algorithm

In today's modern world, traces of artificial intelligence can be found in almost any field. In recent years, with the introduction of algorithms and machines, the fields of building engineering and construction project management have also experienced new challenges, from optimizing processes and improving product design to automating tasks and parametric design. Artificial intelligence in structural engineering involves the use of advanced algorithms and machine learning techniques to simplify and improve various aspects of the design and analysis process. Also, artificial intelligence software related to construction is a group of technological tools and solutions that use artificial intelligence to optimize various functions of this industry. On the other hand, one of the relatively new topics that artificial intelligence can enter into is the investigation of various types of damage, including progressive damage in the design and construction of structures. In this article, an attempt has been made to define artificial intelligence and machine learning, to explain the various functions of this technology, and practical algorithms, plus introduce useful and pioneering software in civil engineering, where artificial intelligence is the main origin. Also, the basic influencing parameters in the study of progressive collapse, such as critical path identification and extreme load patterns, have been investigated. According to the functions stated in this research, the importance of using artificial intelligence in theoretical studies and future applied projects is clearly known. Especially vital projects such as Spatial Structures or buildings with a special seismic bearing system such as Staggered Truss Systems and structures with high ductility requirements that need special analysis, design, and monitoring.

Short-term forecasting of streamflow is one of the most important goals in water resources management and flood control. However, one of the problems that researchers always face in this type of prediction is the Lack of an accurate and high time resolution database. The Fluvial Acoustic Tomography (FAT) is an innovative method of data gathering which has both high accuracy and high-resolution time. Therefore, by using the data collected from this technology with a suitable forecast model, accurate short-term streamflow forecasting can be achieved. In this research, the effect of FAT data on short-term streamflow forecasting by Combinatorial GMDH Algorithm (CGA) has been investigated and compared with one obtained from the Rating Curve method. The k-fold cross-validation criterion has been used to prevent over-fitting. The results showed that the FAT data increases the accuracy of short-term forecasting. As an example, the Nash-Sutcliffe coefficient (ENS) for the 1, 6, 12, 24, 48, and 72 hours forecast were 0.98, 0.96, 0.94, 0.88, 0.73, and 0.54, respectively. While these values for the Rating Curve ones were 0.97, 0.84, 0.61, 0.27, 0.12, and 0.11, respectively.

The importance of maintenance of any system, with worn out components,  during operation, is  clear. Railway transportation system as a global and public system, requires permanent monitoring and maintenance. Because its reliability, safety and efficiency are highly dependent on the quality of the railway tracks. Due to the fact that the length of the railway tracks and the required maintenance volume are very high, the use of human resources to implement projects is uneconomic and virtually impossible. This is done by mechanized machinery used on railways. Because of the limited number of machines and the need to implement projects as many as possible, it is necessary to develop a heuristic technique capable to take both of the objectives and the priorities and requirements of the railway into account, at the same time. In this paper, the scheduling process of railway tracks maintenance operation in Iran Railways has been studied. A heuristic method has been designed and presented to schedule this operation. In order to see better the algorithm's capabilities, the results of the algorithm has been compared with the plan prepared by Iran Railways in 2017. To validate the algorithm, a simulation model has been developed. The results show that the algorithm can significantly increase the volume of scheduled operations. Providing a plan in a very short time and meeting all of the railway requirements and priorities are the other advantages of the proposed algorithm.

Structures depending on the type of their application are subject to different types of loading, such as wind, earthquake and explosion loads. On the other hand, in order to increase the useful lifetime of the structures, optimal operating conditions must be provided. Inappropriate utilization, excessive loading, changes in structural stiffness due to the material imperfection will cause the structure to come out of its design conditions and leads to unsafe structure. Therefore, on time assessment of imperfection for retrofitting and rehabilitation in order to increase the useful lifetime of the structure is very important. This article presents a novel and robust approach for assessment of imperfections in the structure using the concepts of vibration analysis and finite element method. A multi-objective Niched-Pareto Genetic Algorithm was used for computation of geometric position and intensity of imperfection in a steel continuous beam structure as well as a cantilever beam. The results of the imperfection assessment in continuous steel beam demonstrate that the proposed method has great potential in localization and quantification of imperfection in the multi-stage scenario. In addition, the results of this study for cantilever beam were compared with the same experimental results reported in the literature. These results demonstrated that the computational error in the proposed method was less than 0.06, while the error of similar experimental results has been reported as 0.3. Finally, the results of this research, demonstrate that the metaheuristic Niched-Pareto Genetic Algorithm performs very well and therefore this algorithm is recommended as a robust and practical approach for imperfection assessment of structures.

Nowadays, transportation planners are more engaged in effects of transportation on economic as a term of sustainable transportation. Sustainable transportation is an approach to organize principle for sustaining finite transportation resources necessary to provide for the needs of future generations considering economic, social and environmental terms. This paper is focused on the economic term of sustainable transportation through transportation cost and revenues for people as well as for the system operator. An algorithm is offered based on a method to determine provincial economic sustainability index of transportation using factor analysis through SPSS. As an example the algorithm is adopted for analyzing the economic status of sustainable transportation of the U.S. states. Results show that, according to the developed methodology, District of Colombia, Nebraska and Kansas are the most sustainable states while Alaska, Maryland and New Hampshire are the lowest ones regarding the economic term.

Few algorithms are available for economic optimization of underground mining area . This is mainly because there are a variety of underground mining methods with various restrictions and conditions and the underground mining parameters are more complex . Therefore, it does not allow the development of general optimization tools, especially, software tools or computer programs. Based on the Global Optimisation for Underground Mining Area (GOUMA) algorithm, a computer program , GOUMA-CP, was developed to implement the mentioned algorithm . GOUMA enjoys a new system of modelling named "Variable Value Economic Model " (VVEM) and benefits from the mathematical proof and provide the true optimal solution according to the conditions and environment it is applied . This paper includes a description of the GOUMA-CP program in details and the corresponding algorithm briefly . This computer program has successfully been applied to optimise underground mining area of a gold mine in Australia.

Traffic condition is one of the most important issues in urban transportation system of large cities, because it affects other important areas directly and indirectly. For example, water and air pollution as an environmental issue and physical or mental health of citizens as a health issue. Suggesting best routes based on different criteria such as traffic condition is one of the ways to reduce urban traffic, implicitly. In GIS, best routes can be suggested to users based on different algorithms that have been developed so far, to move in urban road network, and consider different costs, travel time, traffic, distance and other factors. Aim of this paper is reviewing route finding algorithms in GIS. For this purpose, algorithms are investigated in two categories, deterministic and heuristic algorithms. Advanced route finding problems which are closer to the real world problems are also described. Finally, a comparison between algorithms are done based on different parameters so that researchers can find the best suited algorithm based on their requirement.

Optimization of underground stope is defined as looking for a boundary with the highest profit considering technological and geometrical constraints. The first algorithms were presented about five decades ago for optimization of mining limits. Most of algorithms, presented till now, have been applied for optimization of open pit limits. Few algorithms have been presented for Underground cases, most of which have been tailored for a specific mining method or lack rigorous techniques. There are, however, some rigorous algorithms but they fail to provide 3D solution,. In this paper, a new algorithm is introduced to optimize stope layout using integer programming (IP) technique. The algorithm is implemented on a specific model, derived from conventional block model. It is a rigorous algorithm and provides a realistic optimum solution, compared to its alternatives. For the first time, two more constraints, namely the minimum width of rib pillars and the maximum stope height, are considered in the proposed algorithm.

Claims are becoming more popular as a way to survive and, indeed, an integral part of modern contracts, i.e. for non-industrial design and building contracts. It should be considered that although the probability of claim incidences cannot be eliminated in any project, their occurrence in special projects with any contract can be prevented by identifying them. As most claims are propounded by contractors, the authors of this research found 250 cases of claims in projects in Iran offered by design and building contractors (including the contractor and their advisor) in non-industrial design and building contracts. They were discovered through studying diverse references and interviewing experts, and studying documents related to real claims of several projects implemented in non-industrial design and building methods in Iran. In this process, at rst, the occurrence of the Algorithm of Legal-Contractual claims was codied by content analysis, and then a closed questionnaire, including the frequency of occurrence and severity of dierent kinds of legal-contractual claim in nonindustrial design and building contracts, was distributed among experts in the eld, and the results were accurately analyzed by the authors. These questionnaires aimed to study the frequency of occurrence and severity of claims, and, based on the rate of transparency of existing claims in non-industrial design and building contracts, were categorized in 12 rows, 4 parts, and 2 groups of legal-contractual claims. Understanding and cognizance of these claims can be useful in predicting future claims and in minimizing their eects on similar future non industrial design and building projects. Offering a categorization of claims of non-industrial design and building projects in Iran, from the perspective of the degree of ambiguity and transparency, and codication of the chart of rate of transparency, frequency of occurrence and severity of claim, is a key nding of this research.

Tourism is one of human activities that have strong cultural، social and economic effects on societies. One of the concerns of the tourist is how to select the order and the path to the tourism centers he/she wants to visit. Information such as the distances between tourist attractions، traffic and the start location of the tourist should be considered when deciding about the travel path of the tourist. The goal of tourist path finding is to find a path، through which the tourist can visit all attractions in shortest possible time، considering his priorities، traffic and the distances between attractions. This can be assumed as a spatial optimization problem with such a wide search space that cannot be solved using mathematical and deterministic methods. The goal of this research is to solve this problem using Artificial Bee Colony (ABC) meta-heuristic algorithm. The simulated data of a city with 10 tourist attraction is used for the implementation. At the 350th run of the ABC algorithm the acceptable results are gained. Taking into account the implementation speed، convergence speed and pattern، and the quality of the answers، the algorithm can be accepted as a suitable solution to the tourist path finding problem.

In order to design retaining walls, first the initial dimensions of the wall should be estimated. In order to choose these dimensions, the designer should use reasonable proportions that were achieved by previous experiences of the designing different retaining walls. These dimensions are introduced based on a ratio of the height of walls. Current reseasrches showed that by changing the conditions such as properties of the backfill materials of the retaining wall, the local seismic conditions, the height of the wall and limitation in choosing the arbitrarily dimensions etc, these estimated dimensions would not be appropriate for an economical design. In this paper, by means of genetic and bees algorithms, economical dimensions of the wall for static, pseudostatic and pseudodynamic loading conditions will be calculated precisely in a such way that stability of the retaining wall against sliding, overturning and bearing capacity are provided. Also from structural consideration point of view, the designed walls could resist appropriately against the applied forces.