rm (list = ls())<\/p>\n
library(timelineS)
\nlibrary(lubridate)
\nlibrary(ggplot2)
\nlibrary(dplyr)
\nlibrary(reshape2)
\nlibrary(rPref)
\nlibrary(mco)
\nlibrary(MASS)<\/p>\n
plot.timeline <- function(lifetime, events.name, start.Date, plot.Name) {
\ndataP <- data.frame(Events = events.name, Event_Dates = ymd(start.Date) +
\nyears(lifetime))
\ntimelineS(dataP, main = plot.Name,
\nlabels = events.name, label.direction = “up”, label.position = 3)
\n}<\/p>\n
dist.Events <- function (lifetime, events, start.Date, option.Name) {
\n#sort the events
\nevents <- sort(events, decreasing = T)
\n#create the distribution of the events
\ndistribution.events <- sapply(events, seq, from = 0, to = lifetime)
\n#all events unlisted
\nall.events <- melt(distribution.events)
\ncolnames(all.events) <- c(“frequency”, “event”)
\n#sort the events ascending
\nall.events <- all.events[order(all.events$frequency),]
\n#get the unique sequence of events
\nunique.events <- all.events[!duplicated(all.events$frequency),]
\nunique.events$event[which(unique.events$frequency == 0)] <- “DC”
\n#plot the timeline
\n#plot.timeline(unique.events$frequency, unique.events$event, start.Date, option.Name)
\nreturn(unique.events)
\n}<\/p>\n
combine.lifeTimelines <- function(product.1, product.2, product.3, product.4,
\np1.dur, p2.dur, p3.dur, p4.dur) {
\nbase <- list(Names = c(), frequency = c(), duration = c())
\nbase$frequency <- sort(unique(c(product.1$frequency,
\nproduct.2$frequency,
\nproduct.3$frequency,
\nproduct.4$frequency)),
\ndecreasing = FALSE)
\nbase$Names[1] <- “DC”
\nbase$duration[1] <- 0
\nbase$Names[length(base$frequency)] <- “END”
\nbase$duration[length(base$frequency)] <- 0<\/p>\n
for(index in 2:(length(base$frequency) – 1)) {<\/p>\n
phase.1 <- product.1$event[which(product.1$frequency ==
\nbase$frequency[index])]
\nphase.2 <- product.2$event[which(product.2$frequency ==
\nbase$frequency[index])]
\nphase.3 <- product.3$event[which(product.3$frequency ==
\nbase$frequency[index])]
\nphase.4 <- product.4$event[which(product.4$frequency ==
\nbase$frequency[index])]<\/p>\n
list <- c(phase.1, phase.2, phase.3, phase.4)
\ndist <- c(p1.dur, p2.dur, p3.dur, p4.dur)<\/p>\n
l <- c()
\nd <- c(0)<\/p>\n
for (i in 1:length(list)){
\nif (length(list[i] != 0)){
\nl <- append(l, list[i])
\nx <- dist[which(names(dist) == list[i])]
\nd <- append(d, x)
\n}
\nelse{
\nd <- append(d, 0)
\n}
\n}<\/p>\n
base$Names[index] <- paste(l, collapse = “&”)
\nbase$duration[index] <- max(d)
\n}
\nreturn(base)
\n}<\/p>\n
par(mfrow = c(2, 1))
\nstart.Date = “2020-01-01”
\nlifetime <- 70<\/p>\n
events.Op1.Metro = c(RR.m = 28, M.m = 5, FER.m = 15, CS.m = 50, END=lifetime)\u00a0\u00a0 ## Re-read!!!!
\nduration.ev.Metro <- c(RR.m = 7, M.m = 2, FER.m = 21)<\/p>\n
events.Op1.Bridge = c(SDO.b = 18, M.b = 6, DR.b = 36, END = lifetime)
\nduration.ev.Bridge <- c(SDO.b = 4, M.b = 2, DR.b = 32)<\/p>\n
events.Op1.Building <- c(SDR.bu = 15, M.bu = 10, DR.bu = 50, END = lifetime)
\nduration.ev.Building <- c(SDO.bu = 5, M.bu = 6, DR.bu = 11)<\/p>\n
events.Op1.Water <- c(PI.w = 2, M.w = 10, PR.w = 55, END = lifetime)
\nduration.ev.Water <- c(PI.w = 8, M.w = 12, PR.w = 31)<\/p>\n
design.Options.Metro <- list(desing.Op1.Metro <- “Metro”)
\ndesign.Options.Bridge <- list(desing.Op1.Bridge <- “Bridge”)
\ndesign.Options.Building <- list(desing.Op1.Building <- “Building”)
\ndesign.Options.Water <- list(desing.Op1.Water <- “Water”)<\/p>\n
maintenance.Metro <- dist.Events(lifetime, events.Op1.Metro, start.Date,
\ndesign.Options.Metro)
\nmaintenance.Bridge <- dist.Events(lifetime, events.Op1.Bridge, start.Date,
\ndesign.Options.Bridge)
\nmaintenance.Building <- dist.Events(lifetime, events.Op1.Building, start.Date,
\ndesign.Options.Building)
\nmaintenance.Water <- dist.Events(lifetime, events.Op1.Water, start.Date,
\ndesign.Options.Water)<\/p>\n
integrated.interv <- combine.lifeTimelines(maintenance.Metro, maintenance.Bridge,
\nmaintenance.Building, maintenance.Water,
\nduration.ev.Metro, duration.ev.Bridge,
\nduration.ev.Building, duration.ev.Water)
\nsum(integrated.interv$duration)<\/p>\n
design.explore <- function(events1, events2, events3, events4) {
\nresults <- c()
\nfor(i in 1: dim(events1)[1]) {
\nev1 <- unlist(events1[i, ])
\ndist.1 <- dist.Events(lifetime, ev1, start.Date, desing.Op1)
\ndur.ev1 <- ev1\/2
\nfor (j in 1: dim(events1)[1]) {
\nev2 <- unlist(events2[j, ])
\ndist.2 <- dist.Events(lifetime, ev2, start.Date, design.Options.Road)
\ndur.ev2 <- ev2\/2
\nfor (k in 1: dim(events1)[1]) {
\nev3 <- unlist(events3[k, ])
\ndist.3 <- dist.Events(lifetime, ev3, start.Date, design.Options.Road)
\ndur.ev3 <- ev3\/2
\nfor (l in 1: dim(events1)[1]) {
\nev4 <- unlist(events4[k, ])
\ndist.4 <- dist.Events(lifetime, ev4, start.Date, design.Options.Road)
\ndur.ev4 <- ev4\/2
\ncombined.lifetime <- combine.lifeTimelines(dist.1, dist.2, dist.3, dist.4,
\ndur.ev1, dur.ev2, dur.ev3, dur.ev4)
\nmin.dist.int <- min(abs(combined.lifetime$frequency[1:(length(combined.lifetime$frequency) – 1)] –
\ncombined.lifetime$frequency[2:length(combined.lifetime$frequency)]))
\nresults <- rbind(results, c(ev1, ev2, ev3, ev4, dur = sum(combined.lifetime$duration), dist.inter = min.dist.int))
\n}
\n}
\n}
\n}
\nreturn(as.data.frame(results))
\n}<\/p>\n
n.grid <- 2<\/p>\n
events.Op1.Metro = c(RR.m = 28, M.m = 5, FER.m = 15, CS.m = 50, END=lifetime)\u00a0\u00a0 ## Re-read!!!!
\nduration.ev.Metro <- c(RR.m = 7, M.m = 2, FER.m = 21)<\/p>\n
events.Op1.Bridge = c(SDO.b = 18, M.b = 6, DR.b = 36, END = lifetime)
\nduration.ev.Bridge <- c(SDO.b = 4, M.b = 2, DR.b = 32)<\/p>\n
events.Op1.Building <- c(SDR.bu = 15, M.bu = 10, DR.bu = 50, END = lifetime)
\nduration.ev.Building <- c(SDO.bu = 5, M.bu = 6, DR.bu = 11)<\/p>\n
events.Op1.Water <- c(PI.w = 2, M.w = 10, PR.w = 55, END = lifetime)
\nduration.ev.Water <- c(PI.w = 8, M.w = 12, PR.w = 31)<\/p>\n
events.grid.metro <- expand.grid(SDO.m = sample(seq(10,20, by = 1), n.grid),
\nM.m = sample(seq(3,8,1), n.grid),
\nDR.m = sample(seq(20, 35, 1), n.grid))<\/p>\n
events.grid.bridge <- expand.grid(SDO.br = sample(seq(12, 25, by = 1), n.grid),
\nM.br = sample(seq(5,10,1), n.grid),
\nR.br = sample(seq(27, 37, 1), n.grid))<\/p>\n
events.grid.building <- expand.grid(SDO.bu = sample(seq(10,20, by = 1), n.grid),
\nM.bu = sample(seq(3,8,1), n.grid),
\nDR.bu = sample(seq(20, 35, 1), n.grid))<\/p>\n
events.grid.water <- expand.grid(SDO.w = sample(seq(12, 25, by = 1), n.grid),
\nM.w = sample(seq(5,10,1), n.grid),
\nR.w = sample(seq(27, 37, 1), n.grid))<\/p>\n
response.space <- design.explore(events.grid.metro, events.grid.bridge, events.grid.building, events.grid.water)<\/p>\n
p <- low(dur)* high(dist.inter)
\nsky <- psel(response.space, p)<\/p>\n
pareto2 <- psel(response.space, p, top = nrow(response.space))<\/p>\n
ggplot(response.space, aes(x = dur, y = dist.inter)) +
\ngeom_point(shape = 21) +
\ngeom_point(data = pareto2, size = 3, aes(color = factor(pareto2$.level)))<\/p>\n
show_front <- function(pref) {
\nplot(response.space$dur, response.space$dist.inter)
\nsky <- psel(response.space, pref)
\nplot_front(response.space, pref, col = rgb(0, 0, 1))
\npoints(sky$dur, sky$dist.inter, lwd = 3)
\n}<\/p>\n
show_front(p)<\/p>\n
p <- high(dur) * low(dist.inter)<\/p>\n
show_front(p)<\/p>\n
## Metro<\/p>\n
LCI.materials <- read.csv(“LCI_Materials-5.csv”, sep=”;”, dec=”,”)<\/p>\n
LCA.station <- function(length, superstructure.Option, materials, dist.event) {
\nsubstructure.section <- 0.506
\nconv.superstructure.section <- 0.46575
\nFRP_conv.superstructure.section <- 0.46575
\nprefab.superstructure.section <- 0.3795
\nFRP_prefab.superstructure.section <- 0.3795
\nrail.unitWeight <- 54.77<\/p>\n
materials.split <- split(materials, materials$scope)<\/p>\n
s <- summary(as.factor(dist.event$event))
\ns2 <- as.data.frame(rbind(Freq = s), stringsAsFactors = F, row.names = 1:length(s))<\/p>\n
if(superstructure.Option == “CRC”) {
\nsuperstructure.volume <- length * conv.superstructure.section * 2 # multiplication by “2” is for each track line direction
\n}
\nelse if (superstructure.Option == “PRC”) {
\nsuperstructure.volume <- length * prefab.superstructure.section * 2
\n}
\nelse if (superstructure.Option == “CFRC”) {
\nsuperstructure.volume <- length * FRP_conv.superstructure.section * 2
\n}
\nelse if (superstructure.Option == “PFRC”) {
\nsuperstructure.volume <- length * FRP_prefab.superstructure.section * 2<\/p>\n
}<\/p>\n
interventions.sup <- s2$DC + s2$M + (if (!is.null(s2$PR)) {s2$PR} else {0}) +
\n(if (!is.null(s2$FPR)) {s2$FPR} else {0})<\/p>\n
substructure.volume <- length * substructure.section * 2
\ninterventions.sub <- s2$DC + s2$M + (if (!is.null(s2$CS)) {s2$CS} else {0})<\/p>\n
rail.weight <- length * rail.unitWeight * 2 * 2 #multiplication by 4 is because there are 2 rail on each direction
\ninterventions.rail <- s2$DC + s2$RR + s2$M + (if (!is.null(s2$FER)) {s2$FER} else {0})<\/p>\n
rail <- mutate(materials.split$Rail, metro.Q = rail.weight, interventions = interventions.rail)
\nsuperstructure <- mutate(materials.split[[superstructure.Option]], metro.Q = superstructure.volume, interventions = interventions.sup)
\nsubstructure <- mutate(materials.split[[“CRC”]], metro.Q = substructure.volume, interventions = interventions.sub)<\/p>\n
LCA.matrix <- rbind(substructure, superstructure, rail)<\/p>\n
LCA.matrix <- mutate(LCA.matrix, TotalMaterials.Q = quantities * metro.Q \/ 1000,
\nmaterials.LC = TotalMaterials.Q * interventions,
\nEnergy.LC = materials.LC * energy,
\nCO2.LC = materials.LC * CO2 * 1000,
\nNOx.LC = materials.LC * NOX * 1000,
\nSO2.LC = materials.LC * SO2 * 1000,
\nTotalCost.LC = materials.LC * UnitPrice * 1000)<\/p>\n
LCA.results <- list(Energy = sum(LCA.matrix$Energy.LC),
\nCO2 = sum(LCA.matrix$CO2.LC),
\nNOx = sum(LCA.matrix$NOx.LC),
\nSO2 = sum(LCA.matrix$SO2.LC),
\nCost = sum(LCA.matrix$TotalCost.LC))
\nreturn(LCA.results)
\n}<\/p>\n
metro.length <- 80<\/p>\n
# chosen.Superstructure.Option <- readline(prompt = “Enter metro station ooption index (1-4): “)
\nsuperstructure.Options <- c(“CRC”, “PRC”, “CFRC”, “PFRC”)<\/p>\n
Option.metro <- LCA.station(metro.length, superstructure.Options[2], LCI.materials, maintenance.Metro)<\/p>\n
## Bridge<\/p>\n
LCA.bridge <- function(length, width, height, thickness, girder.Option, deck.Option, materials, dist.event) {
\nsteel.girders.unitWeight <- 317 #the weight for HEM800 steel profile
\nasphalt.Q <- length * width * thickness<\/p>\n
materials.split <- split(materials, materials$scope)<\/p>\n
s <- summary(as.factor(dist.event$event))
\ns2 <- as.data.frame(rbind(Freq = s), stringsAsFactors=F, row.names = 1:length(s))<\/p>\n
# calculate the volume of the deck based on different materials strategies
\nif(deck.Option == “RC”) {
\ndeck.volume <- length * width * height
\ninterventions.deck <- s2$DC + s2$DR + 0.25 * s2$SDO + 0.35 * (if (!is.null(s2$PR)) {s2$PR}
\nelse {0})
\n} else if (deck.Option == “steel”) {
\ndeck.volume <-\u00a0\u00a0 \u00a00.1 * length * width * height
\ninterventions.deck <- s2$DC + s2$DR + 0.25 * s2$SDO + 0.35 * (if (!is.null(s2$PR)) {s2$PR}
\nelse {0})<\/p>\n
} else if (deck.Option == “rsteel”) {
\ndeck.volume <- 0.1 * length * width * height
\ninterventions.deck <- s2$DC + s2$DR + 0.25 * s2$SDO + 0.35 * (if (!is.null(s2$PR)) {s2$PR}
\nelse {0})
\n}
\n#girder options
\nif (girder.Option == “steel”) {
\nn <- round(width \/ 3, 0)
\ngirders.V <- n * steel.girders.unitWeight * length
\ninterventions.girders <- s2$DC + s2$DR + 0.25 * s2$SDO + 0.35 * (if (!is.null(s2$PR)) {s2$PR}
\nelse {0})<\/p>\n
} else if (girder.Option == “rsteel”) {
\nn <- round(width \/ 3, 0)
\ngirders.V <- n * steel.girders.unitWeight * length
\ninterventions.girders <- s2$DC + s2$DR + 0.25 * s2$SDO + 0.35 * (if (!is.null(s2$PR)) {s2$PR}
\nelse {0}) }<\/p>\n
asph.interventions = length(dist.event$event)-1
\nasphalt <- mutate(materials.split$asphalt, bridge.Q = asphalt.Q, interventions = asph.interventions)
\ndeck <- mutate(materials.split[[deck.Option]],\u00a0\u00a0 \u00a0bridge.Q = deck.volume, interventions = interventions.deck)
\ngirders <- mutate(materials.split[[girder.Option]], bridge.Q = girders.V, interventions = interventions.girders)
\nLCA.matrix <- rbind(deck, girders, asphalt)<\/p>\n
# bridge.Q in m^3 or t (steel)
\nLCA.matrix <- mutate(LCA.matrix, TotalMaterials.Q = quantities * bridge.Q,
\nmaterials.LC = TotalMaterials.Q * interventions,
\nEnergy.LC = materials.LC * energy,
\nCO2.LC = materials.LC * CO2,
\nCost.LC = materials.LC * cost)
\nLCA.results <- list(Energy = sum(LCA.matrix$Energy.LC),
\nCO2 = sum(LCA.matrix$CO2.LC),
\nCost = sum(LCA.matrix$Cost.LC))
\nreturn(LCA.results)
\n}<\/p>\n
bridge.length <- 30 # units: m
\nbridge.width <- 10 #units m
\nbridge.depth <- 0.25 #units m
\nbridge_asphalt.tk <- 0.12 #units m<\/p>\n
girder.Options <- c(“steel”, “rsteel”)
\ndeck.options <- c(“RC”, “steel”, “rsteel”)<\/p>\n
#chosen.Bridge_deck.Option <- readline(prompt = “Enter bridge deck option index (1-3): “)
\n#chosen.Bridge_girder.Option <- readline(prompt = “Enter bridge girder option index (1-2): “)<\/p>\n
Option.bridge <- LCA.bridge(bridge.length, bridge.width, bridge.depth, bridge_asphalt.tk,
\ngirder.Options[2], deck.options[2], LCI.materials, maintenance.Bridge)<\/p>\n
## Building<\/p>\n
LCA.pile <- function(length, width, depth, th, pile.Option, materials, dist.event)
\n{
\nconcpile.crs <- 0.018
\nconcpile.cap.section <- 16
\ntimber.crs <- 0.19
\nsteel.crs <- 49.50
\ncap.Q <- concpile.cap.section * th<\/p>\n
materials.split <- split(materials, materials$scope)<\/p>\n
s <- summary(as.factor(dist.event$event))
\ns2 <- as.data.frame(rbind(Freq = s), stringsAsFactors=F, row.names = 1:length(s))<\/p>\n
# calculate the volume of the pile based on different materials strategies
\nif(pile.Option == “RC”) {
\nx <- round(width \/ 5)
\ny <- round(length \/ 5)
\nn <- x * y
\npile.volume <- n * concpile.crs * depth
\ninterventions.deck <- s2$DC + s2$DR + 0.25 * s2$SDO + 0.35 * (if (!is.null(s2$PR)) {s2$PR}
\nelse {0})
\n} else if (pile.Option == “steel”) {
\nx <- round(width \/ 5)
\ny <- round(length \/ 5)
\nn <- x * y
\npile.volume <- n * timber.crs * depth
\ninterventions.deck <- s2$DC + s2$DR + 0.25 * s2$SDO + 0.35 * (if (!is.null(s2$PR)) {s2$PR}
\nelse {0})
\n} else if (pile.Option == “wood”) {
\nx <- round(width \/ 5)
\ny <- round(length \/ 5)
\nn <- x * y
\npile.volume <- n * steel.crs * depth
\ninterventions.deck <- s2$DC + s2$DR + 0.25 * s2$SDO + 0.35 * (if (!is.null(s2$PR)) {s2$PR}
\nelse {0})
\n}
\ncap.interventions = length(dist.event$event)-1
\ncap <- mutate(materials.split$RC, foun.Q = cap.Q, interventions = cap.interventions)
\npile <- mutate(materials.split[[pile.Option]], foun.Q = pile.volume, interventions =
\ninterventions.deck)
\nLCA.matrix <- rbind(pile, cap)
\nLCA.matrix <- mutate(LCA.matrix, TotalMaterials.Q = quantities * foun.Q \/ 1000,
\nmaterials.LC = TotalMaterials.Q * interventions,
\nEnergy.LC = materials.LC * energy,
\nCO2.LC = materials.LC * CO2 * 1000,
\nNOx.LC = materials.LC * NOX * 1000,
\nSO2.LC = materials.LC * SO2 * 1000)
\nLCA.results <- list(Energy = sum(LCA.matrix$Energy.LC),
\nCO2 = sum(LCA.matrix$CO2.LC),
\nNOX = sum(LCA.matrix$NOx.LC),
\nSO2 = sum(LCA.matrix$SO2.LC))
\nreturn(LCA.results)
\n}<\/p>\n
p.length <- 16 # units: m
\np.width <- 15 #units m
\np.depth <- 50 #units m
\np.th <- 0.12 #units m<\/p>\n
#chosen.pile.Option <- readline(prompt = “Enter pile option index (1-3): “)
\npile.Option <- c(“RC”, “steel”, “wood”)<\/p>\n
Option.building <- LCA.pile(p.length, p.width, p.depth, p.th, pile.Option[2], LCI.materials, maintenance.Building)<\/p>\n
## Water Distribution System
\nLCA.pipe <- function(length, time.length, externalDiameter, internalDiameter, pipe.Option, soil.option, sources, dist.event) {<\/p>\n
pipe.frequency <- 398.97 # value of corrosion mass loss rate<\/p>\n
# pipe option
\n# calculate corrosion rate<\/p>\n
if(pipe.Option == “Cast Iron”) {
\npipe.density <- 7850 #unit kg\/m3
\npipe.initial.mass <- 47.34*length #unit kg (weight\/length = 31.24 kg\/m)
\npipe.modulus <- 206000 # unit MPa
\npipe.poisson <- 0.3
\ninterventions.pipe <- s2$DC + s2$DR + 0.25 * s2$SDO + 0.35 * (if (!is.null(s2$PR)) {s2$PR}
\nelse {0})<\/p>\n
} else if (pipe.Option == “Ductile Iron”) {
\npipe.density <- 7850 #unit kg\/m3
\npipe.initial.mass <- 47.34*length #unit kg (weight\/length = 31.24 kg\/m)
\npipe.modulus <- 206000 # unit MPa
\npipe.poisson <- 0.3
\ninterventions.pipe <- s2$DC + s2$DR + 0.25 * s2$SDO + 0.35 * (if (!is.null(s2$PR)) {s2$PR}
\nelse {0})<\/p>\n
} else if (pipe.Option == “PVC”) {
\npipe.density <- 1450 #unit kg\/m3
\npipe.initial.mass <- 25.44*length #unit kg (weight\/length = 16.21 kg\/m)\u00a0\u00a0\u00a0 pipe.modulus <- 3275 # unit MPa
\npipe.poisson <- 0.3
\ninterventions.pipe <- s2$DC + s2$DR + 0.25 * s2$SDO + 0.35 * (if (!is.null(s2$PR)) {s2$PR}
\nelse {0})<\/p>\n
} else if (pipe.Option == “Concrete”) {
\npipe.external.diameter <-\u00a0 750 #unit mm
\npipe.density <- 2500 #unit kg\/m3
\npipe.initial.mass <- 227.5*length #unit kg (weight\/length = 16.21 kg\/m)
\npipe.modulus <- 43800 # unit MPa
\npipe.poisson <- 0.2
\ninterventions.pipe <- s2$DC + s2$DR + 0.25 * s2$SDO + 0.35 * (if (!is.null(s2$PR)) {s2$PR}
\nelse {0})
\n}<\/p>\n
pipe.mass.loss.rate <- (1\/(2*time.length))*(externalDiameter-sqrt(((4*pipe.frequency*pipe.initial.mass)\/(pi*pipe.density))-(internalDiameter**2)))
\n#\u00a0 pipe.mass.loss.rate <- abs(pipe.mass.loss.rate)<\/p>\n
log_OTC <- filter(sources, type == soil.option)
\nlog_OTC <- select(log_OTC, OTC)<\/p>\n
log_SMZ <- filter(sources, type == soil.option)
\nlog_SMZ <- select(log_SMZ, SMZ)<\/p>\n
log_NOR <- filter(sources, type == soil.option)
\nlog_NOR <- select(log_NOR, NOR)<\/p>\n
LCA.matrix <- cbind(pipe.mass.loss.rate, log_OTC, log_SMZ, log_NOR)<\/p>\n
LCA_result <- list(pipe.mass.loss.rate,
\nlog_OTC,
\nlog_SMZ,
\nlog_NOR)<\/p>\n
return(LCA_result)
\n}<\/p>\n
pipe.length <- 10 # units: m
\npipe.time <- 20 #units years
\npipe.external.diameter <-\u00a0 0.400 #units: m
\npipe.internal.diameter <- 0.360 #unit: m<\/p>\n
# chosen.pipe.Option <- readline(prompt = “Enter pipe option index (1-4): “)
\npipe.Options <- c(“Cast Iron”, “Ductile Iron”, “PVC”, “Concrete”)
\n#pipe.density <- c(7850, 7850, 1450, 2500)
\n# chosen.soil.Option <- readline(prompt = “Enter soil option index (1-6): “)
\nsoil.option <- c(“Fluvo-aquic soil”, “Red soil”, “Yellow soil”, “Bog soil”, “Brown soil”, “Latosolic”)<\/p>\n
Option.water <- LCA.pipe(p.length, p.time, p.external.diameter, p.internal.diameter, pipe.Options[2],
\nsoil.option[2], LCI.materials, maintenance.Water)<\/p>\n
### combined<\/p>\n
integrated.Design <- as.data.frame(list(Energy = Option.metro$Energy + Option.bridge$Energy + Option.building$Energy + Option.water$Energy,
\nCO2 = Option.metro$CO2 + Option.bridge$CO2 + Option.building$CO2 + Option.water$CO2,
\nNOx = Option.metro$NOx + Option.bridge$NOx + Option.building$NOx + Option.water$NOx,
\nSO2 = Option.metro$SO2 + Option.bridge$SO2 + Option.building$SO2 + Option.water$SO2))<\/p>\n
Energy.costs <- 0.128
\nCO2.unitcost <- 26 # per metric tone
\nNOx.unitCost <- 42 # per metric tone
\nSO2.unitCosts <- 85 # per metric tone<\/p>\n
integrated.Design <- mutate(integrated.Design,
\nCosts = (Energy * Energy.costs +
\nCO2*CO2.unitcost +
\nNOx * NOx.unitCost +
\nSO2 * SO2.unitCosts)\/10^9)<\/p>\n
###<\/p>\n
##\u00a0 Multi-Objective Optimization<\/p>\n
fitness <- function(x) {<\/p>\n
z <- numeric(7)<\/p>\n
y <- expand.grid(SDO.m = x[1], M.m = x[2], DR.m = x[3], SDO.br = x[4], M.br = x[5], R.br = x[6],
\nSDO.bu = x[7], M.bu = x[8], DR.bu = x[9], SDO.w = x[10], M.w = x[11], R.w = x[12])
\ndur.ev1 <- unlist(y[1:3] \/ 2)
\ndur.ev2 <- unlist(y[4:6] \/ 2)
\ndur.ev3 <- unlist(y[7:9] \/ 2)
\ndur.ev4 <- unlist(y[10:12] \/ 2)<\/p>\n
dist.1 <- apply(y[1:3], 1, FUN = dist.Events, lifetime = lifetime, start.Date = start.Date)
\ndist.2 <- apply(y[4:6], 1, FUN = dist.Events, lifetime = lifetime, start.Date = start.Date)
\ndist.3 <- apply(y[7:9], 1, FUN = dist.Events, lifetime = lifetime, start.Date = start.Date)
\ndist.4 <- apply(y[10:12], 1, FUN = dist.Events, lifetime = lifetime, start.Date = start.Date)<\/p>\n
results <- combine.lifeTimelines(dist.1[[1]], dist.2[[1]], dist.3[[1]], dist.4[[1]],
\ndur.ev1, dur.ev2, dur.ev3, dur.ev4)<\/p>\n
z[1] <- sum(results[[“duration”]])<\/p>\n
z[2] <- -min(abs(results$frequency[1:(length(results$frequency) – 1)] –
\nresults$frequency[2:length(results$frequency)]))<\/p>\n
# Metro features
\nmetro.length <- 80<\/p>\n
chosen.Superstructure.Option <- readline(prompt = “Enter metro station ooption index (1-4): “)
\nsuperstructure.Options <- c(“CRC”, “PRC”, “CFRC”, “PFRC”)<\/p>\n
product.1.metro <- LCA.station(metro.length, superstructure.Options[chosen.Superstructure.Option],
\nLCI.materials, dist.1[[1]])<\/p>\n
# Bridge features
\nbridge.length <- 30 # units: m
\nbridge.width <- 10 #units m
\nbridge.depth <- 0.25 #units m
\nbridge_asphalt.tk <- 0.12 #units m<\/p>\n
girder.Options <- c(“steel”, “rsteel”)
\ndeck.options <- c(“RC”, “steel”, “rsteel”)<\/p>\n
chosen.Bridge_deck.Option <- readline(prompt = “Enter bridge deck option index (1-3): “)
\nchosen.Bridge_girder.Option <- readline(prompt = “Enter bridge girder option index (1-2): “)<\/p>\n
product.2.bridge <- LCA.bridge(bridge.length, bridge.width, bridge.depth, bridge_asphalt.tk,
\ngirder.Options[chosen.Bridge_girder.Option],
\ndeck.options[chosen.Bridge_deck.Option], LCI.materials, dist.2[[1]])<\/p>\n
# Building features<\/p>\n
p.length <- 16 # units: m
\np.width <- 15 #units m
\np.depth <- 50 #units m
\np.th <- 0.12 #units m<\/p>\n
chosen.pile.Option <- readline(prompt = “Enter pile option index (1-3): “)
\npile.Option <- c(“RC”, “steel”, “wood”)<\/p>\n
product.3.building <- LCA.pile(p.length, p.width, p.depth, p.th, pile.Option[chosen.pile.Option], LCI.materials, dist.3[[1]])<\/p>\n
# Water_Distribution features
\npipe.length <- 10 # units: m
\npipe.time <- 20 #units years
\npipe.external.diameter <-\u00a0 0.400 #units: m
\npipe.internal.diameter <- 0.360 #unit: m<\/p>\n
chosen.pipe.Option <- readline(prompt = “Enter pipe option index (1-4): “)
\npipe.Options <- c(“Cast Iron”, “Ductile Iron”, “PVC”, “Concrete”)
\n#pipe.density <- c(7850, 7850, 1450, 2500)
\nchosen.soil.Option <- readline(prompt = “Enter soil option index (1-6): “)
\nsoil.option <- c(“Fluvo-aquic soil”, “Red soil”, “Yellow soil”, “Bog soil”, “Brown soil”, “Latosolic”)<\/p>\n
product.4.water <- LCA.pipe(p.length, p.time, p.external.diameter, p.internal.diameter, pipe.Options[chosen.pipe.Option],
\nsoil.option[chosen.soil.Option], LCI.materials, dist.4[[1]])<\/p>\n
# Unit Prices
\nEnergy.costs <- 0.128
\nCO2.unitcost <- 26
\nNOx.unitCost <- 42
\nSO2.unitCosts <- 85<\/p>\n
## Combination<\/p>\n
integrated.system <- as.data.frame(list(Energy = Option.metro$Energy + Option.bridge$Energy + Option.building$Energy + Option.water$Energy,
\nCO2 = Option.metro$CO2 + Option.bridge$CO2 + Option.building$CO2 + Option.water$CO2,
\nNOx = Option.metro$NOx + Option.bridge$NOx + Option.building$NOx + Option.water$NOx,
\nSO2 = Option.metro$SO2 + Option.bridge$SO2 + Option.building$SO2 + Option.water$SO2))<\/p>\n
integrated.system <- mutate(integrated.system,
\nCosts = (Energy * Energy.costs +
\nCO2*CO2.unitcost +
\nNOx * NOx.unitCost +
\nSO2 * SO2.unitCosts)\/10^9)<\/p>\n
z[3] <- integrated.system$Energy
\nz[4] <- integrated.system$CO2
\nz[5] <- integrated.system$NOx
\nz[6] <- integrated.system$SO2
\nz[7] <- integrated.system$Costs<\/p>\n
return(z)
\n}<\/p>\n
r2 <- nsga2(fitness, idim = 7, odim = 7,
\ngenerations=5, popsize=4,
\nlower.bounds=c(10, 3, 20, 12, 4, 27, 25),
\nupper.bounds= c(20, 8, 35, 25, 10, 37, 45))<\/p>\n
r2 <- nsga2(fitness, idim = 7, odim = 7,
\ngenerations=10, popsize=100,
\nlower.bounds=c(10, 3, 20, 12, 4, 27, 25),
\nupper.bounds= c(20, 8, 35, 25, 10, 37, 45))<\/p>\n
r2Results <- as.data.frame(r2$value)
\noutNames <- c(“duration”, “interv.dist”, “energy”, “co2”, “nox”, “so2”, “cost” )
\ncolnames(r2Results) <- outNames<\/p>\n
pareto3 <- as.data.frame(paretoFront(r2))
\ncolnames(pareto3) <- outNames<\/p>\n
ggplot(r2Results, aes(x = duration, y = cost)) + geom_point(shape = 21) +
\ngeom_point(data = pareto3, size = 3, color=”red”) +
\ngeom_line(data = pareto3, color=”blue”)<\/p>\n
input.params <- round(r2$par, 0)
\nall.results <- cbind(input.params, r2Results, r2$pareto.optimal)
\ncolnames(all.results) <- c(“SDO”, “M”, “DR”,
\n“SDO.r”, “M.r”, “R.r”,
\n“b.width”, “duration”, “interv.dist”,
\n“energy”, “co2”, “nox”, “so2”, “cost”,
\n“pareto”)
\npar(mfrow = c(1,1))
\nparcoord(all.results[, 1:14], var.label = T,
\ncol = ifelse(all.results$pareto == TRUE, “indianred”, “skyblue2”),
\nlty = ifelse(all.results$pareto == TRUE, 1, 3),
\nlwd = ifelse(all.results$pareto == TRUE, 3, 1))<\/p>\n","protected":false},"excerpt":{"rendered":"
rm (list = ls()) library(timelineS) library(lubridate) library(ggplot2) library(dplyr) library(reshape2) library(rPref) library(mco) library(MASS) plot.timeline <- function(lifetime, events.name, start.Date, plot.Name) { dataP <- data.frame(Events = events.name, Event_Dates = ymd(start.Date) + years(lifetime)) timelineS(dataP, main = plot.Name, labels =Continue reading<\/a><\/p>\n","protected":false},"author":175,"featured_media":0,"parent":11365,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","meta":{"footnotes":""},"class_list":["post-15143","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=\/wp\/v2\/pages\/15143","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=\/wp\/v2\/users\/175"}],"replies":[{"embeddable":true,"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=15143"}],"version-history":[{"count":1,"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=\/wp\/v2\/pages\/15143\/revisions"}],"predecessor-version":[{"id":15144,"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=\/wp\/v2\/pages\/15143\/revisions\/15144"}],"up":[{"embeddable":true,"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=\/wp\/v2\/pages\/11365"}],"wp:attachment":[{"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=15143"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}