NFPA-EconomicImpactFire-case2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NFPA-CBA Example 2 : Warehouse building (WB)"
   ]
  },
  {
   "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 building area of 6000 m2 (a medium-sized warehouse, based on (BRE Global,2013; Cebr 2014), with groundplan of 60m x 100m). The cost-benefit of the following fire protection systems is investigated: (i) sprinkler system, and (ii) compartmentation.  \n",
    "\n",
    "The reference design has neither a sprinkler system nor compartmenation. The cost-benefit of three design alternatives are investigated: (A) presence of sprinkler system only; (B) presence of compartmenation only; (C) presence of both sprinkler system and compartmentation.  \n",
    "\n",
    "The benefit evaluation is done considering the event tree diagram visualized below. Three scenarios are identified: (I) Fire suppressed by sprinkler system; (II) Fire not suppressed by sprinklers but suppressed by the fire and rescue service; (III) Fire not suppressed by sprinklers and not suppressed by the fire and rescue service."
   ]
  },
  {
   "attachments": {
    "0b069195-a6db-455f-939f-f167ff5473ae.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:0b069195-a6db-455f-939f-f167ff5473ae.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": {},
   "source": [
    "### I.1 Building characteristics\n",
    "Medium warehouse building (6000m2). Single story assumed (Warehouse with Metal Panel/ Rigid Steel).  \n",
    "Cost evaluation of structure and the cost of fire protection measures considering (RSMeans 2022), see background document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "S_area = 6000 # [m2] Floor area of structure\n",
    "Construction_Cost = 1070.8 + 4.30 # [USD/m2] Construction cost; 1080 and Smoke detectors cost; 4.30 evaluated through (RSMeans, 2022)\n",
    "Demol_cost = 101.36 # [USD/m2] (RSMeans, 2022) Demolition cost; 101.36 evaluated through (RSMeans, 2022)\n",
    "Disposal_cost = 10.70  # [USD/m2] (RSMeans, 2022) Disposal cost; 10.70 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": {},
   "source": [
    "#### I.3.a Investment cost fire protection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## sprinkler system\n",
    "c_ins_unitcost= 61.67 # [USD/m2] System installation cost; 61.67 for sprinkler system with wet pipe water sprinkler based on (RSMeans, 2022)\n",
    "c_ins = c_ins_unitcost*S_area  # [USD] Total system installation cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Compartmentation\n",
    "# compartment design; user input (see background document)\n",
    "n_comp = np.array([1,2,3,4,6,8]) # Number of compartments considered in the warehouse Building; default: np.array([1,2,3,4,6,8])\n",
    "Comp_wall_len=np.array([0,60,120,160,220,280]) # [m] total compartment wall length for design case\n",
    "\n",
    "# compartment wall cost per m\n",
    "Cost_wall = 1050 # [USD/m] Unit cost of comparmentation wall; 1050 based on (RSMeans, 2022)\n",
    "c_comp = Cost_wall * Comp_wall_len[1:] # [USD] Total cost of compartmentation wall (note: first entry in default code is the entry \"single compartment\" of the reference design)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I.3.b Maintenance cost fire protection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## sprinkler system\n",
    "m_sp= 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\n",
    "m_com=0 * c_comp # [USD per year] annual maintenance considered for compartmentation; 0% of installation cost considered here (i.e., no maintenance cost for compartmentation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
    "<br> See background document for discussion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I.4.a Fire frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "## fire frequency\n",
    "lambda_fi= 0.00156 # [1/year] fire frequency for storage (ignition frequency + unsuccessful immediate suppression by occupants); 0.00156 based on (Manes and Rush, 2019) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I.4.b Fire barrier reliability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## sprinkler reliability\n",
    "psup_sp = 0.95 # [-] probability of succesful fire suppression by sprinklers; 0.92-0.98 based on (Vassart et al., 2012)\n",
    "\n",
    "## fire and rescue service suppression success rate\n",
    "p_frs = 0.1 # [-] probability of succesful fire suppression by fire rescue service; 0.95 based on (Vassart et al., 2012); 0.1 adopted for remote location without possibility of early FRS intervention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I.4.c Fire-induced losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "## [fatalities/fire]\n",
    "Nf_civilian = 1.5/1000 # [fatality/fire] probability of civilian fatalities; 1.5 fatalities per 1000  fires based on (NFPA 2022)\n",
    "Nf_firefighter_onground = 2.8/1e5 # [fatality/fire] probability of firefighter fireground fatality; 2.8/1e5 based on (NFPA, 2022)\n",
    "Nf_firefighter_response = 2.5/1e5 # [fatality/fire] probability of firefighter response fatality; 2.5/1e5 based on (NFPA, 2022)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## [injuries/fire]\n",
    "Ni_civilian = 1.3/100 # [injury/fire] civilian injuries; 1.3 injuries per 100 reported fires based on (NFPA, 2022)\n",
    "Ni_firefighter_onground = 16.2/1000 # [injury/fire] firefighter fireground injuries; 16.2 injuries per 1000 reported fires based on (NFPA, 2022)\n",
    "Ni_firefighter_response = 3.7/1000  # [injury/fire] firefighter response injuries; 3.7 injuries per 1000 reported fires based on (NFPA, 2022)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## property loss\n",
    "S_loss_0 = 41.30 # [m2] average damage area in case of no sprinkler suppression; 41.30 based on (Manes and Rush, 2019)\n",
    "S_loss_s = 22.59 # [m2] average damage area in case of sprinkler suppression; 22.59 based on (Manes and Rush, 2019)\n",
    "\n",
    "eps_loss_content = 2.0 # [-] property loss multiplication factor; eps_loss = ((building loss + content loss) / building loss); 2.0 based on (FEMA, 2015)\n",
    "eps_loss_indirect = 1.65 # [-] total property loss multiplication factor accounting for indirect costs; 1.65 based on (Ramachandran and Hall, 2002)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II. Design fire risk evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### II.1 Calculation of fire scenario parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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\n",
    "\n",
    "# Compartment area\n",
    "comp_area = np.array(S_area/n_comp) # Compartmentation area"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### II.2 Scenario consequence evalluation\n",
    "Evaluation of consequences for identified scenarios (event tree- Scenarios I, II and III)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### II.2.a Scenario I \"Suppression by sprinkler\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Fatalities and injuries\n",
    "# Fatalities\n",
    "Nf_sup = 0 + Nf_firefighter_response # [fatality/fire] total fatality rate with sprinkler suppression\n",
    "# Injuries\n",
    "redux_Nin_civ=0.57 # [-] reduction factor for civilian injuries; 57% reduction in civilian injuries, based on (Butry, 2009)\n",
    "Nin_sup = (1-redux_Nin_civ)*Ni_civilian + Ni_firefighter_response # [injury/fire] total injury rate with sprinkler suppression\n",
    "\n",
    "### Valuation of fatalities and injuries\n",
    "Df_sup=Nf_sup*VSL # [USD/fire] valuation expected life loss\n",
    "Din_sup = Nin_sup*xi_in # [USD/fire] valuation expected injuries\n",
    "\n",
    "### Material loss\n",
    "xi_d_sup =  S_loss_s * Replace_cost * eps_loss_content * eps_loss_indirect # [USD] property loss given fire\n",
    "\n",
    "### Total loss valuation\n",
    "D_I=Df_sup+Din_sup+xi_d_sup # [USD] expected value fire-induced loss, scenario I \"fire suppressed by sprinkler\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### II.2.b Scenario II \"Not suppressed by sprinklers, suppressed by fire and rescue service\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Fatalities and injuries\n",
    "# Fatalities\n",
    "Nf_sup_frs = Nf_civilian + Nf_firefighter_onground + Nf_firefighter_response # [fatality/fire] total fatality rate without sprinkler suppression but suprressed by fire rescue service\n",
    "# Injuries\n",
    "Nin_sup_frs = Ni_civilian + Ni_firefighter_onground + Nf_firefighter_response # [injury/fire] total injury rate without sprinkler suppression but suppressed by fire rescue service\n",
    "\n",
    "### Valuation of fatalities and injuries\n",
    "Df_sup_frs=Nf_sup_frs*VSL # [USD/fire] valuation expected life loss\n",
    "Din_sup_frs = Nin_sup_frs*xi_in # [USD/fire] valuation expected injuries\n",
    "\n",
    "### Material loss\n",
    "xi_d_sup_frs =  S_loss_0 * Replace_cost * eps_loss_content * eps_loss_indirect # [USD] property loss\n",
    "\n",
    "### Total loss valuation\n",
    "D_II=Df_sup_frs+Din_sup_frs+xi_d_sup_frs # [USD] expected value fire-induced loss, scenario II \"fire not suppressed by sprinklers but suppressed by fire and rescue service\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### II.2.c Scenario III \"Not suppressed\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Fatalities and injuries\n",
    "# Fatalities\n",
    "Nf_unsup_frs = Nf_civilian + Nf_firefighter_onground + Nf_firefighter_response # [fatality/fire] total fatality rate without sprinkler suppression\n",
    "# Injuries\n",
    "Nin_unsup_frs = Ni_civilian + Ni_firefighter_onground + Nf_firefighter_response # [injury/fire] total injury rate without sprinkler suppression\n",
    "\n",
    "### Valuation of fatalities and injuries\n",
    "Df_unsup_frs=Nf_unsup_frs*VSL # [USD/fire] valuation expected life loss\n",
    "Din_unsup_frs = Nin_unsup_frs*xi_in # [USD/fire] valuation expected injuries\n",
    "\n",
    "### Material loss\n",
    "xi_d_unsup_frs = comp_area * Replace_cost * eps_loss_content * eps_loss_indirect # [USD] property loss\n",
    "\n",
    "### Total loss valuation\n",
    "D_III=Df_unsup_frs+Din_unsup_frs+xi_d_unsup_frs # [USD] expected value fire-induced loss, scenario III \"fire not suppressed\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III. PNV calculation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total safety system cost *C+A*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III.1.a Investment cost, *C<sub>I</sub>*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "C_I_sp=c_ins # PNV investment cost for sprinkler system\n",
    "C_I_com=c_comp # PNV investment cost for compartmentation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III.1.b PNV maintenance cost, *C<sub>M</sub>*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "C_M_sp=m_sp/g # PNV of maintenace cost for sprinkler system\n",
    "C_M_com=m_com/g # PNV of maintenace cost for compartmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III.1.c Obsolescence cost, *C<sub>A</sub>*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "C_A_sp=C_I_sp*o/g # PNV of obsolescence cost sprinkler system\n",
    "C_A_com=C_I_com*o/g # PNV of obsolescence cost compartmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III.1.d Total safety system cost *C+A*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "net_C_a=C_I_sp+ C_M_sp + C_A_sp # Sprinkler only (Design a)\n",
    "net_C_b = C_I_com + C_M_com + C_A_com # Compartmenation only (Design b) - first entry is the reference design of a single compartment\n",
    "net_C_c = net_C_a + net_C_b # Sprinkler and compartmentation both (Design c)"
   ]
  },
  {
   "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* (no sprinkler system and no compartmentation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Expected loss reference design\n",
    "Dref  =  p_frs * D_II + (1-p_frs) * D_III[0]\n",
    "\n",
    "# Present net value of 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 designs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Expected losses with design A, B and C\n",
    "# Design a (Sprinkler system only)\n",
    "Da = psup_sp * D_I + (1-psup_sp) * (p_frs * D_II + (1-p_frs) * D_III[0])\n",
    "\n",
    "# Design b (Compartmenation only)\n",
    "Db  =  p_frs * D_II + (1-p_frs) * D_III[1:]\n",
    "\n",
    "# Design c (Fire sprinkler and Compartmenation both)\n",
    "Dc  =  psup_sp * D_I + (1-psup_sp) * (p_frs * D_II + (1-p_frs) * D_III[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "## PNV of design a, b and c\n",
    "D_a = Da *lambda_fi/g  # [USD] PNV expected risk for alternative design a (sprinkler system only)\n",
    "D_b = Db *lambda_fi/g  # [USD] PNV expected risk for alternative design b (compartmentation only)\n",
    "D_c = Dc *lambda_fi/g  # [USD] PNV expected risk for alternative design c (sprinkler system and compartmentation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III.2.c PNV net benefit, *B-D*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "netB_a = B - D_a # [USD] Net benefit for alternative design a (sprinkler system only)\n",
    "netB_b = B - D_b # [USD] Net benefit for alternative design b (compartmentation only)\n",
    "netB_c = B - D_c # [USD] Net benefit for alternative design c (sprinkler system and compartmentation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IV. Cost-Benefit evaluation, Z (USD, Present net value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The PNV for alternative design a (sprinkler system only) is 55,463 [USD]; the BCR is 1.06\n",
      "\n",
      "The PNV for alternative design b (compartmentation only) is, with\n",
      "\t2 compartments: 487,035 [USD]; the BCR is 8.73\n",
      "\t3 compartments: 607,380 [USD]; the BCR is 5.82\n",
      "\t4 compartments: 657,052 [USD]; the BCR is 4.91\n",
      "\t6 compartments: 685,725 [USD]; the BCR is 3.97\n",
      "\t8 compartments: 668,561 [USD]; the BCR is 3.27\n",
      "\n",
      "The PNV for alternative design c (sprinkler system and compartmentation) is, with\n",
      "\t2 compartments: 19,964 [USD]; the BCR is 1.02\n",
      "\t3 compartments: -33,868 [USD]; the BCR is 0.97\n",
      "\t4 compartments: -71,285 [USD]; the BCR is 0.94\n",
      "\t6 compartments: -129,701 [USD]; the BCR is 0.89\n",
      "\t8 compartments: -190,409 [USD]; the BCR is 0.85\n"
     ]
    }
   ],
   "source": [
    "## PNV of proposed alternative design\n",
    "Z_a = netB_a - net_C_a # [USD] Present net value for alternative design a (sprinkler system only)\n",
    "Z_b = netB_b - net_C_b # [USD] Present net value for alternative design b (compartmentation only)\n",
    "Z_c = netB_c - net_C_c # [USD] Present net value for alternative design c (sprinkler system and compartmentation)\n",
    "\n",
    "## Benefit cost ratio of proposed alternative design\n",
    "BCR_a=netB_a/net_C_a \n",
    "BCR_b=netB_b/net_C_b\n",
    "BCR_c=netB_c/net_C_c\n",
    "\n",
    "## Print to screen\n",
    "print(\"\\nThe PNV for alternative design a (sprinkler system only) is {0:,.0f} [USD]; the BCR is {1:.2f}\".format(Z_a,BCR_a))\n",
    "print(\"\\nThe PNV for alternative design b (compartmentation only) is, with\")\n",
    "for i,Z in enumerate(Z_b):\n",
    "    print(\"\\t{0:,.0f} compartments: {1:,.0f} [USD]; the BCR is {2:.2f}\".format(n_comp[i+1],Z,BCR_b[i]))\n",
    "print(\"\\nThe PNV for alternative design c (sprinkler system and compartmentation) is, with\")\n",
    "for i,Z in enumerate(Z_c):\n",
    "    print(\"\\t{0:,.0f} compartments: {1:,.0f} [USD]; the BCR is {2:.2f}\".format(n_comp[i+1],Z,BCR_c[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot for cost-benefit of sprinkler and the cost-benefit of compartmentation for failure of sprinkler and fire rescue service\n",
    "plt.rcParams.update({'font.size': 15})\n",
    "fig,ax  = plt.subplots(1,1)\n",
    "\n",
    "ax.plot([n_comp[0],n_comp[-1]],[Z_a/1000,Z_a/1000],'--',color='r')\n",
    "ax.plot(n_comp[1:],Z_b/1000,linestyle='--',marker='x')\n",
    "ax.plot(n_comp[1:],Z_c/1000,linestyle='--',marker='o')\n",
    "\n",
    "ax.plot([2,8],[0,0],'k')\n",
    "ax.set_xlim([2,8])\n",
    "ax.set_xlabel('Number of compartments')\n",
    "ax.set_ylabel('Present net value, Z [$10^3$ USD]')\n",
    "ax.legend(['Sprinkler','Compartmentation','Sprinkler + Compartmentation'])\n",
    "fig.set_size_inches(12,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example 2 - Parameter study"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter study with VSL, indirect cost, 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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function combining above model assumption for parameter study\n",
    "def Zvalue(VSL,psup_sp,eps_loss_indirect,p_frs):\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 I \"suppression by sprinkler\"\n",
    "    ### Valuation of fatalities and injuries\n",
    "    Df_sup=Nf_sup*VSL # [USD/fire] valuation expected life loss\n",
    "    Din_sup = Nin_sup*xi_in # [USD/fire] valuation expected injuries\n",
    "    ### Material loss\n",
    "    xi_d_sup =  S_loss_s * Replace_cost * eps_loss_content * eps_loss_indirect # [USD] property loss given fire\n",
    "    ### Total loss valuation\n",
    "    D_I=Df_sup+Din_sup+xi_d_sup # [USD] expected value fire-induced loss, scenario I \"fire suppressed by sprinkler\"\n",
    "    \n",
    "    ## II.2.b Scenario II \"Not suppressed by sprinklers, suppressed by fire and rescue service\"\n",
    "    ### Valuation of fatalities and injuries\n",
    "    Df_sup_frs=Nf_sup_frs*VSL # [USD/fire] valuation expected life loss\n",
    "    Din_sup_frs = Nin_sup_frs*xi_in # [USD/fire] valuation expected injuries\n",
    "    ### Material loss\n",
    "    xi_d_sup_frs =  S_loss_0 * Replace_cost * eps_loss_content * eps_loss_indirect # [USD] property loss\n",
    "    ### Total loss valuation\n",
    "    D_II=Df_sup_frs+Din_sup_frs+xi_d_sup_frs # [USD] expected value fire-induced loss, scenario II \"fire not suppressed by sprinklers but suppressed by fire and rescue service\"\n",
    "    \n",
    "    ## II.2.c Scenario III \"Not suppressed\"\n",
    "    ### Valuation of fatalities and injuries\n",
    "    Df_unsup_frs=Nf_unsup_frs*VSL # [USD/fire] valuation expected life loss\n",
    "    Din_unsup_frs = Nin_unsup_frs*xi_in # [USD/fire] valuation expected injuries\n",
    "    ### Material loss\n",
    "    xi_d_unsup_frs = comp_area * Replace_cost * eps_loss_content * eps_loss_indirect # [USD] property loss\n",
    "    ### Total loss valuation\n",
    "    D_III=Df_unsup_frs+Din_unsup_frs+xi_d_unsup_frs # [USD] expected value fire-induced loss, scenario III \"fire not suppressed\"\n",
    "    \n",
    "    ## III. PNV calculation ##\n",
    "    ##########################\n",
    "    ## III.2.a Fire-induced losses reference design, B\n",
    "    # Expected loss reference design\n",
    "    Dref  =  p_frs * D_II + (1-p_frs) * D_III[0]\n",
    "    # Present net value of reference design\n",
    "    B = Dref * lambda_fi/g # [USD] PNV fire-induced losses reference design\n",
    "    \n",
    "    ## III.2.b Fire-induced losses trial designs\n",
    "    ## Expected losses with design A, B and C\n",
    "    Da = psup_sp * D_I + (1-psup_sp) * (p_frs * D_II + (1-p_frs) * D_III[0]) # Design a (Sprinkler system only)\n",
    "    Db  =  p_frs * D_II + (1-p_frs) * D_III[1:] # Design b (Compartmenation only)\n",
    "    Dc  =  psup_sp * D_I + (1-psup_sp) * (p_frs * D_II + (1-p_frs) * D_III[1:]) # Design c (Fire sprinkler and Compartmenation both)\n",
    "    ## PNV evaluation\n",
    "    D_a = Da *lambda_fi/g; D_b = Db *lambda_fi/g; D_c = Dc *lambda_fi/g\n",
    "    \n",
    "    ## III.2.c PNV net benefit, B-D\n",
    "    netB_a = B - D_a; netB_b = B - D_b; netB_c = B - D_c\n",
    "    \n",
    "    ## IV. Cost-Benefit evaluation ##\n",
    "    #################################  \n",
    "    ## PNV of proposed alternative design\n",
    "    Z_a = netB_a - net_C_a # [USD] Present net value for alternative design a (sprinkler system only)\n",
    "    Z_b = netB_b - net_C_b # [USD] Present net value for alternative design b (compartmentation only)\n",
    "    Z_c = netB_c - net_C_c # [USD] Present net value for alternative design c (sprinkler system and compartmentation)\n",
    "    ## Benefit cost ratio of proposed alternative design\n",
    "    BCR_a=netB_a/net_C_a \n",
    "    BCR_b=netB_b/net_C_b\n",
    "    BCR_c=netB_c/net_C_c\n",
    "    \n",
    "    return Z_a,Z_b,Z_c,BCR_a,BCR_b,BCR_c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Example of usage function Zvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The PNV for alternative design a (sprinkler system only) is 55,463 [USD]; the BCR is 1.06\n",
      "\n",
      "The PNV for alternative design b (compartmentation only) is, with\n",
      "\t2 compartments: 487,035 [USD]; the BCR is 8.73\n",
      "\t3 compartments: 607,380 [USD]; the BCR is 5.82\n",
      "\t4 compartments: 657,052 [USD]; the BCR is 4.91\n",
      "\t6 compartments: 685,725 [USD]; the BCR is 3.97\n",
      "\t8 compartments: 668,561 [USD]; the BCR is 3.27\n",
      "\n",
      "The PNV for alternative design c (sprinkler system and compartmentation) is, with\n",
      "\t2 compartments: 19,964 [USD]; the BCR is 1.02\n",
      "\t3 compartments: -33,868 [USD]; the BCR is 0.97\n",
      "\t4 compartments: -71,285 [USD]; the BCR is 0.94\n",
      "\t6 compartments: -129,701 [USD]; the BCR is 0.89\n",
      "\t8 compartments: -190,409 [USD]; the BCR is 0.85\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",
    "p_frs = 0.1 # [-] probability of succesful fire suppression by fire rescue service; 0.95 based on (Vassart et al., 2012); 0.1 adopted for remote location without possibility of early FRS intervention\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 = 1.65 # [-] total property loss multiplication factor accounting for indirect costs; 1.65 based on (Ramachandran and Hall, 2002)\n",
    "\n",
    "# CBA \n",
    "Z_a,Z_b,Z_c,BCR_a,BCR_b,BCR_c=Zvalue(VSL,psup_sp,eps_loss_indirect,p_frs) # [USD] PNV of proposed safety measure\n",
    "\n",
    "## Print to screen\n",
    "print(\"\\nThe PNV for alternative design a (sprinkler system only) is {0:,.0f} [USD]; the BCR is {1:.2f}\".format(Z_a,BCR_a))\n",
    "print(\"\\nThe PNV for alternative design b (compartmentation only) is, with\")\n",
    "for i,Z in enumerate(Z_b):\n",
    "    print(\"\\t{0:,.0f} compartments: {1:,.0f} [USD]; the BCR is {2:.2f}\".format(n_comp[i+1],Z,BCR_b[i]))\n",
    "print(\"\\nThe PNV for alternative design c (sprinkler system and compartmentation) is, with\")\n",
    "for i,Z in enumerate(Z_c):\n",
    "    print(\"\\t{0:,.0f} compartments: {1:,.0f} [USD]; the BCR is {2:.2f}\".format(n_comp[i+1],Z,BCR_c[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Parameter study and visualization for design alternative a (sprinklers only) - p_frs modified\n",
    "Indicates how VSL has no real impact on PNV; and the effect of p_sup is very small.  \n",
    "Sprinkler installation is deemed efficient (PNV>0) as the indirect cost factor approaches 3000% (i.e. as the cost factor approaches 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Present net value, Z [$10^3$ USD]')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# VSL, Indirect cost, sprinkler efficiency\n",
    "# %matplotlib qt \n",
    "%matplotlib inline\n",
    "\n",
    "p_frs = 0.95 # [-] probability of succesful fire suppression by fire rescue service; 0.95 based on (Vassart et al., 2012); 0.1 adopted for remote location without possibility of early FRS intervention\n",
    "VSL_var= np.array([2.8, 5.657, 11.6]) *1e+6 # 11.6 USD (Department of Transportation, 2011)\n",
    "psup_var = np.array([0.8,0.95,1.0])\n",
    "Ind_cost_var = np.array([0,2,5,10,20,50,100,200,300,400,500,1000,2000,3000,4000,5000,10000])\n",
    "\n",
    "nCases = len(VSL_var)*len(psup_var)\n",
    "\n",
    "m_psup,m_VSL = np.meshgrid(psup_var,VSL_var)\n",
    "m_VSL = m_VSL.ravel();m_psup = m_psup.ravel()\n",
    "\n",
    "Z_a = np.zeros((len(Ind_cost_var),len(m_VSL)))\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",
    "\n",
    "fig,ax = plt.subplots(1,1)\n",
    "\n",
    "for num in np.arange(len(m_VSL)):\n",
    "    for j in np.arange(len(Ind_cost_var)):\n",
    "        Z_a[j,num]=Zvalue(m_VSL[num],m_psup[num],1+Ind_cost_var[j]/100,p_frs)[0]\n",
    "    \n",
    "    ax.plot(Ind_cost_var/100,Z_a[:,num]/1000,color=color[num],linestyle=linestyle[num])\n",
    "    legends.append('VSL = '+str(m_VSL[num]/1e6)+', $P_{sup}$ = '+str(m_psup[num]))\n",
    "     \n",
    "\n",
    "ax.legend(legends,fontsize=12)\n",
    "ax.plot([0,100],[0,0],linestyle='solid',color='k',linewidth=3.0)\n",
    "ax.set_xlim([0.01,100])\n",
    "ax.set_xscale('log')\n",
    "ax.grid(which='both',axis='both')\n",
    "fig.set_size_inches(12,8)\n",
    "ax.set_xlabel('Indirect cost as factor of direct material cost');ax.set_ylabel('Present net value, Z [$10^3$ USD]')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Design with Compartmentation, design alternative b (compartmentation only), p_frs modified\n",
    "Note that the highest BCR is not necessarily obtained for the optimum design solution.  \n",
    "See e.g., the optimum for an indirect cost factor of 20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The PNV for alternative design b (compartmentation only) is, with\n",
      "\t2 compartments: 325,914 [USD]; the BCR is 6.17\n",
      "\t3 compartments: 392,551 [USD]; the BCR is 4.12\n",
      "\t4 compartments: 415,370 [USD]; the BCR is 3.47\n",
      "\t6 compartments: 417,189 [USD]; the BCR is 2.81\n",
      "\t8 compartments: 386,599 [USD]; the BCR is 2.31\n",
      "\n",
      "The PNV approach indicates 6 compartments as the optimum for an indirect cost factor of 20 (i.e. 2,000%).\n",
      "The BCR is however the largest for 2 compartments because the investment is smaller.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# VSL, Indirect cost, compartmentation efficiency\n",
    "# %matplotlib qt \n",
    "%matplotlib inline\n",
    "\n",
    "Ind_cost_var = np.array([100,200,300,400,500,600,700,800,900,1000,2000,3000,4000,5000,10000]) # [%] indirect cost factor as percentage of total direct cost\n",
    "p_frs = 0.95 # [-] probability of succesful fire suppression by fire rescue service; 0.95 based on (Vassart et al., 2012); 0.1 adopted for remote location without possibility of early FRS intervention\n",
    "\n",
    "Z_b = np.zeros((len(Ind_cost_var),len(n_comp[1:])))\n",
    "BCR_b = np.zeros((len(Ind_cost_var),len(n_comp[1:])))\n",
    "legends = []\n",
    "\n",
    "for j in np.arange(len(Ind_cost_var)):\n",
    "    out=Zvalue(VSL,psup_sp,1+Ind_cost_var[j]/100,p_frs)\n",
    "    Z_b[j,:]=out[1]; BCR_b[j,:]=out[4]\n",
    "\n",
    "fig,ax = plt.subplots(1,1)\n",
    "for i,comp in enumerate(n_comp[1:]):\n",
    "    ax.plot(Ind_cost_var/100,Z_b[:,i]/10**3)\n",
    "    legends.append(str(comp)+' compartments')\n",
    "     \n",
    "\n",
    "ax.legend(legends,fontsize=12)\n",
    "# ax.plot([0,100],[0,0],linestyle='solid',color='k',linewidth=3.0)\n",
    "ax.set_xlim([1,100])\n",
    "ax.set_ylim([-500,500])\n",
    "ax.set_xscale('log')\n",
    "ax.grid(which='both',axis='both')\n",
    "fig.set_size_inches(9,6)\n",
    "ax.set_xlabel('Indirect cost as factor of direct cost');ax.set_ylabel('Present net value, Z [$10^6$ USD]')\n",
    "\n",
    "## Print to screen\n",
    "ind_cost=2000 # [%] indirect cost factor as percentage of total direct cost\n",
    "index=np.where(Ind_cost_var==ind_cost)[0][0]\n",
    "print(\"\\nThe PNV for alternative design b (compartmentation only) is, with\")\n",
    "for i,Z in enumerate(Z_b[index,:]):\n",
    "    print(\"\\t{0:,.0f} compartments: {1:,.0f} [USD]; the BCR is {2:.2f}\".format(n_comp[i+1],Z,BCR_b[index,i]))\n",
    "print(\"\\nThe PNV approach indicates 6 compartments as the optimum for an indirect cost factor of 20 (i.e. 2,000%).\\nThe BCR is however the largest for 2 compartments because the investment is smaller.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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