multi-objective  optimization scenarios.  The effectiveness of the proposed algorithm was illustrated in some benchmark reinforced concrete (RC) optimization problems. The main goal was to minimize the cost or weight of structures while satisfying all design requirements imposed by design codes.  The results confirm the ability of the proposed algorithm to efficiently find optimal solutions for structural optimization problems.

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By incorporating structural engineering, animal husbandry, and veterinary, this interdisciplinary research accomplishes the following two main objectives: 1) design and optimization to reduce the weight of the steel structure skeleton of the stable with ECBO & CBO algorithms; 2) improving the performance of the natural ventilation system in the stable with some changes in the structure's geometric design.
In this study, each algorithm's performance will be investigated in the course of accomplishing the aforementioned objective. Furthermore, using stress ratios by algorithms in each member will be studied. Finally, using the algorithms, a stable steel structure with lower weight is designed.
In this paper, through changing and improving the structure's geometric design, a structure more compatible with the natural ventilation system's requirements is designed. These changes are as follows: 1) design of a taller stable structure; 2) larger design of the air inlets in the joint line between the upper part of the side walls and the lower part of the pitched roof.

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In this study the challenges of managing the civil projects in oil and gas industry over recent years that failed were investigated. For this purpose, the relevant cases and their effectiveness were categorized by analyzing research data obtained from the questionnaire results. The results obtained from the research showed that there is a positive and significant relationship between the project management knowledge and reduction in the challenges. Lack of attention to the project's feasibility study before starting the project, adverse risks at the beginning and end of the projects, proper knowledge of contracts, and the project team's skill are the items that will fail the project if they are not appropriately managed. Since the team's correct design and the key persons of the project and before that feasibility and the necessity of doing it in vital projects in the country are very important and in such a way, the two components studied in this research are derived from the risk management of projects. Considering the importance of this issue as a case study, these cases were investigated in gas pipeline projects in Fars province.
 

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One of the important factors in the efficiency of construction operations is the proper replacement construction projects of the construction site layout planning (CSLP). That this would not be possible without oversight of the factors affecting it. Therefore, the study of factors affecting the replacement of construction site layout is considered vital in projects. Different factors are involved in the replacement of CSLP, which examine the economic dimension and the effects of changing costs and time during work. Due to the complexity of the subject, it is solved using hyper-innovative algorithms. This research is a linear programming model for optimizing the layout of equipment for Launcher/Receiver (L/R) stations. Due to the complexity of the problem, the invasive weed algorithm was used to achieve an optimal response. The goal is to minimize the total costs associated with transportation, relocation and relocation, and changes during implementation. The results of the calculations and output of the algorithm showed the variation of the answer in the optimal layout of the CSLP, which was obtained at the lowest distance and the most optimal mode. The results were presented in a similar scenario in the projects.