NFPA-EconomicImpactFire-case1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NFPA-CBA Example 1 : Single-family dwelling (RES1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import packages\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "This Notebook presents an example application of the prototype methodology for cost-benefit analysis. This case study considers a single-family dwelling. The benefit of sprinkler installation is investigated. The building is adapted from (Butry, 2009). For background on the model and considered input values, see the background document."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The benefit of the sprinkler system relies on an evaluation of the expected cost of fire damages (i) without sprinkler protection, and (ii) with sprinkler protection. The event tree defining the different damage scenarios for the design is visualized below."
   ]
  },
  {
   "attachments": {
    "0fa9a4f4-3eba-4811-be17-16ad493fe43f.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:0fa9a4f4-3eba-4811-be17-16ad493fe43f.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I. Input\n",
    "User input in this area.  \n",
    "Note: All monetary values in 2022 USD.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### I.1 Building characteristics\n",
    "Case derived from second example dwelling in Butry (2009). Building area is 2257 ft2 (210 m2). Two stories considered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "S_area = 210 # [m2] Floor area of structure\n",
    "Construction_Cost = 1305.54 # [USD/m2] Construction cost; 1305.54 evaluated through (RSMeans, 2022), including dectector cost\n",
    "Demol_cost = 33.53 # [USD/m2] Demolition cost; 33.53 evaluated through (RSMeans, 2022)\n",
    "Disposal_cost = 14.43 # [USD/m2] Disposal cost; 14.43 evaluated through (RSMeans, 2022)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I.2 Discount rate and obsolescence rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = 0.03 # [1/year] continuous discount rate; 3% based on (Fischer, 2014)\n",
    "o = 0    # [1/year] obsolescence rate; 0% - building obsolescence neglected"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I.3 Cost of fire protection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### I.3.a Investment cost fire protection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "c_ins_unitcost= 11.68 # [USD/m2] System installation cost; 11.68 for sprinkler installation including water supply for wet pipe sprinkler system evaluated through (RSMeans, 2022)\n",
    "c_ins = c_ins_unitcost*S_area # [USD] Total system installation cost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I.3.b Maintenance cost fire protection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "m= 0.05*c_ins  # [USD per year] annual maintenance cost; 5% of sprinkler installation cost based on (Hopkin, 2019) - maintenance cost assumed to include replacement of parts for system lifetime extension"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### I.4 Benefit of fire protection \n",
    "Benefit evaluation as reduction in cost of fire losses, assessed through flowchart listed in the Introduction.  \n",
    "See background document for discussion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I.4.a Fire frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## fire frequency\n",
    "lambda_fi= 0.00151 # [1/year] fire frequency per dwelling (ignition frequency + unsuccesful immediate suppression by occupants); 0.00151 based on (Manes and Rush, 2019) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.4.b Sprinkler reliability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "psup_sp = 0.95 # [-] probability of succesful fire suppression by sprinklers; 0.92-0.98 based on (Vassart et al., 2014)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I.4.c Fire-induced losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## valuation of risk to life\n",
    "VSL = 5.657*10**6 # [2022 USD] Value of Statistical Life (VSL); 5.657*10**6 based on (ISO 2394:2015) \n",
    "xi_in = (0.047 * VSL) # [2022 USD] valuation of average injury risk; 0.047*VSL based on (Zhuang et al., 2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "## [fatalities/fire]\n",
    "Nf_civilian = 7.4/1000 # [fatality/fire] probability of civilian fatalities; 7.4 fatalities per 1000 residential fires based on (NFPA 2022)\n",
    "Nf_firefighter_on_ground = 2.4/1e5 # [fatality/fire] probability of firefighter fireground fatality; 2.4/1e5 based on (NFPA, 2022)\n",
    "Nf_firefighter_response = 2.2/1e5 # [fatality/fire] probability of firefighter response fatality; 2.2/1e5 based on (NFPA, 2022)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "## [injuries/fire]\n",
    "Nin_civilian = 3/100 # [injury/fire] civilian injuries; 3 injuries per 100 reported fires based on (NFPA, 2022)\n",
    "Nin_firefighter_on_ground = 1.62/100 # [injury/fire] firefighter fireground injuries; 1.62 injuries per 100 reported fires based on (NFPA, 2022)\n",
    "Nin_firefighter_response = 0.37/100 # [injury/fire] firefighter response injuries; 0.37 injuries per 100 reported fires based on (NFPA, 2022)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## property loss\n",
    "S_loss_0 = 35.69 # [m2] average damage area in case of sprinkler suppression; 35.69 based on (Manes and Rush, 2019)\n",
    "S_loss_s = 4.92 # [m2] average damage area in case of sprinkler suppression; 4.92 based on (Manes and Rush, 2019)\n",
    "eps_loss_content = 1.5 # [-] property loss multiplication factor; eps_loss = ((building loss + content loss) / building loss); 1.5 based on (FEMA, 2015)\n",
    "eps_loss_indirect = 1.1 # [-] total property loss multiplication factor accounting for indirect costs; 1.1 based on (Ramachandran, 1998)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## II. Design fire risk evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### II.1 Calculation of fire scenario parameters\n",
    "Input parameter calculation from raw input data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Unit replacement cost\n",
    "Replace_cost = Construction_Cost + Demol_cost + Disposal_cost # [USD/m2] replacement cost as sum of demolition, disposal and reconstruction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### II.2 Scenario consequence evaluation\n",
    "Evaluation of consequences for identified scenarios (event tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### II.2.a Scenario \"no sprinkler suppression\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Fatalities and injuries\n",
    "# fatality rate\n",
    "Nf_0 = Nf_civilian + Nf_firefighter_on_ground + Nf_firefighter_response # [fatality/fire] total fatality rate without sprinkler suppression\n",
    "# injury rate\n",
    "Nin_0 = Nin_civilian + Nin_firefighter_on_ground + Nin_firefighter_response  # [injury/fire] total injury rate without sprinkler suppression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Valuation of fatalities and injuries\n",
    "# fatality valuation\n",
    "Df_0=Nf_0*VSL # [USD/fire] valuation expected life loss\n",
    "# injury valuation\n",
    "Din_0=Nin_0*xi_in # [USD/fire] valuation expected injuries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Material loss\n",
    "xi_d_0= S_loss_0 * Replace_cost * eps_loss_content * eps_loss_indirect # [USD] property loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Total loss valuation\n",
    "D0=Df_0+Din_0+xi_d_0 # expected value fire-induced loss, scenario \"no sprinkler suppression\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### II.2.b Scenario \"successful sprinkler suppression\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Fatalities and injuries\n",
    "# fatality rate\n",
    "# no fire-induced fatalities in case of succesful sprinkler suppression, based on (Butry, 2009); \n",
    "# only fire fighter response fatalities considered\n",
    "Nf_s = Nf_firefighter_response # [fatalities/fire] total fatality rate in case of sprinkler suppression\n",
    "\n",
    "# injury rate and valuation\n",
    "# reduction in civilian injuries in case of succesful sprinkler suppression, based on (Butry, 2009)\n",
    "# only fire fighter response fatalities considered\n",
    "redux_Nin_civ=0.57 # [-] reduction factor for civilian injuries; 57% reduction in civilian injuries, based on (Butry, 2009)\n",
    "Nin_s = (1-redux_Nin_civ)*Nin_civilian + Nin_firefighter_response # [injury/fire] total injury rate in case of sprinkler suppression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Valuation of fatalities and injuries\n",
    "# fatality valuation\n",
    "Df_s=Nf_s*VSL # [USD/fire] valuation expected life loss\n",
    "# injury valuation\n",
    "Din_s=Nin_s*xi_in # [USD/fire] valuation expected injuries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "### Material loss\n",
    "xi_d_s= S_loss_s * Replace_cost * eps_loss_content * eps_loss_indirect # [USD/fire] property loss given fire in case of succesful sprinkler suppression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Total loss valuation\n",
    "Dsup=Df_s+Din_s+xi_d_s # [USD/fire] expected value fire-induced loss, scenario \"successful sprinkler suppression\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III. PNV calculation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### III.1 Total safety system cost *C+A*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### III.1.a Investment cost, *C<sub>I</sub>*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "C_I=c_ins # PNV investment cost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III.1.b Maintenance cost, *C<sub>M</sub>*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "C_M=m/g # PNV of maintenace cost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III.1.c Obsolescence cost, *C<sub>A</sub>*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "C_A=C_I*o/g # PNV of obsolescence cost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III.1.d Total safety system cost *C+A*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "net_C=C_I+C_M+C_A # total PNV investment cost (investment cost and maintenance cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### III.2 Net benefit *B-D*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III.2.a Fire-induced losses reference design, *B*\n",
    "Reference design without sprinkler protection. Single scenario in event tree: scenario \"no sprinkler suppression\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Expected loss reference design\n",
    "Dref=D0 # [USD/fire]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "## PNV reference design\n",
    "B=Dref*lambda_fi/g # [USD] PNV fire-induced losses reference design"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III.2.b Fire-induced losses trial design, *D*\n",
    "The trial design considers the additional safety feature of a sprinkler system.  \n",
    "The sprinkler system has a reliability psup_sup.  \n",
    "The overall design performance is a combination of the scenario \"no sprinkler suppression\" (with probability 1-psup_sp) and the scenario \"successful sprinkler suppression\" (with probability psup_sp)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Expected loss design with sprinkler protection\n",
    "Dsp=(1-psup_sp)*D0+psup_sp*Dsup # [USD/fire]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "## PNV reference design\n",
    "D=Dsp*lambda_fi/g # [USD] PNV expected risk to life reference design"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III.2.c PNV net benefit, *B-D*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "netB=B-D # [USD] PNV net benefit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IV. Cost-Benefit evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BCR = 0.87 and CBR = 1.14\n",
      "The PNV is -823 [USD]\n",
      "5717.401079684167\n"
     ]
    }
   ],
   "source": [
    "BCR=netB/net_C # [-] benefit cost ratio\n",
    "Z=netB-net_C # [USD] PNV of proposed safety measure\n",
    "print(\"BCR = {0:.2f} and CBR = {1:.2f}\\nThe PNV is {2:.0f} [USD]\".format(BCR,1/BCR,Z))\n",
    "print(netB)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example 1 - Parameter study and visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Visualization of cost distribution design alternatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Design alternatives- With and without sprinklers\n",
    "\n",
    "## Investment costs\n",
    "Investment_spr=np.array([C_I,C_M,C_A]) # [USD] array of PNV investment costs for case without sprinklers (investment, maintenance, obsolescence)\n",
    "Investment_nospr=np.array([0,0,0]) # [USD] array of PNV investment costs for case without sprinklers (investment, maintenance, obsolescence)\n",
    "\n",
    "### Two scenarios (Fire suppressed by sprinkler and unsuppressed by sprinkler)\n",
    "## Scenario unsuppressed by sprinklers\n",
    "# valuation of risk to life\n",
    "Df_0=Nf_0*VSL # [USD/fire] valuation expected life loss\n",
    "Din_0=Nin_0*xi_in # [USD/fire] valuation expected injuries\n",
    "\n",
    "# valuation of material losses\n",
    "xd_direct_0= S_loss_0 * Replace_cost # [USD] direct property loss related to the building itself\n",
    "xd_contents_0 = xd_direct_0*(eps_loss_content-1) # [USD] direct property loss related to the contents\n",
    "xd_indirect_0 = xd_direct_0*(eps_loss_content) *(eps_loss_indirect-1) # indirect losses\n",
    "\n",
    "# Array of costs and PNV evaluation\n",
    "Costs_0 = np.array([xd_direct_0,xd_contents_0,xd_indirect_0,Df_0,Din_0])\n",
    "Costs_0 = Costs_0 * lambda_fi/g\n",
    "\n",
    "## Scenario suppressed by sprinklers\n",
    "# valuation of risk to life\n",
    "Df_s=Nf_s*VSL # [USD/fire] valuation expected life loss\n",
    "Din_s=Nin_s*xi_in # [USD/fire] valuation expected injuries\n",
    "\n",
    "# valuation of material losses\n",
    "xd_direct_s= S_loss_s * Replace_cost # [USD] direct property loss related to the building itself\n",
    "xd_contents_s = xd_direct_s*(eps_loss_content-1) # [USD] direct property loss related to the contents\n",
    "xd_indirect_s = xd_direct_s*(eps_loss_content) *(eps_loss_indirect-1) # indirect losses\n",
    "\n",
    "# Array of costs and PNV evaluation\n",
    "Costs_s = np.array([xd_direct_s,xd_contents_s,xd_indirect_s,Df_s,Din_s])\n",
    "Costs_s = Costs_s*lambda_fi/g\n",
    "\n",
    "### Costs per design\n",
    "## Design A, no sprinkler system, \n",
    "psup_sp = 0 # No sprinkler system present\n",
    "Costs_no_spr = (1-psup_sp) * Costs_0 + psup_sp * Costs_s\n",
    "TotalCost_no_spr=np.append(Costs_no_spr,Investment_nospr)\n",
    "\n",
    "## Design B, with sprinkler system\n",
    "psup_sp = 0.95 # [-] probability of succesful fire suppression by sprinklers; 0.95 based on (Vassart et al., 2012)\n",
    "Costs_spr = (1-psup_sp) * Costs_0 + psup_sp * Costs_s\n",
    "TotalCost_spr=np.append(Costs_spr,Investment_spr)\n",
    "\n",
    "### Visualization of cost distribution per design\n",
    "fig, ax = plt.subplots()\n",
    "labels=['no sprinkler','sprinkler']\n",
    "categories=['direct loss building','direct loss contents','indirect loss','fatalities','injuries','investment','maintenance','obsolescene']\n",
    "bar_width = 0.35\n",
    "opacity = 0.8\n",
    "\n",
    "cumCost=np.array([0,0])\n",
    "for i, cat in enumerate(categories):\n",
    "    ax.bar(labels,[TotalCost_no_spr[i],TotalCost_spr[i]],bar_width,bottom=cumCost,label=cat)\n",
    "    cumCost=cumCost+np.array([TotalCost_no_spr[i],TotalCost_spr[i]])\n",
    "\n",
    "ax.set_ylim((0,12000))\n",
    "ax.set_ylabel('PNV costs [USD]')\n",
    "plt.title('Total PNV cost of design alternative')\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[::-1], labels[::-1])\n",
    "fig.set_size_inches(10,8)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Parameter study with VSL and efficiency of sprinkler system"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Function definition PNV Z, to allow parameter study VSL,psup_sp and eps_loss_indirect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Function combining above model assumption for parameter study\n",
    "\n",
    "def Zvalue(VSL,psup_sp,eps_loss_indirect):\n",
    "    ## I. Input ##\n",
    "    ##############\n",
    "    ## I.4.c Fire-induced losses\n",
    "    xi_in = (0.047 * VSL) # [2021 USD] valuation of average injury risk; 0.047*VSL based on (Zhuang et al., 2017)\n",
    "    \n",
    "    ## II. Design fire risk evaluation ##\n",
    "    #####################################    \n",
    "    ## II.2.a Scenario \"no sprinkler suppression\"\n",
    "    ### Valuation of fatalities and injuries\n",
    "    Df_0=Nf_0*VSL # [USD/fire] valuation expected life loss\n",
    "    Din_0=Nin_0*xi_in # [USD/fire] valuation expected injuries\n",
    "    ### Material loss\n",
    "    xi_d_0= S_loss_0 * Replace_cost * eps_loss_content * eps_loss_indirect # [USD] property loss\n",
    "    ### Total loss valuation\n",
    "    D0=Df_0+Din_0+xi_d_0 # expected value fire-induced loss, scenario \"no sprinkler suppression\"\n",
    "    \n",
    "    ## II.2.b Scenario \"successful sprinkler suppression\"\n",
    "    ### Valuation of fatalities and injuries\n",
    "    Df_s=Nf_s*VSL # [USD/fire] valuation expected life loss\n",
    "    Din_s=Nin_s*xi_in # [USD/fire] valuation expected injuries\n",
    "    ### Material loss\n",
    "    xi_d_s= S_loss_s * Replace_cost * eps_loss_content * eps_loss_indirect # [USD/fire] property loss given fire in case of succesful sprinkler suppression\n",
    "    ### Total loss valuation\n",
    "    Dsup=Df_s+Din_s+xi_d_s # [USD/fire] expected value fire-induced loss, scenario \"successful sprinkler suppression\"\n",
    "    \n",
    "    ## III. PNV calculation ##\n",
    "    ##########################\n",
    "    ## III.2.a Fire-induced losses reference design, B\n",
    "    ## Expected loss reference design\n",
    "    Dref=D0 # [USD/fire]\n",
    "    ## PNV reference design\n",
    "    B=Dref*lambda_fi/g # [USD] PNV fire-induced losses reference design\n",
    "    \n",
    "    ## III.2.b Fire-induced losses trial design, D\n",
    "    ## Expected loss design with sprinkler protection\n",
    "    Dsp=(1-psup_sp)*D0+psup_sp*Dsup # [USD/fire]\n",
    "    ## PNV reference design\n",
    "    D=Dsp*lambda_fi/g # [USD] PNV expected risk to life reference design\n",
    "    \n",
    "    ## III.2.c PNV net benefit, B-D\n",
    "    netB=B-D # [USD] PNV net benefit\n",
    "    \n",
    "    ## IV. Cost-Benefit evaluation ##\n",
    "    #################################\n",
    "    Z=netB-net_C # [USD] PNV of proposed safety measure\n",
    "    \n",
    "    return Z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Example of usage function Zvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The PNV is -823 [USD]\n"
     ]
    }
   ],
   "source": [
    "## Example application of function Zvalue - verification base values\n",
    "\n",
    "# input values\n",
    "psup_sp = 0.95 # [-] probability of succesful fire suppression by sprinklers; 0.95 based on (Vassart et al., 2012)\n",
    "VSL = 5.657*10**6 # [2021 USD] Value of Statistical Life (VSL); 5.657*10**6 based on (ISO 2394:2015) \n",
    "eps_loss_indirect = 1.1 # [-] total property loss multiplication factor accounting for indirect costs; 1.1 based on (Ramachandran, 1998)\n",
    "\n",
    "# CBA \n",
    "Z=Zvalue(VSL,psup_sp,eps_loss_indirect) # [USD] PNV of proposed safety measure\n",
    "print(\"The PNV is {0:.0f} [USD]\".format(Z))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Parameter study and visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 15})\n",
    "\n",
    "VSLs = np.array([2.8, 5.7, 11.6]) *1e+6 # [USD]; note: 11.6 USD listed by (Department of Transportation, 2011)\n",
    "eta_sprinkler = np.array([0.92,0.95,0.98])\n",
    "Ind_cost = np.array([0,2,5,10,20,50,100,200,300,400,500])\n",
    "\n",
    "\n",
    "nCases = len(VSLs)*len(eta_sprinkler)\n",
    "\n",
    "m_eta_sprinkler,m_VSL = np.meshgrid(eta_sprinkler,VSLs)\n",
    "m_VSL = m_VSL.ravel();m_eta_sprinkler = m_eta_sprinkler.ravel()\n",
    "\n",
    "m_Z = np.zeros((len(Ind_cost),len(m_VSL)))\n",
    "\n",
    "for num in np.arange(len(m_VSL)):\n",
    "    m_Z[:,num] = Zvalue(m_VSL[num],m_eta_sprinkler[num],1+Ind_cost/100)\n",
    "\n",
    "# Plot the data\n",
    "fig,ax = plt.subplots(1,1)\n",
    "\n",
    "color = (['r','r','r','b','b','b','g','g','g'])\n",
    "linestyle = (['solid','dashed','dashdot','solid','dashed','dashdot','solid','dashed','dashdot'])\n",
    "\n",
    "legends = []\n",
    "for num in np.arange(len(m_VSL)):\n",
    "    ax.plot(Ind_cost,m_Z[:,num]/1e3,linestyle=linestyle[num],color=color[num])\n",
    "    legends.append('VSL = '+str(m_VSL[num]/1e6)+', $\\eta_{sprinkler}$ = '+str(m_eta_sprinkler[num]))\n",
    "\n",
    "ax.plot([0,300],[0,0],linestyle='solid',color='k',linewidth=3.0)\n",
    "ax.text(10,0.15,'Z = 0')\n",
    "ax.set_xlim([0,300])\n",
    "ax.set_yticks([-2,0,2,4,6,8,10,12,14,16])\n",
    "ax.set_xlabel('Indirect cost as percentage of direct costs [%]') \n",
    "ax.set_ylabel('Present net value, $Z$ [$10^3 $ USD]')\n",
    "ax.legend(legends,fontsize=12)\n",
    "\n",
    "fig.set_size_inches(12,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}