Evolutionary Support Vector Machine Inference Model Research Articles

Risk-based maintenance strategy for deteriorating bridges using a hybrid computational intelligence technique: a case study

The current bridge inspection and maintenance protocol that is used in most countries focuses primarily on the visible aspects of bridge fitness and underestimates the invisible aspects, such as resistance to scouring and earthquake hazards. To help transportation authorities to better consider both aspects, the present study developed a new computational intelligence system, the so-called risk-based evaluation model for bridge life-cycle maintenance strategy (REMBMS). This model considers the three main risk factors of component deterioration, scouring and earthquakes in order to minimise the expected life-cycle cost of bridge maintenance. Monte Carlo simulation is used to estimate the probability of bridge maintenance. The evolutionary support vector machine inference model (ESIM) was applied to estimate the risk-related maintenance cost using historical data from the Taiwan Bridge Management System (TBMS) database. The time-influenced expected costs were obtained by multiplying each maintenance probability with its associated cost. Finally, the symbiotic organisms search (SOS) algorithm is used to identify the bridge maintenance schedule that optimises the life-cycle maintenance cost. The present study provides to bridge management authorities an effective approach for determining the optimal timing and budget for maintaining transportation bridges.

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

ABSTRACTNowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.

PREDICTING PRODUCTIVITY LOSS CAUSED BY CHANGE ORDERS USING THE EVOLUTIONARY FUZZY SUPPORT VECTOR MACHINE INFERENCE MODEL

Change orders in construction projects are very common and result in negative impacts on various project facets. The impact of change orders on labor productivity is particularly difficult to quantify. Traditional approaches are inadequate to calculate the complex input-output relationship necessary to measure the effect of change orders. This study develops the Evolutionary Fuzzy Support Vector Machines Inference Model (EFSIM) to more accurately predict change-order-related productivity losses. The EFSIM is an AI-based tool that combines fuzzy logic (FL), support vector machine (SVM), and fast messy genetic algorithm (fmGA). The SVM is utilized as a supervised learning technique to solve classification and regression problems; the FL is used to quantify vagueness and uncertainty; and the fmGA is applied to optimize model parameters. A case study is presented to demonstrate and validate EFSIM performance. Simulation results and our validation against previous studies demonstrate that the EFSIM predicts the impact of change orders significantly better than other AI-based tools including the artificial neural network (ANN), support vector machine (SVM), and evolutionary support vector machine inference model (ESIM).

Seismic Assessment of Bridge Diagnostic in Taiwan Using the Evolutionary Support Vector Machine Inference Model (ESIM)

Because earthquakes cause bridge damage resulting in incidents such as traffic gridlock and casualties, it is necessary to assess possible bridge damage under different seismic intensity levels in order to reduce the incidence of disasters. However, because there are many bridges in Taiwan, the time and budget will be restricted to conduct traditional structural analysis (preliminary assessment, detailed analysis) of each bridge to obtain its yield acceleration (Ay) and collapse acceleration (Ac). Hence, this study developed an inference model by integrating two AI techniques: support vector machines (SVM) and fast messy genetic algorithms (fmGA). The study applied historical cases to infer Ay and Ac values by the mapping relation between preliminary assessment factors (input) of historical cases and the detailed assessment of Ay and Ac values (output). According to the above inference model to predict Ay and Ac values, the probability of possible bridge damage by earthquakes can be predicted as a suggestion for bridge management personnel in a short period of time. This study adopted 121 RC bridges in Taiwan and selected 109 bridges for training cases and 12 bridges for testing cases to calculate the root mean square error (RMSE). The results indicate that the RMSE of training is 0.087 and of testing is 0.0869.

TIME DEPENDENT EVOLUTIONARY FUZZY SUPPORT VECTOR MACHINE INFERENCE MODEL FOR PREDICTING DIAPHRAGM WALL DEFLECTION

Brace diaphragm walls are commonly used in underground structures in metropolitan areas, where avoiding costly damage to adjacent infrastructure / buildings is critical to project success. It is necessary to make accurate diaphragm wall deflection predictions to ensure actual deflection falls within allowable limits, and thus ensure the safety of both the project and adjacent structures. Numer- ous studies and approaches, such as empirical, semi-empirical as well as numerical approaches, have addressed excavation-induced deflection in diaphragm walls. Ar- tificial intelligence (AI) has been used recently by several researchers to improve diaphragm wall deflection prediction capabilities. This paper proposes a hybrid ar- tificial intelligence system, namely the evolutionary fuzzy support vector machine inference model for time series data (EFSIMT ), to predict diaphragm wall deflection in deep excavation through the application of historical project data. Simulations were performed on 1,083 instances, segregated into a total of 988 training data sets and 95 test data sets. Validation results show that the EFSIMT achieves higher performance in comparison with Artificial Neural Networks and the Evolutionary Support Vector Machine Inference Model (ESIM). Therefore, EFSIMT has great potential as a predictive tool for diaphragm wall deflection problems and assisting project managers/engineers to ensure safety during the construction process.

High-performance concrete compressive strength prediction using Genetic Weighted Pyramid Operation Tree (GWPOT)

This study uses the Genetic Weighted Pyramid Operation Tree (GWPOT) to build a model to solve the problem of predicting high-performance concrete compressive strength. GWPOT is a new improvement of the genetic operation tree that consists of the Genetic Algorithm, Weighted Operation Structure, and Pyramid Operation Tree. The developed model obtained better results in benchmark tests against several widely used artificial intelligence (AI) models, including the Artificial Neural Network (ANN), Support Vector Machine (SVM), and Evolutionary Support Vector Machine Inference Model (ESIM). Further, unlike competitor models that use “black-box” techniques, the proposed GWPOT model generates explicit formulas, which provide important advantages in practical application.

Novel Genetic Algorithm-Based Evolutionary Support Vector Machine for Optimizing High-Performance Concrete Mixture

AbstractAn effective method for optimizing high-performance concrete mixtures can significantly benefit the construction industry. However, traditional proportioning methods are not sufficient because of their expensive costs, limitations of use, and inability to address nonlinear relationships among components and concrete properties. Consequently, this research introduces a novel genetic algorithm (GA)–based evolutionary support vector machine (GA-ESIM), which combines the K-means and chaos genetic algorithm (KCGA) with the evolutionary support vector machine inference model (ESIM). This model benefits from both complex input-output mapping in ESIM and global solutions with faster convergence characteristics in KCGA. In total, 1,030 data points from concrete strength experiments are provided to demonstrate the application of GA-ESIM. According to the results, the newly developed model successfully produces the optimal mixture with minimal prediction errors. Furthermore, a graphical user interface is uti...

Forecasting enterprise resource planning software effort using evolutionary support vector machine inference model

Despite significant advances in procedures that facilitate project management, the continued reliance of software managers on guesswork and subjective judgment causes frequent project time overruns. This study uses an Evolutionary Support Vector Machine Inference Model (ESIM) for efficiently and accurately estimating the person-hour of ERP system development projects. The proposed ESIM is a hybrid intelligence model integrating a support vector machine (SVM) with a fast messy genetic algorithm (fmGA). The SVM mainly provides learning and curve fitting while the fmGA minimizes errors. The analytical results in this study confirm that, compared to artificial neural networks and SVM, the proposed ESIM provides preliminary prediction at early phase of ERP software development effort for the manufacturing firms with superior accuracy, shorter training time and less overfitting. Future research can develop user-friendly expert systems with window or browser interfaces that can be used by planning personnel to flexibly input related variables and to estimate development effort and corresponding project time/cost.

IMPROVED CONSTRUCTION SUBCONTRACTOR EVALUATION PERFORMANCE USING ESIM

The evaluation of potential subcontractors is a complex task for construction companies. The success of such evaluations currently relies heavily on personal factors that include management experience and intuition. The object of this study was to propose a support model that would improve current subcontractor performance evaluation practices. The appropriateness of employing the Evolutionary Support Vector Machine Inference Model (ESIM) in evaluation procedures was studied and analyzed, and a Subcontractor Rating Evaluation Model (SREM) was developed by adapting the ESIM to fit subcontractor performance cases in the historical record. The effectiveness of the proposed SRPM was subsequently validated in a case study on an actual general contractor. The proposed method assigned ratings to subcontractors that were substantively the same as ratings assigned by traditional means. Results demonstrate the value of employing the proposed SREM in subcontractor evaluations.

Predicting high-tech equipment fabrication cost with a novel evolutionary SVM inference model

Accurately predicting fabricating cost in a timely manner can enhance corporate competitiveness. This study employs the Evolutionary Support Vector Machine Inference Model (ESIM) to predict the cost of manufacturing thin-film transistor liquid–crystal display (TFT-LCD) equipment. The ESIM is a hybrid model integrating a support vector machine (SVM) with a fast messy genetic algorithm (fmGA). The SVM concerns primarily with learning and curve fitting, while the fmGA is focuses on optimization of minimal errors. Recently completed equipment development projects are utilized to assess prediction performance. The ESIM is developed to achieve the fittest C and γ parameters with minimized prediction error when used for cost estimate during conceptual stages. This study describes an actionable knowledge-discovery process using real-world data for high-tech equipment manufacturing industries. Analytical results demonstrate that the ESIM can predict the costs of manufacturing TFT-LCD fabrication equipment with sufficient accuracy.

Decision making for contractor insurance deductible using the evolutionary support vector machines inference model

Loss risk during the course of a construction project may be described in terms of frequency (i.e., loss frequency) and severity (i.e., loss severity). This study focused on improving the methodology used to evaluate loss risk. The authors first identified the common attributes of building construction project loss through a review of the literature and interviews with experts. Objective factors adequate to describe loss attributes were selected as model inputs. The loss prediction model was created using the evolutionary support vector machine inference model (ESIM) and deployed to evaluate loss frequency and loss severity. This research combined the deductible efficient frontier curve with the indifference curve of risk versus insurance cost, and developed criteria for optimal insurance deductible decision making.

Estimate at Completion for construction projects using Evolutionary Support Vector Machine Inference Model

Construction projects are influenced by a range of factors that impact upon final project cost. Estimate at Completion (EAC) is an important approach used to estimate final project cost, which takes into consideration probable project performance and risks. EAC helps project managers identify potential but still unknown problems and adopt response strategies. This study constructed an evolutionary EAC model to generate project cost estimates that proved significantly more reliable than estimates achievable using currently prevailing formulae. The developed learning model fused two artificial intelligence approaches, namely the fast messy genetic algorithm (fmGA) and Support Vector Machine (SVM), to create an Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM was then applied to estimate final project costs for historical cases. Finally, using the EAC estimate, project cost influence indices, and project cost diagrams, the discrepancy between estimate and practical values was examined to determine potential problems in order to help project managers better control project costs. The learning results were validated in real applications that showed good performance for training models. Providing project managers reliable EAC trend estimates is helpful for their effective control of project costs and taking appropriate peremptory measures to handle potential problems.

Project success prediction using an evolutionary support vector machine inference model

Accomplishing construction projects successfully requires continuous monitoring and control by construction managers of factors critical to project success. This research proposed using an Evolutionary Support Vector Machine Inference Model (ESIM) to predict project success dynamically. ESIM is a hybrid that integrates a support vector machine (SVM) with a fast messy genetic algorithm (fmGA). SVM is concerned primarily with learning and curve fitting, while fmGA deals primarily with optimization. Furthermore, the model integrates the process of Continuous Assessment of Project Performance (CAPP) to select factors that influence project success. Training and test patterns were collected from a CAPP database of 46 construction projects. These projects represent real data collected by Russell from 16 company members of the Construction Industry Institute (CII). Results show that ESIM is able to predict project success at a significant level of accuracy.

Evolutionary support vector machine inference system for construction management

Problems in construction management are complex, full of uncertainty, and vary based on site environment. Two tools, the fast messy genetic algorithms (fmGA) and support vector machine (SVM), have been successfully applied to solve various problems in construction management. Considering the characteristics and merits of each, this paper combines the two to propose an Evolutionary Support Vector Machine Inference Model (ESIM). In the ESIM, the SVM is primarily employed to address learning and curve fitting, while fmGA addresses optimization. This model was developed to achieve the fittest C and γ parameters with minimal prediction error. This research further integrates the developed ESIM with an object-oriented (OO) computer technique to create an Evolutionary Support Vector Machine Inference System (ESIS). Simulations conducted to demonstrate the robustness of the model in application indicate that ESIS may be used as a multifarious intelligent decision support system in decision-making to help solve a wide range of construction management problems.