Yun- Wu

Gravitational particle swarm

Particle swarm optimization (PSO) is inspired by social behavior of bird flocking, gravitational search algorithm (GSA) is based on the law of gravity, and both of them are related to swarm intelligence (SI). Gravitational particle swarm (GPS) is proposed where a GPS agent has attributes of GSA and PSO. GPS agents update their respective positions with PSO velocity and GSA acceleration. GPS agents, therefore, are able to exhibit PSO bird social and cognitive behaviors and motion in flight, while also reflecting the law of gravity of GSA. From results of 23 benchmark functions, GPS does significantly improve PSO and GSA, with noticeably marked improvements. This paper proposes GPS for hybridizing PSO and GSA due to the outstanding performance and interesting concepts embodied in the GPS.

Isolated particle swarm optimization with particle migration and global best adoption

Isolated particle swarm optimization (IPSO) segregates particles into several sub-swarms in order to improve the ability of the global optimization. In this study, particle migration and global best adoption (gbest adoption) are used to improve IPSO. Particle migration allows particles to travel among sub-swarms, based on the fitness of the sub-swarms. The use of gbest adoption allows sub-swarms to peep at the gbest proportionally or probably after a certain number of iterations, i.e. gbest replacing, and gbest sharing, respectively. Three well-known benchmark functions are utilized to determine the parameter settings of the IPSO. Then, 13 benchmark functions are used to study the performance of the designed IPSO. Computational experience demonstrates that the designed IPSO is superior to the original version of particle swarm optimization (PSO) in terms of the accuracy and stability of the results, when isolation phenomenon, particle migration and gbest sharing are involved.

Determining ultimate bearing capacity of shallow foundations using a genetic programming system

Three genetic programming models are developed for determining the ultimate bearing capacity of shallow foundations. The proposed genetic programming system (GPS), which comprises genetic programming (GP), weighted genetic programming (WGP), and soft-computing polynomials (SCP), simultaneously provides accurate prediction and visible formulas. Some improvements are achieved for GP and WGP. The SCP is also designed to model the ultimate bearing capacity of shallow foundations with polynomials. Laboratory experimental tests of shallow foundations on cohesionless soils are used with parameters of the angle of shearing resistance, the unit weight of the soil, and the geometry of a foundation considers depth, width, and length to determine the ultimate bearing capacity. Analytical results confirm that all GPS models perform well with acceptable prediction accuracy. Visible formulas of GPS models also facilitate parameter studies, sensitivity analysis, and application of pruning techniques. Notably, SCP gives concise representations for the ultimate bearing capacity and identifies the significant parameters. Although shear resistance angles have the largest impact on ultimate bearing capacity, foundation width and depth are also significant.

Programming squat wall strengths and tuning associated codes with pruned modular neural network

This study designed a four-layer modular neural network (MNN) to predict and program squat wall strength values. Results generated by the proposed MNN include predictions and programmed formulas that are similar in form to modular polynomials, which permit MNN programming to interpret training results in a meaningful way that offers significant advantages over famous neural networks. This study employed particle swarm optimization for MNN parameter learning and structure learning in order to prune MNN to avoid overfitting and increase programmed formula concision. To extend the uses of MNN programming, this paper further employed MNN tuning to refine existing analytical methods and codes. Case studies focused on squat wall strength analyses. Study results demonstrated that MNN programming uniquely uses a programmed formula to deliver good prediction accuracy. MNN tuning further improved the studied methods. Programmed formulas also provided insights into input parameter impacts and significant modular functions.

LCA-Based Economic Benefit Analysis for Building Integrated Photovoltaic (BIPV) Façades: A Case Study in Taiwan

ABSTRACT Solar energy is one of the major sources of alternative and green energies that humanity need now and will continue to need in the future. There are now a large number of R&D activities on solar power generation facilities and equipment around the world. Located in a subtropical region, Taiwan is rich in solar energy resources; therefore, how to effectively use and store solar energy is a research topic of great interest to Taiwan. The main purpose of this study explores the economic benefits of building-integrated photovoltaics (BIPV) facilities and equipment by analyzing the net present values (NPV) and payback period of the BIPV façade of a shopping mall in Taiwan over its lifecycle. The NPV and payback period analysis results both indicate that the BIPV façade in the case study reaches its breakeven point within 10 years of payback period and 16 years of NPV during a life cycle of 20 years. By showing BIPV investments can bring an acceptable range of benefits profits, this study hopes to provide references for promoting the photovoltaic (PV) industry.

Applying ZigBee wireless sensor and control network for bridge safety monitoring

Internet technologies bring methods to help bridge safety management, to collect data or monitor conditions in real time, and to comprehensively record or analyze the collected data of on-site conditions in real time. In this study, the wireless sensor networks and smart building technologies are adopted to help the bridge safety information transmission and management. The study proposed a bridge safety–monitoring system conceptual framework by applying the ZigBee wireless sensor and control technology. The conceptual framework demonstrated by a prototype includes four major subsystems: (1) monitoring units; (2) photovoltaic units; (3) wireless communication system; and (4) bridge safety–monitoring server system. This system can monitor and analyze in real time the conditions of a bridge and its environment, including the waters levels nearby, pipelines, air, and other safety conditions. The detected data and images are transmitted to the server and database for users to have real-time monitoring of the bridge conditions via mobile telecommunication devices.

Development of an IoT-based bridge safety monitoring system

In this study, an IoT-based bridge safety monitoring system is developed using the ZigBee technology. This system is composed of: (1) monitoring devices installed in the bridge environment; (2) communication devices connecting the bridge monitoring devices and the cloud-based server; (3) a dynamic database that stores bridge condition data; and (4) a cloud-based server that calculates and analyzes data transmitted from the monitoring devices. This system can monitor and analyze in real time the conditions of a bridge and its environment, including the waters levels nearby, pipelines, air and other safety conditions. The detected data and images are transmitted to the server and database for users to have real-time monitoring of the bridge conditions via mobile telecommunication devices.

The impacts of sociopolitical instability on construction dimension

Most existing studies focus on establishing models of interdependence between the construction sector and performance of the national economy, this issue was initiated from financial markets in this study. By adopting an autoregressive conditional jump intensity model, this study examined how various unpredictable events impact the construction sector. Dependence on the arrival process governing jump events in a discrete-time setting was explored, in addition to the behaviour of the fundamental properties of structure index during periods of distinct events. Although the market efficiency hypothesis still holds, results of this study demonstrate that acquisition announcements are perceived as discrete sudden shocks by the stock market.