Hybrid Genetic Programming with Local Search Operators for Dynamic Force Identification

In this paper, based on the Darwinian and Lamarckian evolution theories, three hybrid genetic programming (GP) algorithms integrated with different local search operators (LSOs) are implemented to improve the search efficiency of the standard GP. These three LSOs are the genetic algorithm, the linear bisection search, and the Hooke and Jeeves method. A simple encoding method is presented to encode the GP individuals into the expressions that can be recognized by the different LSOs. The implemented hybrid GP algorithms are applied to identify the excitation force acting on the structures from the measured structural response, which is an important type of inverse problem in structural dynamics. Illustrative examples of a frame structure and a multistory building structure demonstrate that, compared with the standard GP, the hybrid GP algorithms have higher search efficiency which can be used as alternate global search and optimization tools for other engineering problem solving.     

Optimization of Pile Groups Using Hybrid Genetic Algorithms

This paper presents an automated optimal design method using a hybrid genetic algorithm for pile group foundation design. The design process is a sizing and topology optimization for pile foundations. The objective is to minimize the material volume of the foundation taking the configuration, number, and cross-sectional dimensions of the piles as well as the thickness of the pile cap as design variables. A local search operator by the fully stressed design (FSD) approach is incorporated into a genetic algorithm (GA) to tackle two major shortcomings of a GA, namely, large computation effort in searching the optimum design and poor local search capability. The effectiveness and capability of the proposed algorithm are first illustrated by a five by five pile group subjected to different loading conditions. The proposed optimization algorithm is then applied to a large-scale foundation project to demonstrate the practicality of the algorithm. The proposed hybrid genetic algorithm successfully minimizes the volume of material consumption and the result matches the engineering expectation. The FSD operator has great improvement on both design quality and convergence rate. Challenges encountered in the application of optimization techniques to design of pile groups consisting of hundreds of piles are discussed.     

Efficient Parallel Implementation of Hybrid Optimization Approaches for Solving Groundwater Inverse Problems

Inverse problems that are constrained by large-scale partial differential equation (PDE) systems demand very large computational resources. Solutions to these problems generally require the solution of a large number of complex PDE systems. Three-dimensional groundwater inverse problems fall under this category. In this paper, we describe the implementation of a parallel simulation-optimization framework for solving PDE-based inverse problems and demonstrate it for the solution of groundwater contaminant source release history reconstruction problems that are of practical importance. The optimization component employs several optimization algorithms, including genetic algorithms (GAs) and several local search (LS) approaches that can be used in a hybrid mode. This hybrid GA-LS optimizer is used to drive a parallel finite-element (FEM) groundwater forward transport simulator. Parallelism is exploited within the transport simulator (fine grained parallelism) as well as the optimizer (coarse grained parallelism) through the exclusive use of the Message Passing Interface (MPI) communication library. Algorithmic and parallel performance results are presented for an IBM SP3 supercomputer. Simulation and performance results presented in this paper illustrate that an effective combination of efficient optimization algorithms and parallel computing can enable solution to three-dimensional groundwater inverse problems of a size and complexity not attempted before.     

Global Optimization of Pavement Structural Parameters during Back-Calculation Using Hybrid Shuffled Complex Evolution Algorithm

In this paper, the use of a hybrid evolutionary optimization algorithm is proposed for global optimization of pavement structural parameters through inverse modeling. Shuffled complex evolution (SCE) is a population-based stochastic optimization technique combining the competitive complex evolution with the controlled random search, the implicit clustering, and the complex shuffling. Back-calculation of pavement layer moduli is an ill-posed inverse engineering problem, which involves searching for the optimal combination of pavement layer stiffness solutions in an unsmooth, multimodal, complex search space. SCE is especially considered a robust and efficient approach for global optimization of multimodal functions. A desirable characteristic of the SCE algorithm is that it uses information about the nature of the response surface, extracted using the deterministic Simplex geometric shape, to direct the search into regions with higher posterior probability. The hybrid back-calculation system described in this paper combines the robustness of the SCE in global optimization with the computational efficiency of neural networks and advanced pavement system characterization offered by employing finite-element models. This is the first time the SCE approach is applied to real-time nondestructive evaluation of pavement systems required in the routine maintenance and rehabilitation activities for sustainable transportation infrastructure.     

KB-GA-Based Hybrid System for Layout Planning of Multistory Buildings

Layout planning of multistory buildings requires multidisciplinary participation in the decision-making process, which is governed by intuition, creativity, and experience of the designers, and available information. Optimization techniques are rarely applied to the layout planning stage. The present study aims at a hybrid approach by combining knowledge based system and a genetic algorithm (GA)-based optimization technique by employing them for suitable tasks, in order to take specific advantages of respective techniques, i.e., the domain dependent part is isolated and developed as a knowledge-based (KB) system, while the knowledge lean part is handled by the adaptive search techniques. The main issue to be addressed while developing an integrated system is to identify the role of individual approaches and the interactions among them. This paper outlines this concept through an integrated hybrid system GALOP for optimal layout planning of multistory office buildings. A multiobjective GA-based optimization technique produces a set of optimal solutions, from which a designer can select the final design.     

Hybrid Time-Cost Optimization of Nonserial Repetitive Construction Projects

Time-cost trade-off analysis represents a challenging task because the activity duration and cost have uncertainty associated with them, which should be considered when performing schedule optimization. This study proposes a hybrid technique that combines genetic algorithms (GAs) with dynamic programming to solve construction projects time-cost trade-off problems under uncertainty. The technique is formulated to apply to project schedules with repetitive nonserial subprojects that are common in the construction industry such as multiunit housing projects and retail network development projects. A generalized mathematical model is derived to account for factors affecting cost and duration relationships at both the activity and project levels. First, a genetic algorithm is utilized to find optimum and near optimum solutions from the complicated hyperplane formed by the coding system. Then, a dynamic programming procedure is utilized to search the vicinity of each of the near optima found by the GA, and converges on the global optima. The entire optimization process is conducted using a custom developed computer code. The validation and implementation of the proposed techniques is done over three axes. Mathematical correctness is validated through function optimization of test functions with known optima. Applicability to scheduling problems is validated through optimization of a 14 activity miniproject found in the literature for results comparison. Finally implementation to a case study is done over a gas station development program to produce optimum schedules and corresponding trade-off curves. Results show that genetic algorithms can be integrated with dynamic programming techniques to provide an effective means of solving for optimal project schedules in an enhanced realistic approach.     

Optimal Design of a Stable Trapezoidal Channel Section Using Hybrid Optimization Techniques

A cost effective channel section for a specified flow rate, roughness coefficients, longitudinal slope, and various cost parameters can be determined using an optimization technique. However, the derived optimal channel section may not be feasible for construction because of in situ conditions. The local soil conditions may not support the optimal side slope of the channel and if constructed, the slope may fail. It is therefore necessary to also incorporate the criteria for side slope stability in designing an optimal open channel section. In this paper, a new methodology has been developed to design a stable and optimal channel section using hybrid optimization techniques. A genetic algorithm based optimization model is developed initially to determine the factor of safety of a channel slope for given soil parameters. This optimization model is then externally linked with a separate sequential quadratic programming based optimization model to evaluate the parameters of the stable and optimal channel section. Solution for various example problems incorporating different soil parameters are illustrated to demonstrate the applicability of the developed methodology.     

Remediation of TCE-Contaminated Groundwater in a Sandy Aquifer Using Pulsed Air Sparging: Laboratory and Numerical Studies

The objective of this study was to investigate, through laboratory and numerical investigations, the effectiveness of a pulsed air sparging system for remediation of groundwater contaminated with trichloroethylene (TCE) in a sandy aquifer. In laboratory experiments, air was pulsed into TCE source zone on a daily basis in order to remediate TCE-contaminated groundwater. Most dissolved TCE was removed at the end of experiments although its concentrations fluctuated due to the air pulsing. The measured gaseous phase TCE concentration increased whereas the aqueous phase TCE concentration decreased during air sparging pulses. Experimental data were assessed by using a numerical code STOMP (subsurface transport over multiphases) with some modification based on the TCE dissolution kinetics. The unmeasured residual TCE mass was predicted through numerical simulations. Results show that aqueous concentrations for TCE are still much higher than the maximum contaminant level in spite of successful removal of 95% of residual TCE. It may imply that it would be more appropriate to apply air sparging combined with other remediation technologies such as bioremediation for remediation of TCE-contaminated groundwater.     

Hybrid Models of Neural Networks and Genetic Algorithms for Predicting Preliminary Cost Estimates

This technical note applies hybrid models of neural networks (NN) and genetic algorithms (GA) to cost estimation of residential buildings to predict preliminary cost estimates. Data used in the study are for residential buildings constructed from 1997 to 2000 in Seoul, Korea. These are used in training each model and evaluating its performance. The models applied were Model I, which determines each parameter of a back-propagation network by a trial-and-error process; Model II, which determines each parameter of a back-propagation network by GAs; and Model III, which trains weights of NNs using genetic algorithms. The research revealed that optimizing each parameter of back-propagation networks using GAs is most effective in estimating the preliminary costs of residential buildings. Therefore, GAs may help estimators overcome the problem of the lack of adequate rules for determining the parameters of NNs.     

Collision Free Path Planning of Cooperative Crane Manipulators Using Genetic Algorithm

This paper presents a new approach for automated path planning of cooperative crane manipulators using a genetic algorithm (GA). The inverse kinematic problem, i.e., determining the joint angle configuration for the cooperative crane manipulator system in moving the object from pick location to place location, is defined as an optimization problem and solved using GA. For generating the collision-free path, GA with an interference detection algorithm is employed and search is made in the manipulator joint angle space (configuration space). The effectiveness of the proposed approach for automated path planning is demonstrated by comparing the performance of the present approach with the earlier heuristic search proposed by Sivakumar et al. The GA approach finds a near-optimal path with lower path cost and less computational time than earlier heuristic searches.     

Reliability-Constrained Optimization of Water Treatment Plant Design Using Genetic Algorithm

A new approach that links genetic algorithm (GA) as an optimization tool with Monte Carlo simulation (MCS)-based reliability program is presented for reliability-constrained optimal design of water treatment plant (WTP). The reliability of a WTP is defined as the probability that it can achieve the desired effluent water quality standard (WQS). The objective function minimizes the treatment cost, subjected to design and performance constraints, and to achieve desired reliability level for meeting the given effluent WQS. The random variables used to generate the reliability estimates are suspended solids (SS) concentration, flow rate, specific gravity of floc particle, temperature of raw water, sedimentation basin performance index, and model coefficients. The application of GA-MCS approach for design of a WTP is illustrated with a hypothetical case study. The annualized cost of WTP is affected by the number of uncertain parameters included in the analysis, coefficient of variation of uncertain parameters, effluent WQS, and target reliability level. Analysis suggests that higher reliability at lower annual cost of treatment can be achieved by limiting the fluctuation of uncertain parameters. Results show that distribution of effluent SS is also affected by the uncertainty. The suggested GA-MCS approach is efficient to evaluate treatment cost-reliability tradeoff for WTP. Results demonstrate that the combination of GA with MCS is an effective approach to obtain the reliability-constrained optimal/near-optimal solution of WTP design problem consistently.     

Hybrid Soft Computing Approach for Mining of Complex Construction Databases

The paper presents a hybrid soft computing system for mining of complex construction databases. The proposed approach hybridizes soft computing techniques, such as fuzzy logic, artificial neural networks (ANNs), and messy genetic algorithms (mGAs), to form a novel computational method for mining of human understandable knowledge from historical databases. The hybridization combines the merits of explicit knowledge representation of fuzzy logic decision-making systems, learning abilities of ANNs, and global search of mGAs. A hybrid soft computing system (HSCS) is developed for mining complex databases in construction with three characteristics: scarcity, incompleteness, and uncertainty. Real-world construction data repositories are selected to test the capabilities of the proposed HSCS for data-mining under the above-mentioned complex conditions. The testing results show the promising potential of the proposed HSCS for mining of complex databases in construction.     

基于模拟退火多种群遗传算法的C-Mn钢生产优化

模拟退火和多种群进化是改进遗传算法的两种主要手段,文章将这两种方法相结合,建立了基于模拟退火的多种群遗传算法,并将该算法应用于C—Mn钢生产时内控成分和轧制工艺的优化,以满足用户对钢产品的力学性能和化学成分的要求。     

Estimation of Aquifer Parameters from Pumping Test Data by Genetic Algorithm Optimization Technique

Adequate and reliable estimates of aquifer parameters are of utmost importance for proper management of vital groundwater resources. The pumping (aquifer) test is the standard technique for estimating various hydraulic properties of aquifer systems, viz., transmissivity (T), hydraulic conductivity (K), storage coefficient (S), and leakance (L), for which the graphical method is widely used. In the present study, the efficacy of the genetic algorithm (GA) optimization technique is assessed in estimating aquifer parameters from the time-drawdown pumping test data. Computer codes were developed to optimize various aquifer parameters under different hydrogeologic conditions by using the GA technique. Applicability, adequacy, and robustness of the developed codes were tested using 12 sets of the published and unpublished aquifer test data. The aquifer parameters were also estimated by the graphical method using AquiferTest software, and were compared with those obtained by the GA technique. The GA technique yielded significantly low values of the sum of square errors (SSE) for almost all the datasets under study. The results revealed that the GA technique is an efficient and reliable method for estimating various aquifer parameters, especially in the situation when the graphical matching is poor. Also, it was found that because of its inherent characteristics, GA avoids the subjectivity, long computation time and ill-posedness often associated with conventional optimization techniques. Furthermore, the performance evaluation of the developed GA-based computer codes showed that the fitness value (SSE) of the best point in a population reduces with increasing generation number and population size. The analysis of the sensitivity of the parameters during the performance of GA indicated that a unique set of aquifer parameters was obtained for all three aquifer systems. The GA-based computer programs with interactive windows developed in this study are user-friendly and can serve as a teaching and research tool, which could also be useful for practicing hydrologists and hydrogeologists.     

Hail Impact Characteristics of a Hybrid Material by Advanced Analysis Techniques and Testing

The design of an aerospace structure using an off-the-shelf composite would involve increasing the gauge thickness until all the design requirements are met. This can lead to an inefficient design, because excess margins will exist for all properties except the one that determines the gauge. The design of a material can be made practical by creating a hybrid composite consisting of two or more types of fibers or resins, each embellishing a particular trait or function to the material. This paper investigates both high- and low-energy hail impact against a toughened-epoxy, intermediate-modulus, carbon-fiber composite using both experimental and analytical means. The effect of introducing ply-level hybridization by substituting up to 20% of the plies with glass-reinforced plies is considered. It is found that delamination can be reduced by this hybridization, but the benefits are dependent on the impact energy and the test conditions. A computational model based on the peridynamic theory of solid mechanics is used to understand the benefits and trade-offs in hybridization.     

Optimum Design of Welded Steel Plate Girder Bridges Using a Genetic Algorithm with Elitism

The genetic algorithm (GA) is a general optimization technique that has some unique features that are especially suitable for structural engineering problems. This work uses a simple GA with elitism to find the optimum design of welded steel plate girder bridges. The objectives are to minimize the weight and the cost of the girders. Two types of plate-girder bridges are studied: a single-span bridge and a two-equal-span continuous bridge. Bridges with various span lengths, in increments of 20?ft, are investigated; results are tabulated, parametric studies are made, and meaningful conclusions are drawn.     

Interactive Effects of Controlled Drainage and Riparian Buffers on Shallow Groundwater Quality

As a result of recent surface water quality problems in North Carolina, riparian buffers and controlled drainage are being used to reduce the loss of nonpoint source nitrogen from agricultural fields. The effect of controlled drainage and riparian buffers as best management practices to reduce the loss of agricultural nonpoint source nitrogen from the middle coastal plain has not been well documented. The middle coastal plain is characterized by intensive agriculture on sandy soils with deeply incised or channelized streams. A 2-year study was conducted to determine the effectiveness of controlled drainage, riparian buffers, and a combination of both in the middle coastal plain of North Carolina. It was hypothesized that raising the water table near the ditch would enhance nitrate-nitrogen reduction through denitrification. On the sandy soils studied, controlled drainage did not effectively raise the water table near the ditch to a greater degree than observed on the free drainage treatment. Due to random treatment location, the free drainage treatment was installed along a ditch with a shallower impermeable layer compared to the impermeable layer on the controlled drainage treatments (2 m versus 3- to 4-m deep). This resulted in a perched or higher water table on the free drainage treatment. Over 17 storm events, the riparian buffer (free drainage) treatment had an average groundwater table depth of 0.92 m compared to 0.96 and 1.45 m for the combination (riparian buffer and controlled drainage) and controlled drainage treatments, respectively. Nitrate concentration decrease between the field wells and ditch edge wells averaged 29% (buffer only), 63% (buffer and controlled drainage), and 73% (controlled drainage only). Although apparently more nitrate was removed from the groundwater on the controlled drainage treatments, the controlled drainage treatment water table near the ditch was not raised closer to the ground surface compared to the free drainage treatment. Nitrate removal effectiveness was attributed to local soil and landscape properties, such as denitrification in deeper reduced zones of the soil profile.     

Effects of Stormwater Infiltration on Quality of Groundwater Beneath Retention and Detention Basins

Infiltration of storm water through detention and retention basins may increase the risk of groundwater contamination, especially in areas where the soil is sandy and the water table shallow, and contaminants may not have a chance to degrade or sorb onto soil particles before reaching the saturated zone. Groundwater from 16 monitoring wells installed in basins in southern New Jersey was compared to the quality of shallow groundwater from 30 wells in areas of new-urban land use. Basin groundwater contained much lower levels of dissolved oxygen, which affected concentrations of major ions. Patterns of volatile organic compound and pesticide occurrence in basin groundwater reflected the land use in the drainage areas served by the basins, and differed from patterns in background samples, exhibiting a greater occurrence of petroleum hydrocarbons and certain pesticides. Dilution effects and volatilization likely decrease the concentration and detection frequency of certain compounds commonly found in background groundwater. High recharge rates in storm water basins may cause loading factors to be substantial even when constituent concentrations in infiltrating storm water are relatively low.     

Effects of the Loading History on the Local Buckling Behavior and Failure Mode of Welded Beam-to-Column Joints in Moment-Resisting Steel Frames

The cyclic behavior of welded beam-to-column joints for moment-resisting steel frames was assessed by constant amplitude cyclic quasi-static tests. Reanalysis of the results showed that the failure mode of the joints strongly depends on cycle amplitudes. A premature brittle failure of the welds may occur if the cycle amplitude is not large enough to cause local buckling of beam flanges. Influence of both flange and web slenderness ratios on local buckling behavior, investigated through an experimental parametric study, is discussed. Four tests, carried out adopting different variable amplitude displacement histories, confirmed that isolated large amplitude cycles have beneficial effects on the joint response, extending its life; on the contrary, many large cycles clustered together endanger the seismic performance of beam-to-column joints. Numerical analyses allowed interpretation of the experimental data in terms of local stresses and strains. For “large amplitude” cycles numerical results indicate a local state of strain causing a progressive collapse for low-cycle fatigue while, in case of “small amplitude” cycles, brittle failure mode is due to the ratcheting of material.