An enhanced shuffled Shepherd Optimization Algorithm for optimal design of large-scale space structures

Shuffled Shepherd Optimization Algorithm (SSAO) is a swarm intelligence-based optimizer inspired by the herding behavior of shepherds in nature. SSOA may suffer from some shortcomings, including being trapped in a local optimum and starting from a random population without prior knowledge. This study aims to enhance the performance of the SSOA by incorporating two efficient devices. The first device is utilized from the Opposition-Based Learning (OBL) approach to improve the initialization phase of the algorithm. The second device is incorporated a solution generator in the cyclic body of the SSOA based on the statistical results of the solutions. This feature is the so-called statistically regenerated stepsize. The proposed devices provide a good balance between exploration and exploitation capability of the algorithm and reduce the probability of getting tapped in a local optimum. The viability of the proposed Enhanced Shuffled Shepherd Optimization Algorithm (ESSOA) is demonstrated through three large-scale design examples. ESSOA is compared to the standard SSOA and some other existing metaheuristic algorithms. The optimization results reveal the competence and robustness of the ESSOA for optimal design of the large-scale space structures.

     

Interpreting SuperPAVE PG test results with confidence intervals

The innovative method to interpret SuperPAVE PG test results using regression analysis and confidence intervals is evaluated in this research. Till now, most of researches and conclusions in the field of bitumen rheological aspects are based on the mean values of test results. Using the mean values does not show the reliability of the test results and in many cases two bitumen with the same PG show different performances, due to different data variances. In this study the confidence intervals method is implemented to interpret the results of SuperPAVE PG tests and notify that the mean values of the variables are occasionally misleading and always some uncertainty exists when using bitumen. This study presents a new formulation that quantifies the uncertainties in bitumen behavior to obtain the true temperature grade of the bitumen with different confidence levels (75% and 95%). Two samples of bitumen with same PG were evaluated with this formulation and the interesting results were found. The behavior of these samples with same PG was significantly different regarding the higher confidence intervals.     

Performance monitoring of Geosynthetic Reinforced Soil Integrated Bridge System (GRS-IBS) in Louisiana

The Geosynthetic Reinforced Soil (GRS) Integrated Bridge System (IBS) is an alternative design method to the conventional bridge support technology. Closely spaced layers of geosynthetic reinforcement and compacted granular fill material can provide direct bearing support for structural bridge members if designed and constructed properly. This new technology has a number of advantages including reduced construction time and cost, generally fewer construction difficulties, and easier maintenance over the life cycle of the structure. These advantages have led to a significant increase in the rate of construction of GRS-IBS structures in recent years. This paper presents details on the instrumentation plan, short-term behavior monitoring, and experiences gained from the implementation of the first GRS-IBS project in Louisiana. The monitoring program consisted of measuring bridge deformations, settlements, strains along the reinforcement, vertical and horizontal stresses within the abutment, and pore water pressures. In this paper, the performance of instrumentation sensors was evaluated to improve future instrumentation programs. Measurements from the instrumentations also provide valuable information to evaluate the design procedure and the performance of GRS-IBS bridges. The instrumentation readings showed that the magnitude and distribution of strains along the reinforcements vary with depth. The locus of maximum strains in the abutment varied by the surcharge load and time that did not corresponds to the (45+?/2) line, especially after the placement of steel girders. A comparison was made between the measured and theoretical value of thrust forces on the facing wall. The results indicated that the predicted loads by the bin pressure theory were close to the measured loads in the lower level of abutment. However, the bin pressure theory under predicted the thrust loads in the upper layers with reduced reinforcement spacing. In general, the overall performance of the GRS-IBS was within acceptable tolerance in terms of measured strains, stresses, settlements and deformations.     

Improving Consumer Satisfaction in Water Distribution Networks Through Optimal Use of Auxiliary Tanks (A Case Study of Kashan City,Iran)

Temporal and spatial variations in pressure may lead to consumer dissatisfaction and distrust of water distribution networks when it comes to reliable performance. Pressure management is a set of programs and operations conducted in water distribution networks to adjust the pressure. Constructing new auxiliary tanks in proper locations at the best height for the area they serve minimizes the pressure fluctuations. Additionally, chlorine is often injected in the reservoirs and tanks to improve the water quality. The goal of this research was to improve the condition of the network by adding auxiliary tanks with appropriate locations, heights and chlorine concentration. An optimization model is prepared to optimize consumer satisfaction, water quality and the relevant costs as objective functions. The performance of the models are evaluated by a selected case study; and the objectives are optimized in three scenarios. Using the proposed model in a water distribution network, a trade-off diagram of reliability and costs is obtained, that lets the decision makers select the proper options considering the available fund. A new indicator, the consumer satisfaction index, is also proposed as a way to evaluate the performance of water distribution networks.