Doddy Prayogo

Perubahan SNI 0328471992 menjadi SNI 0328472002 membawa perubahan pada nilai rasio jumlah momen nominal kolom terhadap balok (overstrength factor) dari sekitar 1,625 menjadi 1,2. Perubahan ini dirasakan kurang konservatif untuk menjamin terpenuhinya kriteria strong column weak beam pada Sistem Rangka Pemikul Momen Khusus (SRPMK). Penelitian ini bertujuan untuk memeriksa kecukupan nilai overstrength factor kolom pada suatu Sistem Rangka Pemikul Momen Khusus (SRPMK) di wilayah 2 peta gempa Indonesia. Struktur yang ditinjau adalah bangunan perkantoran beton bertulang 6 lantai dengan bentang 6, 8 dan 10 meter dan dengan denah beraturan sesuai dengan persyaratan yang diatur dalam SNI 0328472002. Dalam penelitian ini, semua peraturan pada SNI 0328472002 diikuti termasuk persyaratan waktu getar alami fundamental yang pada penelitian sebelumnya tidak diikuti. Sehingga, menghasilkan dimensi stuktur yang besar dan penggunaan tulangan minimum yang berakibat pada meningkatnya nilai overstrength...

The excessive CO2 is produced through the construction phases including in the material production, in the construction of the building itself, and even in the demolition. Moreover, to save the cost, construction practitioners often neglect the environmental related issues. As a result, construction industry has become one of the main sectors yielding the greenhouse emission. This study develops Ant Colony Optimization (ACO) for determining a set of trade-off solutions between construction cost and CO2 emission. Ant Colony Optimization is a stochastic-search technique inspired by the behavior of real ant colonies. A renovation project case study is provided to validate the performance of ACO. According to the result, the model attempts to minimize construction cost and CO2 emission of the project as two objectives. Moreover, ACO offers strong potential to facilitate the decision-making in construction management.

Finding the effective method for optimizing the high-performance concrete mixture could deliver significant benefits to construction industry. However, traditional proportioning methods were not sufficient due to the expensive cost, limitation of use, and incompetence to deal with nonlinear relationship among components and concrete properties. Consequently, this research introduces a novel GA-based Evolutionary SVM (GA-ESIM) which combines together K-means Chaos Genetic Algorithm (KCGA) with Evolutionary SVM Inference Model (ESIM). This model benefits from both complex input-output mapping in ESIM and global solution with faster convergence characteristics in KCGA. Total 1,030 historical data of concrete strength test are provided to demonstrate the GA-ESIM application. According to the results, the new developed model successfully produces the optimum mixture with minimal prediction error.

Structural design optimization becomes an uneasy task and extremely challenging for many practical applications of structural engineering. Most problems are highly nonlinear due to a huge number of design variables and complex constraints on stresses, displacements, and load carrying capability. Traditional local search algorithms, such as hill-climbing, Nelder-Mead downhill methods, and steepest descent, are often failed to find the optimum solution in nonlinear problems. The growing complexity of these problems has motivated researchers to search for advance optimization methods. Nature has been a source inspiration for developing the computational algorithms over past decades. One of the most recent nature-inspired tools, namely Black Hole (BH), has been constructed based on the gravitational pull phenomenon of black hole. This study intends to investigate BH to solve a set of complex structural optimization problems. Several complex case studies of truss design problem are provi...

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.

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).

An 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 utilized to assist users in performing optimization tasks.

Resource leveling is used in project scheduling to reduce fluctuation in resource usage over the period of project implementation. Fluctuating resource usage frequently creates the untenable requirement of regularly hiring and firing temporary staff to meet short-term project needs. Construction project decision makers currently rely on experience-based methods to manage fluctuations. However, these methods lack consistency and may result in unnecessary waste of resources or costly schedule overruns. This research introduces a novel discrete symbiotic organisms search for optimizing multiple resources leveling in the multiple projects scheduling problem (DSOS-MRLMP). The optimization model proposed is based on a recently developed metaheuristic algorithm called symbiotic organisms search (SOS). SOS mimics the symbiotic relationship strategies that organisms use to survive in the ecosystem. Experimental results and statistical tests indicate that the proposed model obtains optimal results more reliably and efficiently than do the other optimization algorithms considered. The proposed optimization model is a promising alternative approach to assisting project managers in handling MRLMP effectively.

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.

Multiple work shifts are commonly utilized in construction projects to meet project requirements. Nevertheless , evening and night shifts raise the risk of adverse events and thus must be used to the minimum extent feasible. Tradeoff optimization among project duration (time), project cost, and the utilization of evening and night work shifts while maintaining with all job logic and resource availability constraints is necessary to enhance overall construction project success. In this study, a novel approach called " Multiple Objective Sym-biotic Organisms Search " (MOSOS) to solve multiple work shifts problem is introduced. The MOSOS algorithm is new meta-heuristic based multi-objective optimization techniques inspired by the symbiotic interaction strategies that organisms use to survive in the ecosystem. A numerical case study of construction projects were studied and the performance of MOSOS is evaluated in comparison with other widely used algorithms which includes non-dominated sorting genetic algorithm II (NSGA-II), the multiple objective particle swarm optimization (MOPSO), the multiple objective differential evolution (MODE), and the multiple objective artificial bee colony (MOABC). The numerical results demonstrate MOSOS approach is a powerful search and optimization technique in finding optimization of work shift schedules that is it can assist project managers in selecting appropriate plan for project.