Fatigue Stress-Life Model of RC Beams Based on an Accelerated Fatigue Method

Several standard fatigue testing methods are used to determine the fatigue stress-life prediction model (S-N curve) and the endurance limit of Reinforced Concrete (RC) beams, including the application of constant cyclic tension-tension loads at different stress or strain ranges. The standard fatigue testing methods are time-consuming and expensive to perform, as a large number of specimens is needed to obtain valid results. The purpose of this paper is to examine a fatigue stress-life predication model of RC beams that are developed with an accelerated fatigue approach. This approach is based on the hypothesis of linear accumulative damage of the Palmgren–Miner rule, whereby the applied cyclic load range is linearly increased with respect to the number of cycles until the specimen fails. A three-dimensional RC beam was modeled and validated using ANSYS software. Numerical simulations were performed for the RC beam under linearly increased cyclic loading with different initial loading conditions. A fatigue stress-life model was developed that was based on the analyzed data of three specimens. The accelerated fatigue approach has a higher rate of damage accumulations than the standard testing approach. All of the analyzed specimens failed due to an unstable cracking of concrete. The developed fatigue stress-life model fits the upper 95% prediction band of RC beams that were tested under constant amplitude cyclic loading.

PS1 - 189 A Universal Predictive Model for Dose Fall-Off in MLC-Based Stereotactic Brain Radiosurgery

Predictive modeling of dose fall-off in radiosurgery could assist in clinical decision-making when prescribing a treatment plan with minimized toxicity risk. The purpose of this study is to develop a predictive dose fall-off model. Materials/Methods: We retrospectively reviewed treatment plans from 257 patients (365 lesions) with total doses ranging from 20 to 35Gy in 5 fractions. For each plan, we measured both total volume of the external contour (EXT) and BrainMinusPTV (BMP) receiving P=20% to P=80% of the prescription dose. The model has form y=Fa(PTV)b+/-delta. y=volume of EXT or BMP (cc’s); a and b are curve-fitting coefficients; PTV=total planning target volume (cc’s); F is an adjustment factor (>1) to account for number of targets; delta is the 95% prediction band. F, a, b, and delta were modeled such that dose-fall can be forecast for any PTV and dose level. Results: The model coefficients were as follows: Coefficient EXT BMP a 19927(100×P)exp(-2) 17122(100×P)exp(-2) b 0.42(100×P)exp(0.17) 0.63 F -0.0156×(100×P)+2.5517 delta 384467×(100×P)exp(-2.3159) The table can be used to determine the model for any P from 20% to 80%. Example: the EXT receiving 50%, P=0.5, a=8.0, b=0.82, F=1.8, delta=45. Thus, EXT-50=8(PTV0.82) or 1.8×8(PTV0.82) for 1-3 or >3 targets, respectively,+/-45cc’s. The model was verified against published values of dose fall-off from linacs. Conclusion: A predictive dose fall-off model was generated for linac-based radiosurgery. The model can be used for quality assurance or for inter-institutional comparisons. Ongoing work is being conducted to extend the model to a SRS cones system.

Targeting tumour markers: What makes therapy discriminating?

13129 Background: Therapy in inoperable non-small cell lung cancer has limited effectiveness. New therapy directed at tumour markers is attractive but limited by marker prevalence and intrinsic effectiveness. Prior work (Abstract 9639, 2005 PASCO) on 191 trials of first line therapy shows a “typical drug” needs at least a 35% difference in response rate, treatment versus control, to predict a survival gain. The current work describes the characteristics of targeted drugs likely to predict this gain at smaller differences, i.e., more discriminating drugs. We determine how much greater effects on response rate and survival, compared to the typical drug, need to be to predict this survival gain. Methods: By using 1.0, 1.1, 1.2, 2.0-fold increments in response rates over traditional therapy and the same increments for median survival up to that used for response, we defined 55 differently constructed targeted drugs. The drugs were all analysed at each of four marker prevalence rates, 5%, 20%, 50% and 100%, each analysis determining the response rate difference needed to predict a survival gain. For each analysis individual trial response rates were modified in one arm before applying linear regression. The response rate difference to predict a survival gain is the portion of the prediction band fully exceeding zero. Results: Improvements in discrimination were rare and highly sensitive to marker prevalence. Little improvement occurred for any drug with 5% or 20% prevalence, and if no survival improvement occurred, discrimination worsened (up to 90%). If both tumour response and survival were doubled, discrimination improved to 33%, 27%, 17%, and 7% for marker prevalence of 5%, 20%, 50%, or 100%, respectively. Modest changes (×1.4, ×1.6) in response and survival only improved discrimination with 100% prevalence. Clearly improved discrimination (to 20% or less) only occurred with at least ×1.8 increase in response and survival and 50% or more prevalence. Conclusions: The results show that targeting a marker only substantially improves discrimination if it doubles both tumour response rate and median overall survival and if marker prevalence is at least 50%. This approach may help rationalize aspects of drug development in oncology. No significant financial relationships to disclose.