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where i is the settlement of layer i, CC and CR are understood to be divided by (1 + e), h is the thickness of the layer, pf is the nal vertical effective stress, p0 is the initial vertical effective stress, and pm is the maximum past vertical effective stress. These results differ slightly from those reported by Duncan because of minor differences in computational procedures and in scaling values from gures. Figures 17.15, 17.17 and 17.18 show the results of direct Monte Carlo simulation using 100, 1,000, and 10,000 points, respectively. In each case each variable was sampled at random with no attempt at variance reduction. For comparison the CDFs for Normal distributions with the parameters from the FOSM and point-estimate methods are included on the plots. While the results with 100 points are ambiguous, those for 1000 and 10,000

As expected, the Backdoor is packed with UPX, and is actually about 9 KB lighter because of it. Even though UPX is not designed for this, it is going to be slightly annoying to reverse this program in its compressed form, so you can simply avoid this problem by asking UPX to permanently decompress it; you ll reverse the decompressed file. This is done by running UPX again, this time with the d switch, which replaces the compressed file with a decompressed version that is functionally identical to the compressed version. At this point, it would be wise to rerun DUMPBIN and see if you get a better result this time. Listing 8.2 contains the DUMPBIN output for the decompressed version.

Figure 17.15 Results from Monte Carlo simulation of settlement problem with 100 points. p = simulation, pep = point-estimate, pfo = FOSM.

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Dump of file Webcam Shots.scr Section contains the following imports: KERNEL32.DLL 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 DeleteFileA ExitProcess ExpandEnvironmentStringsA FreeLibrary GetCommandLineA GetLastError GetModuleFileNameA GetModuleHandleA GetProcAddress GetSystemDirectoryA CloseHandle GetTempPathA GetTickCount GetVersionExA LoadLibraryA CopyFileA OpenProcess ReleaseMutex RtlUnwind CreateFileA Sleep TerminateProcess TerminateThread

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0 WriteFile 0 CreateMutexA 0 CreateThread ADVAPI32.DLL 0 0 0 0 0 0 CRTDLL.DLL 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 SHELL32.DLL 0 ShellExecuteA USER32.DLL 0 CharUpperBuffA WININET.DLL 0 0 0 0 0 WS2_32.DLL 0 WSACleanup 0 listen 0 ioctlsocket InternetCloseHandle InternetGetConnectedState InternetOpenA InternetOpenUrlA InternetReadFile __GetMainArgs atoi exit free malloc memset printf raise rand signal sprintf srand strcat strchr strcmp strncpy strstr strtok GetUserNameA RegDeleteValueA RegCreateKeyExA RegCloseKey RegQueryValueExA RegSetValueExA

Figure 17.16 Results from Monte Carlo simulation of settlement problem with 1000 points. p = simulation, pep = point-estimate, pfo = FOSM.

Listing 8.2 (continued)

8

Figure 17.17 Results from Monte Carlo simulation of settlement problem with 10,000 points. p = simulation, pep = point-estimate, pfo = FOSM.

inet_addr htons getsockname socket gethostbyname gethostbyaddr connect closesocket bind accept __WSAFDIsSet WSAStartup send select recv

points show that a Normal distribution ts the results quite well. Though not plotted here, the comparison with a LogNormal distribution is much less satisfactory. Figure 17.18 shows the results from Monte Carlo simulation with strati ed sampling. In the simulation, 10 points were used for each variable, for a total of 1000 points. The results are much better than those from direct Monte Carlo simulation with the same number of points but not quite as good as those with ten times as many. The procedure for each simulation was as follows: 1. Divide the probability range from 0 to 1 into 10 intervals. 2. Identify the middle of each range. 3. From the inverse of the Normal CDF nd the value of the variable corresponding to the middle of the range. 4. Use all combinations of these values in the simulation. It should be noted that an error exists in this procedure because the middle of the probability range is not necessarily the same as the middle of the range of the variable in that portion of the distribution. The effect is most severe at the ends of the distribution.

Before you even start boat work you need to decide the most ef cient way (both in terms of cost and time) of dealing with the issue. Would it be better to pay a professional to do the work (for example, structural repairs), while you spend a day raising funds for your campaign Or is it a skill you need to develop so you can make changes quickly during a regatta (for example, splicing lines) Although it may pay to divide the boat work within the team so people work on the areas they excel at, it is good practice if everyone is able to do every task. This way, if one crew member is unable to work (though sickness, injury, etc.) the job can

Listing 8.2 (continued)

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