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).

A novel hybrid intelligent approach for contractor default status prediction

In the construction industry, evaluating the financial status of a contractor is a challenging task due to the myriad of the input data as well as the complexity of the working environment. This article presents a novel hybrid intelligent approach named as Evolutionary Least Squares Support Vector Machine Inference Model for Predicting Contractor Default Status (ELSIM-PCDS). The proposed ELSIM-PCDS is established by hybridizing the Synthetic Minority Over-sampling Technique (SMOTE), Least Squares Support Vector Machine (LS-SVM), and Differential Evolution (DE) algorithms. In this new paradigm, the SMOTE is specifically used to deal with the imbalanced classification problem. The LS-SVM acts as a supervised learning technique for learning the classification boundary that separates the default and non-default contractors. Additionally, the DE algorithm automatically searches for the optimal parameters of the classification model. Experimental results have demonstrated that the classification performance of the ELSIM-PCDS is better than that of other benchmark methods. Therefore, the proposed hybrid approach is a promising alternative for predicting contractor default status.

GROUTABILITY PREDICTION OF MICROFINE CEMENT BASED SOIL IMPROVEMENT USING EVOLUTIONARY LS-SVM INFERENCE MODEL

Permeation grouting is a widely used technique for soil improvement in construction engineering. Thus, predicting the results of the grouting activity is a particularly interesting topic that has drawn the attention of researchers both from the academic field and industry. Recent literature has indicated that artificial intelligence (AI) approaches for groutability prediction are capable of delivering better performance than traditional formula-based ones. In this study, a novel AI method, evolutionary Least Squares Support Vector Machine Inference Model for groutability prediction (ELSIM-GP), is proposed to forecast the result of grouting activity that utilizes microfine cement grout. In the model, Least Squares Support Vector Machine (LS-SVM) is a supervised machine learning technique that is employed to learn the decision boundary for classifying high dimensional data. Differential Evolution (DE) is integrated into ELSIM-GP for automatically optimizing its tuning parameters. 240 historical cases of grouting process for sandy silt soil have been collected to train, validate, and test the inference model. Experimental results demonstrated that ELSIM-GP can overcome other benchmark approaches in terms of forecasting accuracy. Therefore, the proposed approach is a promising alternative for predicting groutability.

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...

ENHANCED TIME-DEPENDENT EVOLUTIONARY FUZZY SUPPORT VECTOR MACHINES INFERENCE MODEL FOR CASH FLOW PREDICTION AND ESTIMATE AT COMPLETION

This study conducts a mechanism enhancing the time series data treatment of the time-dependent evolutionary fuzzy support vector machine inference model (EFSIMT). The enhanced model, EFSIMET, was developed particularly to treat construction management problems that contain time series data. EFSIMET is an artificial intelligent hybrid system in which fuzzy logic (FL) deal with vagueness and approximate reasoning; support vector machine (SVM) acts as supervise learning tool; and fast messy genetic algorithm (fmGA) works to optimize FL and SVMs parameters simultaneously. Moreover, to capture the time series data characteristics, the inference model develops fmGA-based searching mechanism to seek suitable weight values to weight the training data points. This random-based searching mechanism has capacity to address the complex and dynamic nature of time series data; thus, it could improve the model's performance significantly. Nowadays, construction managementis facing complex and difficult problems due to the increasing uncertainties during project implementation. Therefore, the second objective of this study is proposed for the application of EFSIMET to treat two typical problems in construction: forecasting cash flow and estimate at completion. Through performance's comparison with previous works, the effectiveness and reliability of EFSIMET are proven. Hence, this model may be used as an intelligent decision support tool to assist the decision-making process to solve the construction management's difficulties.

Risk Score Inference for Bridge Maintenance Project Using Evolutionary Fuzzy Least Squares Support Vector Machine

Bridges are essential infrastructure of the transportation network. Therefore, maintenance tasks are mandatory to prevent these structures from degradation over time. In practice, funding availability for maintenance projects is often confined and this necessitates prioritization of different bridges that are in need of remedial activities. The writers aim to construct an artificial intelligence (AI) approach, evolutionary fuzzy least-squares support-vector machine (LSSVM) inference model (EFLSIM), for prioritizing bridges based on risk scores (RSs). In EFLSIM, fuzzy logic (FL) is utilized to enhance the capability of approximate reasoning and to deal with subjective information, which is obtained from human judgment. The inference model employs LSSVM as a supervised learning technique to infer the fuzzy input-output mapping relationship. Differential evolution (DE) is integrated into the model to optimize its tuning parameters. Experimental results and comparison illustrates that EFLSIM can successfully absorb and simulate human knowledge in the bridge-assessment process. Additionally, the newly built model has outperformed other benchmark approaches in terms of both reliability and accuracy. A 10-fold cross-validation process has demonstrated that the EFLSIM has achieved more than 38% reduction in RMS error compared to other benchmark methods. Thus, the proposed AI approach is a promising tool to support decision-makers in bridge-maintenance planning.

High-performance Concrete Compressive Strength Prediction using Time-Weighted Evolutionary Fuzzy Support Vector Machines Inference Model

The major different between High Performance Concrete (HPC) and conventional concrete is essentially the use of mineral and chemical admixture. These two admixtures made HPC mechanical behavior act differently compare to conventional concrete at microstructures level. Certain properties of HPC are not fully understood since the relationship between ingredients and concrete properties is highly nonlinear. Therefore, predicting HPC behavior is relatively difficult compared to predicting conventional concrete behavior. This paper proposes an Artificial Intelligence hybrid system to predict HPC compressive strength that fuses Fuzzy Logic (FL), weighted Support Vector Machines (wSVM) and fast messy genetic algorithms (fmGA) into an Evolutionary Fuzzy Support Vector Machine Inference Model for Time Series Data (EFSIMT). Validation results show that the EFSIMT achieves higher performance in comparison with Support Vector Machines (SVM) and obtains results comparable with Back-Propagation Neural Network (BPN). Hence, EFSIMT offers strong potential as a valuable predictive tool for HPC compressive strength.

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.

Evolutionary risk preference inference model using fuzzy support vector machine for road slope collapse prediction

Road slope collapse events are frequent occurrences in Taiwan, often exacerbated by earthquakes and/or heavy rainfall. Such collapses disrupt transportation, damage infrastructure and property, and may cause injuries and fatalities. While significant efforts are regularly invested in reducing road slope collapse risk, most focus exclusively on limiting the potential for slope failure. Collapse prediction efforts may result in inference errors that cause allocated road slope maintenance resources to be expended inefficiently, resulting in relatively higher collapse risk than should be achievable under ideal circumstances. Most maintenance programs rely on decision maker risk preferences, as his/her knowledge and experience can contribute to risk assessment decision making. The decision maker is capable of choosing an acceptable balance between two types of inference error, i.e., α and β errors. This preference may later be used as guidance to minimize inference error. This paper proposed the evolutionary risk preference fuzzy support vector machine inference model (ERP-FSIM) as a hybrid AI system able to make predictions regarding road slope collapse that takes decision maker risk preference into account. Validation results demonstrate ERP-FSIM viability, as level of average error both for the training set and validation set conform to the decision maker risk preference ratio and is significantly lower than the error tolerance of ±10%.

Risk Preference Based Support Vector Machine Inference Model for Slope Collapse Prediction

Abstract Slope collapse prediction inference errors may be divided into two types, namely 1) predicted collapse followed by actual non-collapse (i.e., α error) and 2) predicted non-collapse followed by actual collapse (i.e., β error). As limited time and information make it difficult to reduce the rate of prediction error, making predictions in a manner that considers decision maker risk preferences in order to consider the preferred α to β error ratio in road slope maintenance strategy formulation represents an important issue. This study proposes an innovative inference model, the Risk Preference based Support Vector Machine Inference Model (RP-SIM). RP-SIM infers the mapping relationship between input and output variables from historical cases using a Support Vector Machine (SVM), and then uses a fast messy genetic algorithm (fmGA) to conduct an optimal search based on α and β values set in accordance with actual decision maker risk preference.

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.

Evolutionary fuzzy decision model for cash flow prediction using time-dependent support vector machines

The ability of project managers to make reliable cash flow predictions enhances project cost flow control and management. Reliable cash flow prediction over the course of a construction project puts the project manager in a better position to identify potential problems and develop appropriate strategies to mitigate the negative effects of such on overall project success. Therefore, managers should monitor project progress using cash flow data, which has unique characteristics, as time series data. However, the complex, mutable nature of construction projects currently requires significant reliance on experience and expert opinions to predict cash flow on an ongoing basis. Recent studies have indicated good potential for using artificial intelligence to reduce reliance on human input in cash flow prediction processes. The Evolutionary Fuzzy Support Vector Machine Inference Model for Time Series Data (EFSIM T), an artificial intelligence hybrid system focusing on the management of time series data characteristics which fuses fuzzy logic (FL), weighted support vector machines (weighted SVMs) and a fast messy genetic algorithm (fmGA), represents a promising alternative approach to predicting cash flow. Simulations performed on historical cash flow data demonstrate the EFSIM T is an effective tool for predicting cash flow.