Genetic programming for fiber-threading for fiber-reinforced plastics

Setting up fiber-threading for a pultrusion line is tedious, error prone and takes a long time. Between 100 and 1000 fibers have to be arranged into a two-dimensional shape, which have to be threaded between several support plates without causing crossovers. When manually planning this process based on intuition, it is hard to keep track of the complexity. This slows the process down to where it can take several hours or several days, and shortening this duration reduces the cost considerably. As planning the setup takes up a large chunk of time, we are proposing a simulation and an algorithm to automatically calculate how the fiber bundles need to be threaded from the creels through the support plates to result in the desired shape. Using a three-dimensional simulation for collision detection in conjunction with a genetic algorithm, we are able to shorten the planning of the fibers to around 10 minutes on a modern 8-core personal computer. Based on this data, further work can be done to further improve, visualize or permanently store the data in a digitized company

Aided diagnosis of structural pathologies with an expert system

Sustainability and safety are social demands for long-life buildings. Suitable inspection and maintenance tasks on structural elements are needed for keeping buildings safely in service. Any malfunction that causes structural damage could be called pathology by analogy between structural engineering and medicine. Even the easiest evaluation tasks require expensive training periods that may be shortened with a suitable tool. This work presents an expert system (called Doctor House or DH) for diagnosing pathologies of structural elements in buildings. DH differs from other expert systems when it deals with uncertainty in a far easier but still useful way and it is capable of aiding during the initial survey 'in situ', when damage should be detected at a glance. DH is a powerful tool that represents complex knowledge gathered from bibliography and experts. Knowledge codification and uncertainty treatment are the main achievements presented. Finally, DH was tested and validated during real surveys.Peer ReviewedPostprint (author's final draft

Optimum design of reinforced concrete rectangular columns subjected to axial compression and biaxial bending moments

This study investigates the optimum design in terms of minimum cost of reinforced concrete rectangular columns subjected to axial compression force and biaxial bending moments about x and y axes. For the optimisation process, the Generalised Reduced Gradient (GRG) technique was implemented which is embedded within Excel Solver add-in tool. GRG method was adopted because of its robustness and efficiency in dealing with a wide range of engineering problems as demonstrated by several works available in the literature. GRG is regarded as one of the best deterministic local optimisation methods. Equally important, the GRG is part and parcel of Microsoft Excel which means that there is no need to pay for extra licence to run any optimisation problem. The formulated models for the design of reinforced concrete columns and the imposed constraints were based on the provisions of Eurocode 2 (EC2). The design variables were the cross sectional dimensions (width and depth) and reinforcing steel area. It is worthwhile to mention that the position and orientation of neutral axis were also considered as design variables in order to avoid solving the highly nonlinear simultaneous equations which is time consuming and not necessarily leading to a convergent solution. Several design parameters, such as materials cost ratios and depth to width ratios were tested. Consequently, optimum design charts were developed for a wide range of practical combination of axial compression forces and eccentricities in both x and y directions. Following a comprehensive investigation of the minimum cost problems carried out for different cases, one can conclude that variation of depth to width ratio may have noticeable effect on the optimum width, depth and area of steel only when the eccentricity in y direction (ey) is much greater than the eccentricity in x direction (ex). Furthermore, the effect of steel to concrete unit cost ratio (Cs/Cc) is more obvious at larger loads and higher eccentricities

Improvements in meta-heuristic algorithms for minimum cost design of reinforced concrete rectangular sections under compression and biaxial bending

A numerical procedure is proposed in this paper for achieving the minimum cost design of reinforced concrete rectangular sections under compression and biaxial bending by using biologically-inspired meta-heuristic optimization algorithms. The problem formulation includes the costs of concrete, reinforcement and formwork, obtaining the detailed optimum design in which the section dimensions and the reinforcement correspond to values used in practice. The formulation has been simplified in order to reduce the computational cost while ensuring the rigor necessary to achieve safe designs. The numerical procedure includes the possibility of using high-strength concrete and several design constraints, such as mínimum reinforcement and limiting the neutral axis depth. Two numerical examples are presented, drawing comparisons between the results obtained by ACI318 and EC2 standards

Multi-objective engineering shape optimization using differential evolution interfaced to the Nimrod/O tool

This paper presents an enhancement of the Nimrod/O optimization tool by interfacing DEMO, an external multiobjective optimization algorithm. DEMO is a variant of differential evolution – an algorithm that has attained much popularity in the research community, and this work represents the first time that true multiobjective optimizations have been performed with Nimrod/O. A modification to the DEMO code enables multiple objectives to be evaluated concurrently. With Nimrod/O’s support for parallelism, this can reduce the wall-clock time significantly for compute intensive objective function evaluations. We describe the usage and implementation of the interface and present two optimizations. The first is a two objective mathematical function in which the Pareto front is successfully found after only 30 generations. The second test case is the three-objective shape optimization of a rib-reinforced wall bracket using the Finite Element software, Code_Aster. The interfacing of the already successful packages of Nimrod/O and DEMO yields a solution that we believe can benefit a wide community, both industrial and academic

Ant colony optimisation and local search for bin-packing and cutting stock problems

The Bin Packing Problem and the Cutting Stock Problem are two related classes of NP-hard combinatorial optimization problems. Exact solution methods can only be used for very small instances, so for real-world problems, we have to rely on heuristic methods. In recent years, researchers have started to apply evolutionary approaches to these problems, including Genetic Algorithms and Evolutionary Programming. In the work presented here, we used an ant colony optimization (ACO) approach to solve both Bin Packing and Cutting Stock Problems. We present a pure ACO approach, as well as an ACO approach augmented with a simple but very effective local search algorithm. It is shown that the pure ACO approach can compete with existing evolutionary methods, whereas the hybrid approach can outperform the best-known hybrid evolutionary solution methods for certain problem classes. The hybrid ACO approach is also shown to require different parameter values from the pure ACO approach and to give a more robust performance across different problems with a single set of parameter values. The local search algorithm is also run with random restarts and shown to perform significantly worse than when combined with ACO

Gene expression programming application for prediction of ultimate axial strain of FRP-confined concrete

The last three decades have seen increasing applications of fiber-reinforced polymer materials in structural engineering because of their many advantages over traditional strengthening and reinforcing materials. On the other hand, soft computing approaches have recently been widely used to model human activity in many areas of civil engineering applications. This paper presents the use of genetic expression programming as a tool to predict the ultimate axial strain of fiber-reinforced polymer-confined concrete. A large experimental data set (219) of these tests is collected from published literature. The prediction of the proposed new genetic expression programming-based model was compared with the results obtained using the existing analytical equations proposed in the current literature. In this paper, attempts were made to present a complete review of genetic expression programming in structural engineering. Good agreement between the experimental data and predicted results is obtained