Using Biological Knowledge for Layout Optimization of Construction Site Temporary Facilities: A Case Study

In recent years, a number of studies have successfully transformed various models for biological collective behavior into intelligent optimization algorithms. These bio-inspired optimization techniques have been developed to provide better solutions than traditional methods to a variety of engineering problems. This paper attempts to apply and compare recent bio-inspired algorithms for determining the best layout of construction temporary facilities. To validate the performance of the proposed techniques, an actual building construction project was used as a test problem. Based on the obtained results, the performance of each bio-inspired algorithm is highlighted and discussed. This paper presents beneficial insights to decision-makers in the construction industry that are involved in handling optimization problems

PREDIKSI KUAT TEKAN BETON DENGAN MENGGUNAKAN METODE ARTIFICIAL INTELLIGENCE

Metode yang akurat dalam memprediksi kuat tekan beton dapat memberikan keuntungan yang signifikan terhadap industri material konstruksi. Namun, metode-metode traditional yang ada sekarang ini memiliki banyak kekurangan, diantaranya biaya eksperimen yang mahal dan ketidakmampuan untuk menjabarkan hubungan antara komponen-komponen campuran beton dengan kuat tekan yang dihasilkan. Oleh sebab itu, studi ini memperkenalkan metode-metode kecerdasan buatan yang mampu memetakan hubungan input-output yang kompleks dalam campuran beton dengan tingkat akurasi yang tinggi. Dua buah metode artificial intelligence (AI), diantaranya artificial neural network (ANN), dan support vector machine (SVM) digunakan dalam studi ini. Total 1030 data historis dari data tes kuat tekan beton disediakan untuk mendemonstrasikan penggunaan model prediksi AI. Hasil simulasi menunjukkan bahwa metode-metode kecerdasan buatan ini mampu menghasilkan model prediksi dengan akurasi yang baik

A Novel Implementation of Nature-inspired Optimization for Civil Engineering: A Comparative Study of Symbiotic Organisms Search

The increasing numbers of design variables and constraints have made many civil engineering problems significantly more complex and difficult for engineers to resolve in a timely manner. Various optimization models have been developed to address this problem. The present paper introduces Symbiotic Organisms Search (SOS), a new nature-inspired algorithm for solving civil engineering problems. SOS simulates mutualism, commensalism, and parasitism, which are the symbiotic interaction mechanisms that organisms often adopt for survival in the ecosystem. The proposed algorithm is compared with other algorithms recently developed with regard to their respective effectiveness in solving benchmark problems and three civil engineering problems. Simulation results demonstrate that the proposed SOS algorithm is significantly more effective and efficient than the other algorithms tested. The proposed model is a promising tool for assisting civil engineers to make decisions to minimize the expenditure of material and financial resources

Optimizing the prediction accuracy of load-settlement behavior of single pile using a self-learning data mining approach

Pile foundations usually are used when the upper soil layers are soft clay and, hence, unable to support the structures’ loads. Piles are needed to carry these loads deep into the hard soil layer. Therefore, the safety and stability of pile-supported structures depends on the behavior of the piles. Additionally, an accurate prediction of the piles’ behavior is very important to ensure satisfactory performance of the structures. Although many methods in the literature estimate the settlement of the piles both theoretically and experimentally, methods for comprehensively predicting the load-settlement of piles are very limited. This study develops a new data mining approach called self-learning support vector machine (SL-SVM) to predict the load-settlement behavior of single piles. SL-SVM performance is investigated using 446 training data points and 53 test data points of cone penetration test (CPT) data obtained from the previous literature. The actual prediction accuracy is then compared to other prediction methods using three statistical measurements, including mean absolute error (MAE), coefficient of correlation (R), and root mean square error (RMSE). The obtained results show that SL-SVM achieves better accuracy than does LS-SVM and BPNN. This confirms the capability of the proposed data mining method to model the accurate load-settlement behavior of single piles through CPT data. The paper proposes beneficial insights for geotechnical engineers involved in estimating pile behavior

A novel fuzzy adaptive teaching–learning‑based optimization (FATLBO) for solving structural optimization problems

This paper presents a new optimization algorithm called fuzzy adaptive teaching–learning-based optimization (FATLBO) for solving numerical structural problems. This new algorithm introduces three new mechanisms for increasing the searching capability of teaching–learning-based optimization namely status monitor, fuzzy adaptive teaching–learning strategies, and remedial operator. The performance of FATLBO is compared with well-known optimization methods on 26 unconstrained mathematical problems and five structural engineering design problems. Based on the obtained results, it can be concluded that FATLBO is able to deliver excellence and competitive performance in solving various structural optimization problems

Modeling the Permanent Deformation Behavior of Asphalt Mixtures Using a Novel Hybrid Computational Intelligence

One of the main causes of pavement rutting is the repetitive action of traffic loads which results in the accumulation of permanent deformations. As a result, it is important to understand the characteristics of the permanent deformation behavior of asphalt mixes under repeated loading and to build the accurate mix model before they are placed in roadways. This study proposed a hybrid computational intelligence system named SOS-LSSVM for modelling the permanent pavement deformation behavior of asphalt mixtures. The SOS-LSSVM fuses Least Squares Support Vector Machine (LSSVM) and Symbiotic Organisms Search (SOS). LSSVM is employed for establishing the relationship model between the flow number, which is obtained from the laboratory test, and the parameters of the asphalt mix design. SOS is used to find the best LSSVM tuning parameters. A total 118 historical cases were used to establish the intelligence prediction model. Obtained results validate the ability of SOS-LSSVM to model the pavement rutting behavior of asphalt mixture with a relatively high accuracy measured by four error indicators. Therefore, the proposed computational intelligence systems can offer a high benefit for road designers and engineers in decision-making processes

Symbiotic Organisms Search Algorithm: theory, recent advances and applications

The symbiotic organisms search algorithm is a very promising recent metaheuristic algorithm. It has received a plethora of attention from all areas of numerical optimization research, as well as engineering design practices. it has since undergone several modifications, either in the form of hybridization or as some other improved variants of the original algorithm. However, despite all the remarkable achievements and rapidly expanding body of literature regarding the symbiotic organisms search algorithm within its short appearance in the field of swarm intelligence optimization techniques, there has been no collective and comprehensive study on the success of the various implementations of this algorithm. As a way forward, this paper provides an overview of the research conducted on symbiotic organisms search algorithms from inception to the time of writing, in the form of details of various application scenarios with variants and hybrid implementations, and suggestions for future research directions

Enhanced Symbiotic Organisms Search (ESOS) for Global Numerical Optimization

Symbiotic organisms search (SOS) is a simple yet effective metaheuristic algorithm to solve a wide variety of optimization problems. Many studies have been carried out to improve the performance of the SOS algorithm. This research proposes an improved version of the SOS algorithm called the “enhanced symbiotic organisms search” (ESOS) for global numerical optimization. The conventional SOS is modified by implementing a new searching formula into the parasitism phase to produce a better searching capability. The performance of the ESOS is verified using 26 benchmark functions and one structural engineering design problem. The results are then compared with existing metaheuristic optimization methods. The obtained results show that the ESOS gives a competitive and effective performance for global numerical optimization

Layout, Topology, and Size Optimization of Steel Frame Design Using Metaheuristic Algorithms: A Comparative Study

Determining the topology, layout, and size of structural elements is one of the most important aspects in designing steel seismic-resistant structures. Optimization of these parameters is beneficial to find the lightest weight of the structure, thus reducing construction cost. This study compares the performance of three metaheuristic algorithms, namely, Particle Swarm Optimization (PSO), Symbiotic Organisms Search (SOS), and Differential Evolution (DE). Three study cases are used in order to find the lightest structural weight without violating constraints based on SNI 1726:2019, SNI 1729:2020, and SNI 7860:2020. The results of this study show that SOS has better performance than other algorithms