Abstract:
This paper presents an experimental study on prediction of the early-age creep and shrinkage of concrete with and without silica fumes, trass, ground-granulated blast-furnace slag, combinations thereof, and in uences of the proposed Iranian pozzolans. Experiments were carried out under a controlled ambient condition at a temperature of 40C and a relative humidity (RH) of 50%, and a laboratory ambient condition at a temperature of 20C and a relative humidity (RH) of 30% in order to collect the required data. Comparisons are made between ACI209-92, BS8110-1986 and CEB1970 prediction models, and an estimation model, based on 28-day results (short-term test method), using the same experimental data to predict the creep and shrinkage of the specimens at the ages of 120 and 200 days after curing. The results show that the above-mentioned models are not accurate enough for predicting the creep and shrinkage of concrete containing local Iranian pozzolans. It was also observed that the prediction, based on short-term results, would lead to more accurate creep and shrinkage predictions.

Keywords:
Creep

Pozzolanic concrete
Prediction models
Short-term test method
Shrinkage
Silica fume
Ground-granulated blast-furnace slag
Trass.

Abstract:
Every year a great amount of money is expended for the rehabilitation and reconstruction of roads and pavements in most countries. Besides, making an ideal decision on this based on the types of failures determined, takes too long. The rough-set theory is an eective tool for the analysis of information systems in a Pavement Management System (PMS) database gained by both objective and subjective methods. A rough-set based analysis acts like a knowledge engineer who sits between data and the user. This approach appears to capture information on uncertainty, imprecision and ambiguity along with precise values in a PMS database. This paper explores a new approach to the rough-set theory in a PMS database that enables pavement engineers to discover the shortest subsets of condition attributes having quality equal to the general quality of dened characteristics in the information system, to assess and describe pavement conditions, and to derive decision rules for rehabilitation and reconstruction of the pavements. To evaluate the results, the best algorithm of dened attributes in the information system is determined by making use of a stepwise linear regression method and the result is compared with rough-set ones. The results of the research indicate that the rough-set theory has a better and stronger operational capability in identifying the eective parameters for the severity evaluation of typical distresses in asphalt pavements and in decision-making for selecting the type of repair.

Keywords:
Pavement Management System (PMS)

Pavement Condition Index (PCI)
Asphalt pavement; Rough-set theory
Pavement distress.

Abstract:
Various damage indexes have been introduced in recent years incorporating dierent parameters for estimating structural damage. Amongst these indexes, Plastic Ductility and Drift have been the center of attention of standards and building codes, like FEMA-356, because of their straightforward physical interpretation and ease of calculation. In this paper, several steel moment frames have been considered and their responses have been evaluated under a set of scaled earthquakes. A group of various damage indexes, which included cumulative and non cumulative, cyclic fatigue based, deformation based and modal parameter based, has been considered and the damage to structures has been evaluated on their basis. On the other hand, the performance levels of the frames have been estimated on the basis of the FEMA-356 standard. Based on these results, the correlation between FEMA performance levels and the values of damage indexes has been studied and some polynomial equations have been proposed. These simple equations can be used to estimate the value of the damage indexes from FEMA performance levels. Also, the range of each damage index for each FEMA performance level has been determined. At last, based on the concepts underlying each damage index, the indications of some important aspects of the structure response correlated to each FEMA performance level are investigated.

Keywords:
Seismic assessment

Structural damage index
FEMA-356
Performance levels

Abstract:
Wind-induced wave characteristics are one of the most important factors in the design of coastal and marine structures. Therefore, accurate estimation of wave parameters is of considerable importance. The wave climate study can be conducted by eld measurements, empirical studies, physical modeling and numerical simulations. In this paper, the skill of a third-generation spectral model called SWAN has been evaluated in the prediction of wave parameters. The varying wind and wave climate of Lake Erie in the year 2002 has been used for evaluation of the model. The signicant wave height (Hs) and the peak spectral wave period (Tp) were the parameters employed in the study and the model has been executed in a nonstationary mode. The linear and exponential growth from wind input, four-wave nonlinear interaction, whitecapping and bottom friction have been considered in the simulation. The results of this study show that in the investigated case, the average scatter index of SWAN is about 19 percent for signicant wave height and 23 percent for the peak period. The error of the SWAN model in prediction of the wave height and period reduced about 3 percent after elimination of wave heights less than 0.5 meters. It was also found that using the cumulative steepness method for whitecapping dissipation yields worse results for signicant wave height and better results for peak spectral period estimation. After using this method, the average scatter index for the prediction of Hs increased about 5 percent and decreased more than 4 percent for Tp. It should be mentioned that the computational time required by using this method is approximately more than twice that of the Komen option.

Keywords:
Wave prediction

Numerical model
Third generation
Lake Erie.

Abstract:
The Solidication/Stabilization (S/S) of heavy metal contaminated soils using Ordinary Pontlandite Cement (OPC) is studied as a hazardous waste treatment technology. The soil-binder ratio of 8% by weight is selected for treatment by setting a justication constraint on the solidied matrix. In order to consider the physical and chemical characteristics of the solidied soil-cement mixtures, Compressive Strength, Toxicity Characteristic Leaching Procedure (TCLP), X-Ray Diraction (XRD) and Scanning Electron Microscopy (SEM) are the tests done during this research. The results indicate that despite an intensive decrease in the compressive strength of solidied soil at early curing ages, the justication constraint set on the preparation of specimens enhances hydration reactions

and the solidied matrix achieves high compressive strength after 7 curing days. The leaching metal concentration in leachates is decreased gradually by aging from 1 to 28 curing days, indicating more metal stabilization in the form of metal hydroxide precipitation, which presents itself in the developed crystalline phases of hydrated cement, as observed in XRD patterns or by absorption on CSH gel or crystals. Leaching metal concentration in leachates is lower than regulatory limits, so there is no strength concern for environmentally safe disposal. Heavy metal contamination diverts the cement hydration reactions toward production of more subsidiary products, that bear less compressive strength, instead of main hydration products. Therefore, the main crystalline phases develop irregularly and coat the aggregates poorly, as observed in SEM micrographs.

Keywords:
Solidication/stabilization
Heavy metal contaminated soils
Portland cement
Leaching; XRD
SEM.

Abstract:
The repair and strengthening of RC structures has become a major problem for civil engineers in the past few decades. To satisfy this problem, a previous method for the repair and strengthening of RC beams included bonding steel plates to the inferior structure. However, bonding steel plates to concrete presents disadvantages, including corrosion of the steel/adhesive joints and the heavy weight of the material. These problems increase installation and maintenance costs. The bonding of Fiber Reinforced Plastics (FRP) to structures provides an attractive alternative to steel plates. This material is corrosion resistant and lightweight, has a high strength-to-weight ratio and possesses nonconductive properties. The use of Fiber Reinforced Plastics (FRP) in repairing and strengthening RC beams has been researched in recent years. In particular, attaching unidirectional FRP to the tension face of RC beams has provided an increase in the stiness and load capacity of the structure. However, due to the brittle nature of unidirectional FRP, the ductility of the beam decreases. Consequently, the safety of the structure is compromised, due to the reduction in ductility. The purpose of this research is to investigate the behavior of high strength reinforced concrete beams strengthened with FRP sheets. The major test variables included the dierent layouts of CFRP sheets and the tensile reinforcement ratio. More particularly, change in the strength and ductility of the beams, as the number of FRP layers and tensile reinforcement bar ratios are altered, is investigated. Eight under-reinforced concrete beams were fabricated and tested to failure. With the exception of the control beam, one or four layers of CFRP were applied to the specimens.

Keywords:
Beams

Ductility
FRP
High strength concrete
Tensile bars

Abstract:
Long lead rainfall prediction is important in the management and operation of water resources and many models have been developed for this purpose. Each of the developed models has its special strengths and weaknesses that must be considered in real time applications. In this paper, eld and General Circulation Models (GCM) data are used with the Statistical Downscaling Model (SDSM) and the Articial Neural Network (ANN) model for long lead rainfall prediction. These models have been used for the prediction of rainfall for 5 months (from December to April) in a study area in the south eastern part of Iran. The SDSM model considers climate change scenarios using the selected climate parameters in rainfall prediction, but the ANN models are driven by observed data and do not consider physical relations between variables. The results show that SDSM outperforms the ANN model.

Keywords:
Statistical Downscaling Model (SDSM)

Articial Neural Network (ANN)
Precipitation; GCM.

Abstract:
Many topological objectives and constraints can not easily be assessed by analytical formulations. This paper introduces a number of graph theoretical operators, as suitable combinatorial tools, for discrete topology assessment. Using such an approach, the load paths from their exertion points to the support joints can be guided topologically during the optimization process. Eleven variants of the proposed method are developed for the inclusion of various constraints and/or objectives. The presented algorithms are then applied to the optimal bracing layout of multi-story frames under lateral loadings for minimal weight or static compliance. Benchmark examples from literature are treated to validate the eciency and to compare the capability of the proposed algorithms. The bracing patterns obtained from optimization are graph theoretically categorized.

Keywords:
Optimization

Topological assessment
Load path
Graph theory
Super bracing
Frame.