Neuro Fuzzy And Soft Computing Solution Manual
Julia Heaslet
Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems. Soft computing is a collection of methodologies, which aim to exploit tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The focus is to publish the highest quality research in application, advance and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Swarm Intelligence and other similar techniques to address real world complexities.
Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.
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Similar to this, it is clear that AI is effective in the field of construction engineering and management, enabling users to achieve project objectives within budget and time constraints16,17. Yet, due to the complicated nature of many variable restrictions and the lack of clear or precise detailed information processing, the relevant research has shown that standalone AI systems have limits for handling non-trivial real-work situations18,19,20. Construction engineering and management constraints are classified according to their complexity, non-linearity, non-specificity, dynamism and uncertainty. For instance, fuzzy systems are particularly effective in evaluating the representation of explicit knowledge and making inferences15,21,22,23. Elmousalami24 investigated the appropriateness of computational intelligence techniques that included neuro-computing, fuzzy logic and evolutionary computation, which were modified for the evaluation of parametric cost-prediction models. Gregory et al.25 adapted a neuro-fuzzy soft computing technique for the prediction of the engineering performance in construction projects. Shahtaheri et al.26 proposed a predictive model based on an adaptive neuro-fuzzy inference system (ANFIS), employing 272 data points from 14 projects in the construction industry to approximate reference line tolls. Rashidi et al.27 used genetic and neuro-fuzzy systems to address the issue of choosing a skilled project manager. Similar to this, Shahhosseini and Sebt28 used an adaptive neuro-fuzzy inference system (ANFIS) to assign and select workers for construction projects depending on their qualifications.
The use of AI in the construction industry benefits both shareholders and investors in all phases of the construction process, including the proposal, costing and financing; material acquisition and correct execution; setup and resource management; and commercial prototype rehabilitation. In order to reduce the demand for experts in structure development and schedule designs, researchers and participants in construction-related projects develop technologies that resemble AI. To complete a project on time and under budget, a great project schedule is essential29,30. According to Schelle31, effective structure management entails the competent arrangement of several instances of contributing stakeholders, societies and fundamental building blocks. This might involve simulated elements such as tasks, errands and charges, as well as infinite associated units of diverging interactions. For building projects, when given step-by-step instructions and mandatory reinvigoration of responsibilities, this may allow complex undertakings to be managed successfully so that the intended results are achieved. Such recommendations and principles are given for large datasets that have existed over time and are perhaps active. They flow from one correctly specified form to the next properly outlined one. AlTabtabai32, for instance, employed a networked BP to launch a managerial method employing specialists chosen from the activities timetable, who supervised and predicted the repurposing of an abandoned many-story building.
This study is aimed at applying artificial intelligence to construction scheduling to achieve better prediction of the project duration and minimize the costs in the building construction industry. The details derived from this research study will provide a new dynamic monitoring and optimization tool to track the progress of a project. The purpose of the research is to investigate the potential of neural networks and Neuro-fuzzy models in improving construction scheduling accuracy and efficiency and to provide insights into the application of these models in the broader field of construction engineering and management. A good construction project schedule is accurate, thorough and updated frequently, with communication regarding the project given first importance. Team cooperation is another important element since it helps tasks to be completed successfully. Scheduling allows project managers to match the labor, supplies, equipment and all other resources connected with activities and construction tasks over time, which is essential for the completion and success of a construction project. A well-planned construction schedule ensures the completion of projects by outlining the exact pace at which each job is to be completed, the sequences and methods for delivering resources, and the execution of all generated tasks35,36.
The application of neural networks and neuro-fuzzy models in construction scheduling is significant for several reasons. First, construction projects are complex and involve multiple tasks that need to be completed in a specific sequence. Any delay in one task can have a cascading effect on the rest of the project. Therefore, accurate scheduling is critical for the success of a construction project. Secondly, traditional scheduling methods rely on the experience and intuition of project managers, which can be subjective and lead to errors. The use of artificial intelligence (AI) models, such as neural networks and neuro-fuzzy models, can provide objective and data-driven scheduling solutions. Thirdly, the construction industry has been slow to adopt new technologies, and the application of AI in construction scheduling represents a step forward in the adoption of digital technologies. The use of AI models can help improve productivity, reduce project delays, and ultimately save costs. Overall, the significance of this study lies in its contribution to the development of more accurate and efficient scheduling methods for the construction industry, which can lead to improved project outcomes and better resource utilization.
This is the opposite of the front-scrolling algorithm. It calculates from the last node of the activity network and returns to the foremost node using mathematical relationships, as presented in Eq. (2)47.
Earned value management (EVM) depicts in straightforward words the level of coverage and what tasks remain in a project. This accurate report is critical in recognizing faults, changing plans, amending mistakes and ensuring not only timely but also excellent delivery. The EVM puts cost and time on a unified scale, allowing one to graphically evaluate the actual work done vs. what was expected. The following direct indicators are adopted to appropriately scrutinize the timetable and costs accrued for a given mission using EVM49.
Planned value (PV): is otherwise called the budgeted cost of work scheduled (BCWS). It is the cost sum through the current reporting period. It is the projected rate of a task arranged to conclude within an agreed interval50;
Actual cost (AC): is also called the actual cost of work performance (ACWP). The actual cost implies the authentic payments made to complete a task by the set date. It is the recorded cost of completed works when using the preset interval alone;
Earned value (EV): is otherwise referred to as the budgeted cost of work performance (BCWP). This is the aggregate task financial plan, increased by the percentage of task achievement. It denotes the accepted financial plan of tasks completed by the deadline51;
Cost Performance Index (CPI) and Cost Variance (CV): the CPI is the ratio of EV to AC. It is a comparative quota of the cost of the project in terms of proficiency, which is capable of guesstimating the price of tasks left uncompleted52. The CV, therefore, stands for the variance between EV and AC. Whether a project is carried out as budgeted is showcased by the EV and AC. Zero indicates that the project is falling within the appropriated cost margins, whereas the project is considered as over or under the appropriate cost if the difference is negative or positive. The mathematical relationships are presented in Eqs. (5) and (6)53,54:
The performance of the intelligent model developed was evaluated in order to confirm that it has a proven ability to predict or estimate the target parameters with an acceptable degree of accuracy. Several performance criteria (statistical measures) used in the related literature, such as the loss function parameters, mean absolute error (MAE) and root mean square error (RMSE), are given with the formulas shown in Eqs. (7) and (8)55,56,57.
Through expert judgment and consultations, the model variables were sorted to evaluate the performance indicators of the construction project. Distribution histograms were plotted for the model input and explanatory variables, as shown in Fig. 6, which present how often each value occurred in a dataset, showing slight or no skewness for the two parameters used63.
The modeling process was carried out with the datasets fed to the neural network using MATLAB software. The model framework was designed as six input variables namely, ES, planned progress, SV, SPI, CPI and AT; with one output parameter as the EV. The processing parameter settings for the neural network model are presented in Table 4 and Fig. 7, which show a 6-10-1 two-layer feed-forward network with a tansig activation function (AF) for the hidden neurons and linear AF output neurons. This can perform multidimensional mapping to solve complex system solutions. In order to determine the best-performing n-neurons, mean squared error (MSE) and R-values, evaluation criteria were used, which revealed that 10 neurons produced optimal results66,67.
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