本文關(guān)鍵詞： 多屬性決策 滑坡治理方案優(yōu)選 AOWEA算子 風(fēng)險(xiǎn)態(tài)度因子 變形監(jiān)測分析與預(yù)測　出處：《長安大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：滑坡治理是災(zāi)害治理工程中的熱點(diǎn),選擇經(jīng)濟(jì)、合理、有效的滑坡治理方案是開展滑坡治理工程的重要基礎(chǔ),但在實(shí)際滑坡防治中由于治理方案選擇不合理而導(dǎo)致治理效果不佳,后期進(jìn)行應(yīng)急治理和方案變更等造成極大的資源浪費(fèi)。因此,在滑坡治理工程開展之前如何選擇最優(yōu)治理方案尤為重要。本文以某滑坡在初設(shè)階段提出的四種治理方案為基礎(chǔ),分別利用AOWEA算子和風(fēng)險(xiǎn)態(tài)度因子對這四種方案進(jìn)行了優(yōu)選研究,并對第二種方法的風(fēng)險(xiǎn)態(tài)度因子α進(jìn)行了敏感性分析;然后根據(jù)地表變形監(jiān)測數(shù)據(jù),對實(shí)際執(zhí)行治理方案進(jìn)行了危險(xiǎn)性和工程治理評價(jià);最后基于Elman神經(jīng)網(wǎng)絡(luò),根據(jù)地表變形監(jiān)測數(shù)據(jù)對該滑坡變形進(jìn)行了預(yù)測。本文的主要研究成果如下:(1)基于AOWEA算子建立滑坡治理方案優(yōu)選模型,求解得出滑坡治理初步設(shè)計(jì)方案優(yōu)選排序結(jié)果:S1(?)S3(?)S4(?)S2。(2)基于風(fēng)險(xiǎn)態(tài)度因子α建立滑坡治理方案優(yōu)選模型,求解得出滑坡治理初步設(shè)計(jì)方案優(yōu)選排序結(jié)果:當(dāng)α∈[-0.20,0.50]時(shí),S1(?)S2(?)S3(?)S4; 當(dāng)α∈[-0.50,-0.20)時(shí),S4的綜合屬性效用值依次超過S3、S2和S1,且當(dāng)α = -0.5時(shí),S4(?)S3(?)S2(?)S1。(3)風(fēng)險(xiǎn)態(tài)度影響下的方案排序敏感性分析結(jié)果:S3和S4的敏感區(qū)間為α ∈ [-0.21,-0.20],S2和S4的敏感區(qū)間為α ∈ [-0.34,-0.33],S1和S4的敏感區(qū)間為α∈ [-0.48,-0.47] 。(4)根據(jù)地表變形監(jiān)測分析對執(zhí)行治理方案進(jìn)行工程治理評價(jià),結(jié)果表明:中區(qū)滑坡地表變形累計(jì)位移、速率和加速度均較大,工程治理效果較差;南區(qū)滑坡地表變形累計(jì)位移、速率和加速度均較小,工程治理效果顯著;北區(qū)滑坡后緣變形累計(jì)位移、速率和加速度較小,但滑坡中部變形累計(jì)位移、速率和加速度均較大,工程治理效果一般。(5)基于Elman神經(jīng)網(wǎng)絡(luò),以監(jiān)測點(diǎn)J24和J29滑坡變形動(dòng)態(tài)預(yù)測為例,結(jié)果表明:J24和J29的變形預(yù)測曲線與實(shí)測值曲線趨勢基本一致,預(yù)測值與實(shí)測值的最大誤差分別為8.00%和5.84%,平均誤差分別為1.72%和1.07%。
[Abstract]:The landslide is hot, engineering disaster governance in the economic, reasonable and effective scheme of landslide governance is the important basis for landslide control project, but in the actual landslide prevention because treatment scheme selection unreasonable ineffective governance, governance and the emergency scheme change caused great waste of resources. Therefore, how to select the optimal treatment plan before the landslide development is particularly important. This paper takes a landslide in the four control scheme based on the design stage, respectively, using the AOWEA operator and the risk attitude factor of these four schemes are optimized, and the second methods of risk attitude factor sensitivity analysis is carried out; then according to the monitoring data of surface deformation, the risk evaluation of project management and actual implementation of governance scheme based on Elman neural network; finally, according to the change of surface 褰㈢洃嫻嬫暟鎹璇ユ粦鍧″彉褰㈣繘琛屼簡棰勬祴.鏈枃鐨勪富瑕佺爺絀舵垚鏋滃涓，

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