Recent tendencies in the use of optimization techniques in geotechnics:a review

The use of optimization methods in geotechnics dates back to the 1950s. They were used in slope stability analysis (Bishop) and evolved to a wide range of applications in ground engineering. We present here a non-exhaustive review of recent publications that relate to the use of different optimization techniques in geotechnical engineering. Metaheuristic methods are present in almost all the problems in geotechnics that deal with optimization. In a number of cases, they are used as single techniques, in others in combination with other approaches, and in a number of situations as hybrids. Different results are discussed showing the advantages and issues of the techniques used. Computational time is one of the issues, as well as the assumptions those methods are based on. The article can be read as an update regarding the recent tendencies in the use of optimization techniques in geotechnics

Development of a sustainable groundwater management strategy and sequential compliance monitoring to control saltwater intrusion in coastal aquifers

The coastal areas of the world are characterized by high population densities, an abundance of food, and increased economic activities. These increasing human settlements, subsequent increases in agricultural developments and economic activities demand an increasing amount quantity of freshwater supplies to different sectors. Groundwater in coastal aquifers is one of the most important sources of freshwater supplies. Over exploitation of this coastal groundwater resource results in seawater intrusion and subsequent deterioration of groundwater quality in coastal aquifers. In addition, climate change induced sea level rise, in combination with the effect of excessive groundwater extraction, can accelerate the seawater intrusion. Adequate supply of good quality water to different sectors in coastal areas can be ensured by adoption of a proper management strategy for groundwater extraction. Optimal use of the coastal groundwater resource is one of the best management options, which can be achieved by employing a properly developed optimal groundwater extraction strategy. Coupled simulation-optimization (S-O) approaches are essential tools to obtain the optimal groundwater extraction patterns. This study proposes approaches for developing multiple objective management of coastal aquifers with the aid of barrier extraction wells as hydraulic control measure of saltwater intrusion in multilayered coastal aquifer systems. Therefore, two conflicting objectives of management policy are considered in this research, i.e. maximizing total groundwater extraction for advantageous purposes, and minimizing the total amount of water abstraction from barrier extraction wells. The study also proposes an adaptive management strategy for coastal aquifers by developing a three-dimensional (3-D) monitoring network design. The performance of the proposed methodologies is evaluated by using both an illustrative multilayered coastal aquifer system and a real life coastal aquifer study area. Coupled S-O approach is used as the basic tool to develop a saltwater intrusion management model to obtain the optimal groundwater extraction rates from a combination of feasible solutions on the Pareto optimal front. Simulation of saltwater intrusion processes requires solution of density dependent coupled flow and solute transport numerical simulation models that are computationally intensive. Therefore, computational efficiency in the coupled S-O approach is achieved by using an approximate emulator of the accompanying physical processes of coastal aquifers. These emulators, often known as surrogate models or meta-models, can replace the computationally intensive numerical simulation model in a coupled S-O approach for achieving computational efficiency. A number of meta-models have been developed and compared in this study for integration with the optimization algorithm in order to develop saltwater intrusion management model. Fuzzy Inference System (FIS), Adaptive Neuro Fuzzy Inference System (ANFIS), Multivariate Adaptive Regression Spline (MARS), and Gaussian Process Regression (GPR) based meta-models are developed in the present study for approximating coastal aquifer responses to groundwater extraction. Properly trained and tested meta-models are integrated with a Controlled Elitist Multiple Objective Genetic Algorithm (CEMOGA) within a coupled S-O approach. In each iteration of the optimization algorithm, the meta-models are used to compute the corresponding salinity concentrations for a set of candidate pumping patterns generated by the optimization algorithm. Upon convergence, the non-dominated global optimal solutions are obtained as the Pareto optimal front, which represents a trade-off between the two conflicting objectives of the pumping management problem. It is observed from the solutions of the meta-model based coupled S-O approach that the considered meta-models are capable of producing a Pareto optimal set of solutions quite accurately. However, each meta-modelling approach has distinct advantages over the others when utilized within the integrated S-O approach. Uncertainties in estimating complex flow and solute transport processes in coastal aquifers demand incorporation of the uncertainties related to some of the model parameters. Multidimensional heterogeneity of aquifer properties such as hydraulic conductivity, compressibility, and bulk density are considered as major sources of uncertainty in groundwater modelling system. Other sources of uncertainty are associated with spatial and temporal variability of hydrologic as well as human interventions, e.g. aquifer recharge and transient groundwater extraction patterns. Different realizations of these uncertain model parameters are obtained from different statistical distributions. FIS based meta-models are advanced to a Genetic Algorithm (GA) tuned hybrid FIS model (GA-FIS), to emulate physical processes of coastal aquifers and to evaluate responses of the coastal aquifers to groundwater extraction under groundwater parameter uncertainty. GA is used to tune the FIS parameters in order to obtain the optimal FIS structure. The GA-FIS models thus obtained are linked externally to the CEMOGA in order to derive an optimal pumping management strategy using the coupled S-O approach. The evaluation results show that the proposed saltwater intrusion management model is able to derive reliable optimal groundwater extraction strategies to control saltwater intrusion for the illustrative multilayered coastal aquifer system. The optimal management strategies obtained as solutions of GA-FIS based management models are shown to be reliable and accurate within the specified ranges of values for different realizations of uncertain groundwater parameters. One of the major concerns of the meta-model based integrated S-O approach is the uncertainty associated with the meta-model predictions. These prediction uncertainties, if not addressed properly, may propagate to the optimization procedures, and may deteriorate the optimality of the solutions. A standalone meta-model, when used within an optimal management model, may result in the optimization routine producing actually suboptimal solutions that may undermine the optimality of the groundwater extraction strategies. Therefore, this study proposes an ensemble approach to address the prediction uncertainties of meta-models. Ensemble is an approach to assimilate multiple similar or different algorithms or base learners (emulators). The basic idea of ensemble lies in developing a more reliable and robust prediction tool that incorporates each individual emulator's unique characteristic in order to predict future scenarios. Each individual member of the ensemble contains different input -output mapping functions. Based on their own mapping functions, these individual emulators provide varied predictions on the response variable. Therefore, the combined prediction of the ensemble is likely to be less biased and more robust, reliable, and accurate than that of any of the individual members of the ensemble. Performance of the ensemble meta-models is evaluated using an illustrative coastal aquifer study area. The results indicate that the meta-model based ensemble modelling approach is able to provide reliable solutions for a multilayered coastal aquifer management problem. Relative sea level rise, providing an additional saline water head at the seaside, has a significant impact on an increase in the salinization process of the coastal aquifers. Although excessive groundwater withdrawal is considered as the major cause of saltwater intrusion, relative sea level rise, in combination with the effect of excessive groundwater pumping, can exacerbate the already vulnerable coastal aquifers. This study incorporates the effects of relative sea level rise on the optimized groundwater extraction values for the specified management period. Variation of water concentrations in the tidal river and seasonal fluctuation of river water stage are also incorporated. Three meta-models are developed from the solution results of the numerical simulation model that simulates the coupled flow and solute transport processes in a coastal aquifer system. The results reveal that the proposed meta-models are capable of predicting density dependent coupled flow and solute transport patterns quite accurately. Based on the comparison results, the best meta-model is selected as a computationally cheap substitute of the simulation model in the coupled S-O based saltwater intrusion management model. The performance of the proposed methodology is evaluated for an illustrative multilayered coastal aquifer system in which the effect of climate change induced sea level rise is incorporated for the specified management period. The results show that the proposed saltwater intrusion management model provides acceptable, accurate, and reliable solutions while significantly improving computational efficiency in the coupled S-O methodology. The success of the developed management strategy largely depends on how accurately the prescribed management policy is implemented in real life situations. The actual implementation of a prescribed management strategy often differs from the prescribed planned strategy due to various uncertainties in predicting the consequences, as well as practical constraints, including noncompliance with the prescribed strategy. This results in actual consequences of a management strategy differing from the intended results. To bring the management consequences closer to the intended results, adaptive management strategies can be sequentially modified at different stages of the management horizon using feedback measurements from a deigned monitoring network. This feedback information can be the actual spatial and temporal concentrations resulting from the implementation of actual management strategy. Therefore, field-scale compliance of the developed coastal aquifer management strategy is a crucial aspect of an optimally designed groundwater extraction policy. A 3-D compliance monitoring network design methodology is proposed in this study in order to develop an adaptive and sequentially modified management policy, which aims to improve optimal and justifiable use of groundwater resources in coastal aquifers. In the first step, an ensemble meta-model based multiple objective prescriptive model is developed using a coupled S-O approach in order to derive a set of Pareto optimal groundwater extraction strategies. Prediction uncertainty of meta-models is addressed by utilizing a weighted average ensemble using Set Pair Analysis. In the second step, a monitoring network is designed for evaluating the compliance of the implemented strategies with the prescribed management goals due to possible uncertainties associated with field-scale application of the proposed management policy. Optimal monitoring locations are obtained by maximizing Shannon's entropy between the saltwater concentrations at the selected potential locations. Performance of the proposed 3-D sequential compliance monitoring network design is assessed for an illustrative multilayered coastal aquifer study area. The performance evaluations show that sequential improvements of optimal management strategy are possible by utilizing saltwater concentrations measurements at the proposed optimal compliance monitoring locations. The integrated S-O approach is used to develop a saltwater intrusion management model for a real world coastal aquifer system in the Barguna district of southern Bangladesh. The aquifer processes are simulated by using a 3-D finite element based combined flow and solute transport numerical code. The modelling and management of seawater intrusion processes are performed based on very limited hydrogeological data. The model is calibrated with respect to hydraulic heads for a period of five years from April 2010 to April 2014. The calibrated model is validated for the next three-year period from April 2015 to April 2017. The calibrated and partially validated model is then used within the integrated S-O approach to develop optimal groundwater abstraction patterns to control saltwater intrusion in the study area. Computational efficiency of the management model is achieved by using a MARS based meta-model approximately emulating the combined flow and solute transport processes of the study area. This limited evaluation demonstrates that a planned transient groundwater abstraction strategy, acquired as solution results of a meta-model based integrated S-O approach, is a useful management strategy for optimized water abstraction and saltwater intrusion control. This study shows the capability of the MARS meta-model based integrated S-O approach to solve real-life complex management problems in an efficient manner

The Application of Hydraulic and Sediment Transport Models in Fluvial Geomorphology

After publishing the famous “Fluvial Processes in Geomorphology” in the early 1960s, the work of Luna Leopold, Gordon Wolman, and John Miller became a key for opening the door to understanding rivers and streams. They first illustrated the problem to geomorphologists and geographers. Later, Chang, in his “Fluvial Processes in River Engineering”, provided a basis for engineers, showing this group of professionals how to deal with rivers and how to understand them. Since then, more informative studies have been published. Many of the authors started to combine fluvial geomorphology knowledge and river engineering needs, such as “Tools in Fluvial Geomorphology” by G. Mathias Kondolf and Hervé Piégay, or focused more on river engineering tasks, such as “Stream Restoration in Dynamic Fluvial Systems: Scientific Approaches” by Andrew Simon, Sean Bennett, and Janine Castro. Finally, Luna Leopold summarized river and stream morphologies in the beautiful “A view of the river”. It appears that we continue to explore this subject in the right direction. We better understand rivers and streams, and as engineers and fluvial geomorphologists, we can establish tools to help bring rivers alive. However, there is still a hunger for more scientific tools that we could use to further understand rivers and to support the development of healthy streams and rivers with high biodiversity in the present world, which has started to face water scarcity

Development and Applications of Self-learning Simulation in Finite Element Analysis

Numerical analysis such as the finite element analysis (FEA) have been widely used to solve many engineering problems. Constitutive modelling is an important component of any numerical analysis and is used to describe the material behaviour. The accuracy and reliability of numerical analysis is greatly reliant on the constitutive model that is integrated in the finite element code. In recent years, data mining techniques such as artificial neural network (ANN), genetic programming (GP) and evolutionary polynomial regression (EPR) have been employed as alternative approach to the conventional constitutive modelling. In particular, EPR offers great advantages over other data mining techniques. However, these techniques require a large database to learn and extract the material behaviour. On the other hand, the link between laboratory or field tests and numerical analysis is still weak and more investigation is needed to improve the way that they matched each other. Training a data mining technique within the self-learning simulation framework is currently considered as one of the solutions that can be utilised to accurately represent the actual material behaviour. In this thesis an EPR based machine learning technique is utilised in the heart of the self-learning framework with an automation process which is coded in MATLAB environment. The methodology is applied to simulate different material behaviour in a number of structural and geotechnical applications. Two training strategies are used to train the EPR in the developed framework, total stress-strain and incremental stress-strain strategies. The results show that integrating EPR based models in the framework allows to learn the material response during the self-learning process and provide accurate predictions to the actual behaviour. Moreover, for the first time, the behaviour of a complex material, frozen soil, is modelled based on the EPR approach. The results of the EPR model predictions are compared with the actual data and it is shown that the proposed model can capture and reproduce the behaviour of the frozen soil with a very high accuracy. The developed EPR based self-learning methodology presents a unified approach to material modelling that can also help the user to gain a deeper insight into the behaviour of the materials. The methodology is generic and can be extended to modelling different engineering materials

Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management

Seepage criteria based optimal design of water retaining structures with reliability quantification utilizing surrogate model linked simulation-optimization approach

The safety of hydraulic water retaining structures (HWRS) is an important issue as many instances of HWRS failure have been reported. Failure of HWRS may lead to catastrophic events, especially those associated with seepage failures. Therefore, seepage safety factors recommended for HWRS design are generally very conservative. These safety factors have been developed based on approximation calculations, unreliable assumptions, and ideal experimental conditions, which are rarely replicated in real field situations. However, with the development of the numerical methods, and high speed processors, more accurate seepage analysis has become possible, even for complex flow domains, different scenarios of boundary conditions, and varied hydraulic conductivity. On the other hand, because construction of HWRS requires a significant amount of construction material and engineering effort, the construction cost efficiency of HWRS is an issue that must be considered in design of HWRS. This study aims to determine the minimum cost design of HWRS constructed on permeable soils, incorporating numerical solutions of a seepage system related to HWRS, utilizing linked a simulation–optimization (S-O) model. Due to the complexity and inefficacy of directly linking a simulation model to the optimization model, the numerical simulation model was replaced by trained surrogate models. These surrogate models can be trained based on numerically simulated data sets. Therefore, trained surrogate models expeditiously and accurately provide predicted responses relating to seepage characteristics pertaining to HWRS. The optimization model based on the linked S-O technique incorporated different safety factors and hydraulic structure design requirements as constraints. The majority of these constraints and objective function(s) were affected by the responses of predicted seepage characteristics based on the developed surrogate models. To improve the safety of HWRS design, the effect of non-homogenous and anisotropic hydraulic conductivity were incorporated in the S-O model. Obtained solution results demonstrated that considering stratification of the flow domain due to different hydraulic conductivity values or anisotropic ratios can significantly change the optimum design of HWRS. Low hydraulic conductivity and anisotropic ratios resulted in more critical seepage characteristics. Consequently, the minimum construction cost increased due to an increase of dimensions of involved seepage protection design variables. Furthermore, uncertainty in estimating hydraulic conductivity is incorporated in the S-O model. The reliability based optimal design (RBOD) framework based on the multi-realization optimization technique was implemented using the S-O model. The uncertainty in seepage quantities due to uncertainty of hydraulic conductivity was represented using many stochastic ensemble surrogate models. Each ensemble model included many surrogate models trained in utilizing input– output data sets simulated with different scenarios of hydraulic conductivity drawn from diverse random fields based on different log-normal distributions. Obtained results of this approach demonstrated substantial consequences of considering uncertainty in hydraulic conductivity. Also, the deterministic safety factors, especially for those pertaining to the exit gradient, were insufficient to provide prescribed safety in the long term. Although surrogate models are utilized in S-O approaches, each run of the S-O model takes a long time as developed S-O models are applied to complex and large scale problems. Hence, efficiency of the S-O model was a key factor to successfully implement the methodology. Three main techniques were utilized to increase the efficiency of the S-O technique: using parallel computing, utilizing nested function technique, and using a vectorised formulation system. These strategies substantially boosted efficiency of implementing the S-O model. The S-O models were implemented for many hypothetical scenarios for different purposes. In general, results demonstrated that optimum design of the seepage protection system relating to HWRS design must include two end cut-offs with an apron between them. The dimensions of these components were augmented with an increase of upstream water head, and reduction of anisotropic ratios or hydraulic conductivity value. The main role of the downstream cut-off was to decrease the actual exit gradient value. This impact is more pronounced if the inclination angle of the cut-off is toward the downstream side (>90 degrees). The role of the upstream cut-off was to decrease uplift pressure values on the HWRS base. Consequently, this partially contributed to decreasing the exit gradient value. The effect of the upstream cut-off in reducing the uplift pressure was more when the inclination angle was toward the upstream side (<90 degrees). Moreover, the apron (floor) width helped to increase the stability of HWRS. This variable provided the required weight to improve HWRS resistance to external hydraulic forces and to uplift pressure. Incorporating the weight of water (hydrostatic pressure) at the upstream side in counterbalancing momentum and hydraulic forces showed improvement in the safety of the HWRS. Also, all conditions and safety factors pertaining to HWRS design were satisfied. The exit gradient safety factor was the most important critical factor affecting optimum design as obtained optimum solutions satisfied the minimum permissible values of the exit gradient safety factor, i.e., at the minimum permissible value. Also, the eccentric load condition played a crucial role in resulting optimum solutions. Finally, applying the S-O model to obtain reliable and safe design of HWRS at minimum cost was successfully implemented for performance evaluation purposes. This technique may be extended to incorporate more complex scenarios in HWRS design where the impact of dynamic and seismic load could be incorporated. The effect of unsteady state seepage system could be another interesting direction for future studies. Further, incorporating more sources of the uncertainty associated with design parameters could achieve a more accurate estimation of actual safety for the HWRS design

Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems

The Latest Scientific Problems Related to the Implementation and Diagnostics of Construction Objects

This book contains publications related to the special topic entitled: "The Latest Scientific Problems Related to the Implementation and Diagnostics of Construction Objects". Construction is a sector of the economy that is characterized by a very high variability of execution conditions and a large variety of building structures. In a period of very rapid economic development, this high variability and diversity generates many new scientific problems that must be solved in order to further improve the quality of production, as well as to reduce the cost and time of construction. The purpose of the issue is to present and discuss the results of the latest research in the broad field of construction engineering, particularly concerning: modification of the composition of construction materials using various micro- and nanomaterials, by-products or wastes; modern methods of controlling construction processes; methods of planning and effective management in construction, as well as methods of diagnosing construction objects. The articles published in this issue deal with theoretical, experimental, applied and modeling research conducted worldwide in the above-mentioned scientific areas

World Multidisciplinary Civil Engineering- Architecture- Urban Planning symposium

We would like to express our sincere gratitude to all 900+ submissions by 600+ participants of WMCAUS 2018 from 60+ different countries all over the world for their interests and contributions in WMCAUS 2018. We wish you enjoy the World Multidisciplinary Civil Engineering-Architecture-Urban Planning Symposium – WMCAUS 2018 and have a pleasant stay in the city of romance Prague. We hope to see you again during next event WMCAUS 2019 which will be held in Prague (Czech Republic) approximately in the similar period