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The coasts of Türkiye are vulnerable to tsunami hazards due to the intensive use of coastal areas and the activity of faults in the surrounding seas. The Samos-İzmir earthquake occurred on October 30, 2020, followed by a tsunami that affected the Sığacık Bay revealed this fact once again, demonstrating the importance of accurately modeling the tsunami hazard across the country. Probabilistic Tsunami Hazard Assessment (PTHA) results for various coastal engineering parameters (i.e., tsunami wave height, tsunami inundation distance) constitute one of the essential inputs of performance-based tsunami risk analysis. The TSUMAPS-NEAM project that ended in 2018 was one of the studies following the probabilistic approach for the Northeast Atlantic, Mediterranean, and connected seas (Basili et al. 2021). The primary objective of this study which is constructed within the TUBITAK (Scientific and Technological Research Council of Turkey) funded 121M750 project, is to develop a comprehensive probabilistic tsunami hazard analysis framework in which the uncertainties regarding active faults that can generate tsunamis for our country's Aegean Sea coasts are addressed fully. For this purpose, a holistic seismotectonic database has been created by compiling catalogs of active faults that can generate tsunamis in the Aegean Sea and its surroundings, important fault parameters, earthquake and focal mechanism solutions from national and international sources. The compiled database is utilized to determine possible tsunami source scenarios and model the epistemic and aleatory uncertainties in these scenarios. In this regard, a complete probabilistic set of tsunami source scenarios that have not been included in previous studies is being developed, and the near-shore tsunami wave height estimations will be determined by performing high-resolution tsunami simulations for each scenario. Considering the lack of hazard-based tsunami assessment for the coasts of the Aegean Sea, the near-shore tsunami wave height hazard curves to be obtained as a result of the project are of great importance in determining the effects of possible tsunamis and assessing the tsunami risk.Acknowledgement: This study is supported by TUBITAK 1001-Grant Project No: 121M750.

Bu çalışma tam derinlikli esnek üstyapıların katman özelliklerinin tahmin edilmesinde kullanılacak, Lig Şampiyonası Algoritması (LCA) ve Yapay Sinir Ağları (ANN) tabanlı bir geri hesaplama algoritması önermektedir. LCA-ANN adı verilen bu algoritma iki ana bölümden oluşmaktadır: (i) yol yüzeyindeki deplasmanların hesaplandığı, lineer olmayan sonlu elemanlar çözümleri ile geliştirilen, ANN ileri hesaplama modeli ve (ii) ANN modeline girdi olarak verilecek en uygun katman elastisite modullerinin belirlenmesinde kullanınan LCA arama algoritmasıdır. Önerilen yönetimin performansını değerlendirmek amacıyla sentetik olarak üretilen veri seti ile gerçek bir veri seti kullanılmıştır. Ayrıca, LCA’nın arama yeteğini değerlendirmek için, kabul görmüş algoritmalar olan Basit Genetik Algoritma (SGA) ve Parçacık Sürü Optimizasyonu (PSO) karşılaştırma amacıyla kullanılmıştır. Elde edilen sonuçlar göstermiştir ki önerilen algoritma düşük hata miktarlarıyla esnek üstyapı katman özellikleri tahmin edebilmekte ve geri hesaplama çalışmalarında hızlı ve güvenilir bir yöntem olarak ortaya çıkmaktadır., This study proposes a backcalculation tool, based on the hybrid use of League Championship Algorithm (LCA) and Artificial Neural Network (ANN), in order to predict the stiffness related layer properties of full-depth asphalt pavements. The proposed algorithm, namely LCA-ANN, is composed of two main parts; (i) an ANN forward response model, which is developed with the nonlinear finite element solution, for computing the surface deflections, and (ii) LCA search algorithm which is employed to search and provide the best set of layer moduli to the ANN model. In order to evaluate the performance of the proposed method, a synthetically generated dataset and real field data are utilized. Moreover, to assess the searching ability of LCA, well-accepted metaheuristic algorithms; Simple Genetic Algorithm (SGA) and Particle Swarm Optimization (PSO) are employed for comparison purposes. Obtained results reveal that the proposed algorithm can predict the layer properties with a low order of error values and enables fast and reliable tool for backcalculation studies.

© 2021Assessment of potential seismic risk and losses in urban environments is necessary for several purposes ranging from risk mitigation to city and regional planning. It is well known that loss estimation should be performed within an interdisciplinary setting involving earth sciences and engineering. Field experience from recent events worldwide shows that the spatial variability of seismic damage is due to the combined effects of earthquake source properties, local site conditions and structural characteristics. In this study, a scenario-based multi-input damage estimation framework in an urban region was utilized for the Gaziantep city center (southeastern Turkey) that is located in a region of high seismic hazard with no large events in the instrumental era. Initially, a thorough geological and seismo-tectonic assessment of the area was performed followed by estimation of two critical scenario events with moment magnitude (Mw) of 6.5 and 6.6 on nearby active faults. Then, a regional velocity model was compiled from regressions of existing regional geotechnical and seismic data in terms of the VS30 parameter. As the next step, field surveys for the assessment and classification of buildings in the study area were performed followed by vulnerability analyses. As the last step, the mean damage ratios were computed at 198 neighborhoods within the city center. The results indicate not only a high hazard but also high risk in the Gaziantep area due to the combination of close proximity to the faults along with local site effects and building fragilities.

Slope stability analysis is considered to be one of the most critical problems in geotechnical engineering. Typically, the analysis consists of two parts: (i) determination of the slip surface and (ii) calculation of the factor of safety. The primary aim is to find the most critical slip surface with the lowest factor of safety, which generally governs the behavior of landslide. Due to the nature of soils, the problem is very complex and requires solving several equilibrium equations simultaneously; therefore, obtaining accurate results can be a complicated task. In this study, a novel metaheuristic method, namely flower pollination algorithm (FPA), is proposed to locate the slip surface for various slope stability problems. The developed method utilizes Spencer and Janbu’s simplified limit equilibrium equations to calculate the safety factor. To investigate the performance of the proposed method, a set of slope stability problems having different soil properties with various geometries are solved, and results are compared with the solutions previously obtained in the literature. Findings indicate that FPA can effectively solve slope stability problems and reaches the critical slip surfaces producing the lowest safety factors when compared to those obtained using traditional optimization methods and some of the metaheuristic algorithms. Consequently, it is achieved that FPA presents competitive performance against well-known algorithms. Further improvements are on the way for FPA, which can be implemented for solving a broader range of slope stability problems.

The truss sizing optimization problem, where the cross-sectional areas of truss members are optimized, is a nonlinear and complex problem. The problem becomes quite challenging especially when a large number of variables exist. Metaheuristics proved to be working efficiently for this problem type. Flower pollination algorithm (FPA) is a recently developed metaheuristic search algorithm, and it has been successfully applied to structural optimization problems in the literature. In this paper, the formulation of FPA is enhanced to improve its local and global searching capabilities for better performance. For this purpose, a hybrid algorithm is developed by imitating the algorithmic structure of FPA while the mutation and crossover operators of differential evolution (DE) with elitism strategy are replaced with Levy flights of FPA to explore search space more efficiently and exploitation ability of FPA is improved with the addition of mutation factor and crossover operator of DE working in the local search formula of FPA. The performance of the proposed algorithm is tested with benchmark truss sizing optimization problems and the results are compared with other metaheuristics. The results show that hybrid FPA improves the capabilities of the original one, produces more robust solutions than both FPA and DE, generates quite competitive results for the test problems compared to the previous studies with different algorithms. Therefore, the hybrid FPA proves to be a promising optimization algorithm alternative for engineering practice.

Retaining walls need to be designed optimally since huge cost savings are probable considering their dimensions and materials used in the design and construction. Even better savings are possible when they are constructed in earthquake-prone regions. In this study, an improved flower pollination algorithm (IFPA) is used to optimize the design of the reinforced concrete cantilever retaining wall subjected to dynamic loadings. The mathematical model contains three constraints including geotechnical, structural, and geometrical considerations. As the previous FPA applications revealed the efficiency of this method for retaining wall problems, some modifications have been made on the existing method when dynamic loadings are included. To reveal the performance of IFPA, sensitivity analyses are carried out using a variety of soil parameters. Also, tuning of IFPA parameters is illustrated with two different retaining wall case studies reported in the literature. The results indicate that IFPA is a viable alternative to the well-known metaheuristics. This study also reveals that there is space for further improvements to cover wider range of geotechnical engineering-related optimization problems.

Having a wide range of applications in civil engineering practice, Mechanically Stabilized Earth Walls (MSEWs) are regarded as efficient and reliable alternatives to the conventional retaining structure types. As is often the case in engineering, the performance and cost-effectiveness of these structures rely on robust design strategies, which must be proficient to yield optimal solutions in multimodal spaces. While the inherent characteristics of engineering problems often render the design a challenging task, metaheuristic algorithms are suitable options provided that problem-specific considerations and modifications are implemented. In this regard, Cuckoo Search (CS) and its variants are successful in many engineering applications. In the present study, CS is adopted to optimize the reinforcement type, length, and layout of MSEWs and a hybrid CS (HCSDE) variant based on Differential Evolution formulation is developed to further enhance the search capability of the algorithm. The proposed algorithm is applied to various MSEW design benchmarks and comparatively evaluated with respect to well-established methods such as Genetic Algorithm and Particle Swarm Optimization. The results of the study indicate that CS is competent for the problem and the capability of the algorithm can be further enhanced through the proposed adaptations in HCSDE. The improved solutions of HCSDE compared to the other optimization methods highlight the proposed formulation as a promising algorithm for practical implementations.

This study introduces a backcalculation algorithm to estimate the material properties of the full-depth asphalt pavements. The proposed algorithm, namely CS-ANN, uses an Artificial Neural Network (ANN) based forward response engine, which is developed from the solutions of nonlinear finite element analysis to calculate the deflections mathematically. In the backward phase of the method, Cuckoo Search (CS), is utilized to search for the layer moduli values. The performance of the proposed method is investigated by analyzing the synthetically calculated deflections by a finite element based software and deflection data obtained from the field. In addition, to evaluate the searching capability of CS, optimization algorithms widely used in pavement backcalculation; Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Gravitational Search Algorithm (GSA), are employed for comparison purposes. Obtained results indicate that the proposed backcalculation approach is able to determine stiffness-related layer properties in an accurate and rapid manner. In addition, CS presents a promising performance in reaching the optimum solutions that are better than GA, PSO, and GSA.

The use of biological means for ground improvement have become popular, which generally works through the process called microbially-induced calcium carbonate precipitation (MICP). Many studies ind...

In the past two decades, there has been an ongoing trend to design retaining walls in an optimal way rather than the conventional trial and-error approach. In this study, reinforced concrete cantilever retaining wall is optimized using the Cuckoo Search (CS) algorithm, a metaheuristic swarm-based method that imitates the reproductive behavior of cuckoo birds. To obtain the optimal solution, design requirements are expressed as constraints to overcome violated solutions. Together with a mathematical definition of the objective function, three constraint groups are used to represent the geotechnical, structural, and geometrical design considerations. In addition, an objective-based design approach is introduced to optimize the cost and weight objective functions simultaneously. The performance of the CS is proved through its application on cantilever retaining walls, where two numerical examples are solved in terms of both cost and the weight of the walls. The results indicate that the CS is a viable solution for the optimum design of retaining walls.

The NEAM Tsunami Hazard Model 2018 (NEAMTHM18) is a probabilistic hazard model for tsunamis generated by earthquakes. It covers the coastlines of the North-East Atlantic, the Mediterranean, and connected Seas (NEAM). In this online data product, the hazard results are provided by hazard curves calculated at 2,343 Points of Interest (POI), distributed in the North-East Atlantic (1,076 POIs), the Mediterranean Sea (1,130 POIs), and the Black Sea (137 POIs) at an average spacing of ~20 km. For each POI, hazard curves are given for the mean, 2nd, 16th, 50th, 84th, and 98th percentiles. Maps derived from hazard curves are Probability maps for Maximum Inundation Heights (MIH) of 1, 2, 5, 10, 20 meters; Hazard maps for Average Return Periods (ARP) of 500, 1,000, 2,500, 5,000, 10,000 years. For each map, precalculated displays are provided for the mean, the 16th percentile, and the 84th percentile. All data are also made accessible through an interactive web mapper and through Open Geospatial Consortium standard protocols. The model was prepared in the framework of the European Project TSUMAPS-NEAM (http://www.tsumaps-neam.eu/) funded by the mechanism of the European Civil Protection and Humanitarian Aid Operations (grant no. ECHO/SUB/2015/718568/PREV26)., Co-financed by the European-Union Civil Protection Mechanism Agreement Number: ECHO/SUB/2015/718568/PREV26

Development of a lean and automated progress tracking system is one of the most challenging subjects in the construction industry. Since the current methods depend on individual and manual inspections of the site, they may have various problems. For instance, results include a natural bias since field personnel makes interpretations depending on their educational and expertise level. From the practical point of view, manual data collection and their comparison with construction drawings and schedules are inefficient as it takes too much time. With the recent advancement of Unmanned Aerial Vehicles (UAVs) into the market and inclusion of lean project management principles, project management studies are inclined towards the digital and lean approaches. Within this perspective, the objective of this study is to reveal the use of UAVs as a lean project management tool. This paper indicates merely that UAVs have the potential for such a purpose.

Stability analyses of slopes have been a challenge for engineers, requiring development of complex numerical models to assess the risk levels and potential hazards. The numerical models involve combination of analysis methods and integrated optimization approaches, which generally induce intense engineering calculations with upscale time complexity. To obtain good and quick solutions, a robust optimization algorithm is necessary, leading to an efficient and reliable stability analysis framework. Within this context, various optimization techniques involving deterministic and metaheuristic approaches were proposed in the past decades. The proposed methods often suffer from convergence issues have time deficiencies, which highlights a necessity of development of an effective optimization algorithm. In this study, a modified version of Differential Evolution (DE) algorithm named Elitist Differential Evolution (EDE) is proposed to solve slope stability analysis problems. To develop a complete analysis framework, EDE is integrated with a non-circular failure surface generation method and limit equilibrium based stability analysis techniques. Its performance is compared with other optimization algorithms such as conventional DE, Particle Swarm Optimization and Grey Wolf Optimizer using benchmark problems reported in the literature. The experiments demonstrate that EDE greatly improves the results of other alternatives, validating the applicability of the algorithm to slope stability analysis. Furthermore, statistical performance of EDE has become prominent in the experiments, which further emphasizes its robustness.

One approach to model the high-frequency attenuation of spectral amplitudes of S-waves is to express the observed exponential decay in terms of Kappa ( κ ) factor [1] . Kappa is a significant parameter used for identifying the high-frequency attenuation behavior of ground motions as well as one of the key parameters for stochastic strong ground motion simulation method. As of now, there is not a systematic investigation of the Kappa parameter based on the recently-compiled Turkish ground motions. In this study, we examine a strong ground motion dataset from Northwestern Turkey with varying source properties, site classes and epicentral distances. We manually compute κ from the S -wave portion of each record and study both horizontal and vertical kappa values. We use traditional regression techniques to describe the (potential) relationships between kappa and selected independent variables such as the site class, distance from the source or magnitude of the event. A linear effect of magnitude on kappa is not found statistically significant for the database studied herein. We express the initial findings of a regional κ model for Northwestern Turkey as a function of site class and epicentral distances. Single station analyses at selected sites confirm the regional model. Finally, we present stochastic strong motion simulations of past events in the region using the proposed kappa model. Regardless of the magnitude, source-to-site distance and local site conditions at the stations, the high-frequency spectral decay is simulated effectively at all stations considered.

The city of Adapazari, which is mostly located on a deep alluvial basin in the near field of the ruptured fault, experienced substantial damage during the 17 August 1999 Kocaeli (Izmit), Turkey earthquake (Mw 7.4). Besides over 2800 collapsed and heavily damaged buildings in the city, numerous cases of displacements of various forms and levels occurred at the building foundations. The undrained shear and deformation behavior of silt–clay mixtures encountered at the sites with significant foundation displacements are investigated through a series of standard and rapid monotonic, and stress-controlled cyclic triaxial tests conducted over anisotropically consolidated natural soil samples. Test results show that, while the dynamic stiffness and strength of these soils do not degrade under means of loading comparable to that of 17 August earthquake, their plastic strain accumulation characteristics critically depend on the mode of loading as well as the relative levels of applied load with regard to the monotonic strength. Implications of these results are consistent with the factors observed to be effective in the occurrence and severity of foundation displacements throughout the city.

Pavement condition assessment is an essential piece of modern pavement management systems as rehabilitation strategies are planned based upon its outcomes. For proper evaluation of existing pavements, they must be continuously and effectively monitored using practical means. Conventionally, truck-based pavement monitoring systems have been in-use in assessing the remaining life of in-service pavements. Although such systems produce accurate results, their use can be expensive and data processing can be time consuming, which make them infeasible considering the demand for quick pavement evaluation. To overcome such problems, Unmanned Aerial Vehicles (UAVs) can be used as an alternative as they are relatively cheaper and easier-to-use. In this study, we propose a UAV based pavement crack identification system for monitoring rigid pavements' existing conditions. The system consists of recently introduced image processing algorithms used together with conventional machine learning techniques, both of which are used to perform detection of cracks on rigid pavements' surface and their classification. Through image processing, the distinct features of labelled crack bodies are first obtained from the UAV based images and then used for training of a Support Vector Machine (SVM) model. The performance of the developed SVM model was assessed with a field study performed along a rigid pavement exposed to low traffic and serious temperature changes. Available cracks were classified using the UAV based system and obtained results indicate it ensures a good alternative solution for pavement monitoring applications.

This paper describes an advanced pavement analysis application based on a soft computing methodology, SOFTSYS, recently developed at the University of Illinois for identifying pavement geometry and layer properties through various nondestructive testing methods. Specifically, it is capable of backcalculating pavement layer properties as well as determining thickness of full depth asphalt pavements using results of the Falling Weight Deflectometer (FWD) test. SOFTSYS uses a combination of Genetic Algorithms and Artificial Neural Networks together with a structural pavement analysis model based on the Finite Element Method to obtain accurate and reliable backcalculation solutions. In this paper, basics of SOFTSYS, its numerical schemes and computer code implementation are described first within the scope of soft computing methods. An illustrative example of pavement backcalculation is then presented using the synthetic FWD field data obtained from nonlinear ILLI-PAVE finite element program representing typical fulldepth asphalt pavements.

Artificial neural networks (ANNs) were used in this paper to develop an improved and more accurate approach for backcalculating pavement layer moduli from Falling Weight Deflectometer (FWD) test data collected in the field. For this purpose, critical pavement responses were computed by the ILLI-PAVE finite element program widely used and proven to be effective for the analysis of flexible pavement systems with the considerations of the nonlinear aggregate base and subgrade soil behavior. The ANN models were then trained to map the nonlinear functional relationships between the FWD deflections, layer properties, and the critical pavement responses.